From 3d34a3fd6137caa0fe4d25e63c29e75b367baf22 Mon Sep 17 00:00:00 2001
From: Sara Faghih-Naini <sara.faghihnaini@ecmwf.int>
Date: Mon, 9 Dec 2024 10:43:49 +0100
Subject: [PATCH 1/8] First version of ICON dataset notebooks and update other
 notebooks

---
 04-example-datasets/01-hplp.ipynb     | 3468 ++++++++++--
 04-example-datasets/02-OpenIFS.ipynb  | 6654 +++++++++++++++++++++-
 04-example-datasets/03-NextGEMS.ipynb | 1307 +++++
 04-example-datasets/04-ICONXPP.ipynb  | 7465 +++++++++++++++++++++++++
 4 files changed, 18214 insertions(+), 680 deletions(-)
 create mode 100644 04-example-datasets/03-NextGEMS.ipynb
 create mode 100644 04-example-datasets/04-ICONXPP.ipynb

diff --git a/04-example-datasets/01-hplp.ipynb b/04-example-datasets/01-hplp.ipynb
index 9a5e104..982127c 100644
--- a/04-example-datasets/01-hplp.ipynb
+++ b/04-example-datasets/01-hplp.ipynb
@@ -1,476 +1,3072 @@
 {
-  "metadata": {
-    "kernelspec": {
-      "name": "python",
-      "display_name": "Python (Pyodide)",
-      "language": "python"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "python",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
+   "metadata": {},
+   "source": [
+    "# IFS hplp experiment data"
+   ]
   },
-  "nbformat_minor": 5,
-  "nbformat": 4,
-  "cells": [
-    {
-      "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
-      "cell_type": "markdown",
-      "source": "# IFS hplp experiment data",
-      "metadata": {}
-    },
-    {
-      "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
-      "cell_type": "markdown",
-      "source": "> [!NOTE]\n> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n\n> [!CAUTION]\n> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep.",
-      "metadata": {}
-    },
-    {
-      "id": "463bf2c0-ac3f-4786-9687-362393802dce",
-      "cell_type": "markdown",
-      "source": "This notebook is a comprehensive guide to accessing GRIB files for the [hplp experiment](https://apps.ecmwf.int/ifs-experiments/rd/hplp/). It includes details about the available parameters, levels, and steps, along with instructions for using the `load_hplp_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/). The GRIB files are not downloaded locally; instead, they are accessed remotely using `gribscan`, `fsspec`, and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `gribscan`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point single-time atmospheric and wave model (long window 4Dvar) outputs from the IFS.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco399), corresponding to a horizontal resolution of approximately 28 km, and with 137 vertical model levels. The 10-day weather forecast is provided in 12-hour intervals, beginning from 2020-07-21 at 00:00:00 UTC.\n\nThe GRIB files are organized into different types of data fields based on the levels at which they are stored.\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in hPa):\n```python\n[1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000]\n```\n\nThe corresponding IDs and more information are given in the table below:",
-      "metadata": {}
-    },
-    {
-      "id": "1c1d220c-d8b8-4e7c-90f4-0625b4cbc15b",
-      "cell_type": "markdown",
-      "source": "|   paramId | gridName   | gridType   | typeOfLevel   | shortName   | packingType      |\n|----------:|:-----------|:-----------|:--------------|:------------|:-----------------|\n|       129 | not_found  | sh         | isobaricInhPa | z           | spectral_complex |\n|       130 | not_found  | sh         | isobaricInhPa | t           | spectral_complex |\n|       133 | O400       | reduced_gg | isobaricInhPa | q           | spectral_complex |\n|       135 | not_found  | sh         | isobaricInhPa | w           | spectral_complex |\n|       138 | not_found  | sh         | isobaricInhPa | vo          | spectral_complex |\n|       155 | not_found  | sh         | isobaricInhPa | d           | spectral_complex |\n|       131 | not_found  | sh         | isobaricInhPa | u           | spectral_complex |\n|       132 | not_found  | sh         | isobaricInhPa | v           | spectral_complex |\n|       157 | not_found  | sh         | isobaricInhPa | r           | spectral_complex |",
-      "metadata": {}
-    },
-    {
-      "id": "789d34ac-2fdb-4d92-8c0c-c62b363a57a0",
-      "cell_type": "markdown",
-      "source": "### Model Level Parameters\n\nThe following parameters are available as model level fields:\n```python\n[\"crwc\", \"cswc\", \"z\", \"t\", \"q\", \"w\", \"vo\", \"lnsp\", \"d\", \"u\", \"v\", \"o3\", \"clwc\", \"ciwc\", \"cc\"]\n```\nThese parameters are stored at the following levels:\n```python\n[list(range(1, 138))] # Levels 1 to 137\n```\nFor more information about the model levels please see [here](https://confluence.ecmwf.int/display/UDOC/Model+level+definitions) and [here](https://confluence.ecmwf.int/display/UDOC/L137+model+level+definitions).\n\nThe corresponding IDs and more information are given in the table below:",
-      "metadata": {}
-    },
-    {
-      "id": "d96a88c4-7954-42b5-989b-c433d4c87f4e",
-      "cell_type": "markdown",
-      "source": "|   paramId | gridName   | gridType   | typeOfLevel   | shortName   | packingType   |\n|----------:|:-----------|:-----------|:--------------|:------------|:--------------|\n|        75 | O400       | reduced_gg | hybrid        | crwc        | grid_ieee     |\n|        76 | O400       | reduced_gg | hybrid        | cswc        | grid_ieee     |\n|       129 | not_found  | sh         | hybrid        | z           | grid_ieee     |\n|       130 | not_found  | sh         | hybrid        | t           | grid_ieee     |\n|       133 | O400       | reduced_gg | hybrid        | q           | grid_ieee     |\n|       135 | not_found  | sh         | hybrid        | w           | grid_ieee     |\n|       138 | not_found  | sh         | hybrid        | vo          | grid_ieee     |\n|       152 | not_found  | sh         | hybrid        | lnsp        | grid_ieee     |\n|       155 | not_found  | sh         | hybrid        | d           | spectral_ieee |\n|       131 | not_found  | sh         | hybrid        | u           | spectral_ieee |\n|       132 | not_found  | sh         | sh            | v           | spectral_ieee |\n|       203 | O400       | reduced_gg | sh            | o3          | spectral_ieee |\n|       246 | O400       | reduced_gg | sh            | clwc        | spectral_ieee |\n|       247 | O400       | reduced_gg | sh            | ciwc        | spectral_ieee |\n|       248 | O400       | reduced_gg | sh            | cc          | spectral_ieee |",
-      "metadata": {}
-    },
-    {
-      "id": "8539c000-1d96-439e-bc4d-2076c6f3f442",
-      "cell_type": "markdown",
-      "source": "### Surface Level Parameters\n\nThe following parameters are available as surface level fields:\n```python\n['cin', 'sro', 'ssro', 'vis', 'i10fg', 'ci', 'asn', 'rsn', 'sst', 'istl1', 'istl2', 'istl3', 'istl4', 'swvl1', 'swvl2', 'swvl3', 'swvl4', 'es', 'smlt', 'dsrp', '10fg', 'lspf', 'uvb', 'par', 'cape', 'tclw', 'tciw', 'tcrw', 'tcsw', 'z', 'ocu', 'ocv', 'tcw', 'tcwv', 'stl1', 'sd', 'lsp', 'cp', 'sf', 'bld', 'sshf', 'slhf', 'chnk', 'msl', 'blh', 'tcc', '10u', '10v', '2t', '2d', 'ssrd', 'stl2', 'lsm', 'strd', 'ssr', 'str', 'tsr', 'ttr', 'ewss', 'nsss', 'e', 'stl3', 'lcc', 'mcc', 'hcc', 'sund', 'lgws', 'mgws', 'gwd', 'src', 'mx2t', 'mn2t', 'ro', 'tco3', 'tsrc', 'ttrc', 'ssrc', 'strc',  'tisr', 'vimd', 'fzra', 'ilspf', 'crr', 'lsrr', 'csfr', 'lssfr', 'tp', 'iews', 'inss', 'ishf', 'ie', 'skt', 'stl4', 'tsn', 'fal', 'fsr', 'flsr', 'pev', 'ptype', 'kx', 'totalx']\n```\n\nThe corresponding IDs and more information are given in the (hidden) table below:",
-      "metadata": {}
-    },
-    {
-      "id": "557c2ea2-1790-4af2-921d-46110b5502d3",
-      "cell_type": "markdown",
-      "source": "|   paramId | gridName   | gridType   | typeOfLevel         | shortName   | packingType   |\n|----------:|:-----------|:-----------|:--------------------|:------------|:--------------|\n|    228001 | O400       | reduced_gg | surface             | cin         | grid_ieee     |\n|         8 | O400       | reduced_gg | surface             | sro         | grid_ieee     |\n|         9 | O400       | reduced_gg | surface             | ssro        | grid_ieee     |\n|      3020 | O400       | reduced_gg | surface             | vis         | grid_ieee     |\n|    228029 | O400       | reduced_gg | surface             | i10fg       | grid_ieee     |\n|        31 | O400       | reduced_gg | surface             | ci          | grid_ieee     |\n|        32 | O400       | reduced_gg | surface             | asn         | grid_ieee     |\n|        33 | O400       | reduced_gg | surface             | rsn         | grid_ieee     |\n|        34 | O400       | reduced_gg | surface             | sst         | grid_ieee     |\n|        35 | O400       | reduced_gg | surface             | istl1       | grid_ieee     |\n|        36 | O400       | reduced_gg | surface             | istl2       | grid_ieee     |\n|        37 | O400       | reduced_gg | surface             | istl3       | grid_ieee     |\n|        38 | O400       | reduced_gg | surface             | istl4       | grid_ieee     |\n|        39 | O400       | reduced_gg | surface             | swvl1       | grid_ieee     |\n|        40 | O400       | reduced_gg | surface             | swvl2       | grid_ieee     |\n|        41 | O400       | reduced_gg | surface             | swvl3       | grid_ieee     |\n|        42 | O400       | reduced_gg | surface             | swvl4       | grid_ieee     |\n|        44 | O400       | reduced_gg | surface             | es          | grid_ieee     |\n|        45 | O400       | reduced_gg | surface             | smlt        | grid_ieee     |\n|        47 | O400       | reduced_gg | surface             | dsrp        | grid_ieee     |\n|        49 | O400       | reduced_gg | surface             | 10fg        | grid_ieee     |\n|        50 | O400       | reduced_gg | surface             | lspf        | grid_ieee     |\n|        57 | O400       | reduced_gg | surface             | uvb         | grid_ieee     |\n|        58 | O400       | reduced_gg | surface             | par         | grid_ieee     |\n|        59 | O400       | reduced_gg | surface             | cape        | grid_ieee     |\n|        78 | O400       | reduced_gg | surface             | tclw        | grid_ieee     |\n|        79 | O400       | reduced_gg | surface             | tciw        | grid_ieee     |\n|    228089 | O400       | reduced_gg | surface             | tcrw        | grid_ieee     |\n|    228090 | O400       | reduced_gg | surface             | tcsw        | grid_ieee     |\n|       129 | O400       | reduced_gg | surface             | z           | grid_ieee     |\n|    151131 | O400       | reduced_gg | surface             | ocu         | grid_ieee     |\n|    151132 | O400       | reduced_gg | surface             | ocv         | grid_ieee     |\n|       136 | O400       | reduced_gg | surface             | tcw         | grid_ieee     |\n|       137 | O400       | reduced_gg | surface             | tcwv        | grid_ieee     |\n|       139 | O400       | reduced_gg | surface             | stl1        | grid_ieee     |\n|       141 | O400       | reduced_gg | surface             | sd          | grid_ieee     |\n|       142 | O400       | reduced_gg | surface             | lsp         | grid_ieee     |\n|       143 | O400       | reduced_gg | surface             | cp          | grid_ieee     |\n|       144 | O400       | reduced_gg | surface             | sf          | grid_ieee     |\n|       145 | O400       | reduced_gg | surface             | bld         | grid_ieee     |\n|       146 | O400       | reduced_gg | surface             | sshf        | grid_ieee     |\n|       147 | O400       | reduced_gg | surface             | slhf        | grid_ieee     |\n|       148 | O400       | reduced_gg | surface             | chnk        | grid_ieee     |\n|       151 | O400       | reduced_gg | surface             | msl         | grid_ieee     |\n|       159 | O400       | reduced_gg | surface             | blh         | grid_ieee     |\n|       164 | O400       | reduced_gg | surface             | tcc         | grid_ieee     |\n|       165 | O400       | reduced_gg | surface             | 10u         | grid_ieee     |\n|       166 | O400       | reduced_gg | surface             | 10v         | grid_ieee     |\n|       167 | O400       | reduced_gg | surface             | 2t          | grid_ieee     |\n|       168 | O400       | reduced_gg | surface             | 2d          | grid_ieee     |\n|       169 | O400       | reduced_gg | surface             | ssrd        | grid_ieee     |\n|       170 | O400       | reduced_gg | surface             | stl2        | grid_ieee     |\n|       172 | O400       | reduced_gg | surface             | lsm         | grid_ieee     |\n|       175 | O400       | reduced_gg | surface             | strd        | grid_ieee     |\n|       176 | O400       | reduced_gg | surface             | ssr         | grid_ieee     |\n|       177 | O400       | reduced_gg | surface             | str         | grid_ieee     |\n|       178 | O400       | reduced_gg | surface             | tsr         | grid_ieee     |\n|       179 | O400       | reduced_gg | surface             | ttr         | grid_ieee     |\n|       180 | O400       | reduced_gg | surface             | ewss        | grid_ieee     |\n|       181 | O400       | reduced_gg | surface             | nsss        | grid_ieee     |\n|       182 | O400       | reduced_gg | surface             | e           | grid_ieee     |\n|       183 | O400       | reduced_gg | surface             | stl3        | grid_ieee     |\n|       186 | O400       | reduced_gg | surface             | lcc         | grid_ieee     |\n|       187 | O400       | reduced_gg | surface             | mcc         | grid_ieee     |\n|       188 | O400       | reduced_gg | surface             | hcc         | grid_ieee     |\n|       189 | O400       | reduced_gg | surface             | sund        | grid_ieee     |\n|       195 | O400       | reduced_gg | surface             | lgws        | grid_ieee     |\n|       196 | O400       | reduced_gg | surface             | mgws        | grid_ieee     |\n|       197 | O400       | reduced_gg | surface             | gwd         | grid_ieee     |\n|       198 | O400       | reduced_gg | surface             | src         | grid_ieee     |\n|       201 | O400       | reduced_gg | surface             | mx2t        | grid_ieee     |\n|       202 | O400       | reduced_gg | surface             | mn2t        | grid_ieee     |\n|       205 | O400       | reduced_gg | surface             | ro          | grid_ieee     |\n|       206 | O400       | reduced_gg | surface             | tco3        | grid_ieee     |\n|       208 | O400       | reduced_gg | surface             | tsrc        | grid_ieee     |\n|       209 | O400       | reduced_gg | surface             | ttrc        | grid_ieee     |\n|       210 | O400       | reduced_gg | surface             | ssrc        | grid_ieee     |\n|       211 | O400       | reduced_gg | surface             | strc        | grid_ieee     |\n|       212 | O400       | reduced_gg | surface             | tisr        | grid_ieee     |\n|       213 | O400       | reduced_gg | surface             | vimd        | grid_ieee     |\n|    228216 | O400       | reduced_gg | surface             | fzra        | grid_ieee     |\n|    228217 | O400       | reduced_gg | surface             | ilspf       | grid_ieee     |\n|    228218 | O400       | reduced_gg | surface             | crr         | grid_ieee     |\n|    228219 | O400       | reduced_gg | surface             | lsrr        | grid_ieee     |\n|    228220 | O400       | reduced_gg | depthBelowLandLayer | csfr        | grid_ieee     |\n|    228221 | O400       | reduced_gg | depthBelowLandLayer | lssfr       | grid_ieee     |\n|       228 | O400       | reduced_gg | depthBelowLandLayer | tp          | grid_ieee     |\n|       229 | O400       | reduced_gg | depthBelowLandLayer | iews        | grid_ieee     |\n|       230 | O400       | reduced_gg | depthBelowLandLayer | inss        | grid_ieee     |\n|       231 | O400       | reduced_gg | depthBelowLandLayer | ishf        | grid_ieee     |\n|       232 | O400       | reduced_gg | depthBelowLandLayer | ie          | grid_ieee     |\n|       235 | O400       | reduced_gg | depthBelowLandLayer | skt         | grid_ieee     |\n|       236 | O400       | reduced_gg | depthBelowLandLayer | stl4        | grid_ieee     |\n|       238 | O400       | reduced_gg | depthBelowLandLayer | tsn         | grid_ieee     |\n|       243 | O400       | reduced_gg | depthBelowLandLayer | fal         | grid_ieee     |\n|       244 | O400       | reduced_gg | depthBelowLandLayer | fsr         | grid_ieee     |\n|       245 | O400       | reduced_gg | depthBelowLandLayer | flsr        | grid_ieee     |\n|    228251 | O400       | reduced_gg | depthBelowLandLayer | pev         | grid_ieee     |\n|    260015 | O400       | reduced_gg | depthBelowLandLayer | ptype       | grid_ieee     |\n|    260121 | O400       | reduced_gg | depthBelowLandLayer | kx          | grid_ieee     |\n|    260123 | O400       | reduced_gg | depthBelowLandLayer | totalx      | grid_ieee     |",
-      "metadata": {}
-    },
-    {
-      "id": "f18ec120-3193-42d4-bcda-5a8a80bc574c",
-      "cell_type": "markdown",
-      "source": "### Wave Model Parameters\n\nThe following parameters are available for the wave model data:\n```python\n['wstar', 'rhoao', 'phioc', 'tauoc', 'ust', 'vst', 'hmax', 'wmb', 'mp1', 'mp2', 'wdw', 'swh', 'mwd', 'pp1d', 'mwp', 'cdww', 'wind','dwi']\n```\n\nThe corresponding IDs and more information are given in the table below:",
-      "metadata": {}
-    },
-    {
-      "id": "968e5b37-1165-4396-b0eb-8b9e03dd7d2b",
-      "cell_type": "markdown",
-      "source": "|   paramId | gridName   | gridType   | typeOfLevel       | shortName   | packingType   |\n|----------:|:-----------|:-----------|:------------------|:------------|:--------------|\n|    140208 | not_found  | reduced_ll | meanSea           | wstar       | grid_simple   |\n|    140209 | not_found  | reduced_ll | meanSea           | rhoao       | grid_simple   |\n|    140212 | not_found  | reduced_ll | meanSea           | phioc       | grid_simple   |\n|    140214 | not_found  | reduced_ll | meanSea           | tauoc       | grid_simple   |\n|    140215 | not_found  | reduced_ll | meanSea           | ust         | grid_simple   |\n|    140216 | not_found  | reduced_ll | meanSea           | vst         | grid_simple   |\n|    140218 | not_found  | reduced_ll | meanSea           | hmax        | grid_simple   |\n|    140219 | not_found  | reduced_ll | meanSea           | wmb         | grid_simple   |\n|    140220 | not_found  | reduced_ll | meanSea           | mp1         | grid_simple   |\n|    140221 | not_found  | reduced_ll | meanSea           | mp2         | grid_simple   |\n|    140222 | not_found  | reduced_ll | meanSea           | wdw         | grid_simple   |\n|    140229 | not_found  | reduced_ll | meanSea           | swh         | grid_simple   |\n|    140230 | not_found  | reduced_ll | meanSea           | mwd         | grid_simple   |\n|    140231 | not_found  | reduced_ll | meanSea           | pp1d        | grid_simple   |\n|    140232 | not_found  | reduced_ll | meanSea           | mwp         | grid_simple   |\n|    140233 | not_found  | reduced_ll | heightAboveGround | cdww        | grid_simple   |\n|    140245 | not_found  | reduced_ll | heightAboveGround | wind        | grid_simple   |\n|    140249 | not_found  | reduced_ll | heightAboveGround | dwi         | grid_simple   |",
-      "metadata": {}
-    },
-    {
-      "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
-      "cell_type": "markdown",
-      "source": "## Available Steps \n\nThe following timesteps are available: \n\n```python \n[0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144, 156, 168, 180, 192, 204, 216, 228, 240]\n ```\nTimestep 0 is 2020-07-21T 00:00:00 UTC, increasing in 12-hour steps to 240  which is 2020-07-31T 00:00:00 UTC.\n\n## Using the `load_hplp_data` Function\n\nTo load the GRIB files, you can use the following function:\n\n```python\nload_hplp_data(leveltype=None, gridtype=None, step=None, remap=False)\n```\n\n### Parameters\n\n- **`leveltype`**: Defines the type of data to load. Choose one of the following:\n  - `\"pl\"`: Pressure level fields.\n  - `\"ml\"`: Model level fields.\n  - `\"sfc\"`: Surface level fields.\n  - `\"wave\"`: Wave model fields.\n- **`gridtype`**: Specifies the grid type for the data. Options include:\n  - `\"reduced_gg\"`: Reduced Gaussian grid (available for `pl`, `ml`, and `sfc`).\n  - `\"sh\"`: Spherical harmonic coefficients (available for `pl` and `ml`).\n  - `\"reduced_ll\"`: Reduced lat-long grid (available for `wave`).\n- **`step`**: The `ml` data on the `reduced_gg` grid is split into two files. Specify the timestep (`step`) to load the corresponding file. Valid steps are:\n  - `0-120`: First file (2020-07-21T00:00:00 to 2020-07-26T00:00:00).\n  - `132-240`: Second file (2020-07-26T12:00:00 to 2020-07-31T00:00:00).\n- **`remap`**: When using gridtype=`sh`, data cannot be directly plotted. To load remapped data on an O400 grid, set `remap=True`.",
-      "metadata": {}
-    },
-    {
-      "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
-      "cell_type": "code",
-      "source": "# URL of S3 bucket\nBASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\"",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [],
-      "execution_count": 1
-    },
-    {
-      "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
-      "cell_type": "markdown",
-      "source": "First, the package `zarr` (modern dataset format that is specifically designed for chunked access), its dependencies, and `gribscan` need to be imported for the remote access.",
-      "metadata": {}
-    },
-    {
-      "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
-      "cell_type": "code",
-      "source": "import aiohttp\nimport fsspec\nimport gribscan\nimport zarr",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Loading aiohttp, aiosignal, asciitree, async-timeout, attrs, cffi, cfgrib, click, eccodes, findlibs, frozenlist, fsspec, gribscan, msgpack, multidict, numcodecs, numpy, openblas, pandas, propcache, pycparser, python-dateutil, pytz, scipy, tzdata, xarray, yarl, zarr\n[pyodide]: Loaded aiohttp, aiosignal, asciitree, async-timeout, attrs, cffi, cfgrib, click, eccodes, findlibs, frozenlist, fsspec, gribscan, msgpack, multidict, numcodecs, numpy, openblas, pandas, propcache, pycparser, python-dateutil, pytz, scipy, tzdata, xarray, yarl, zarr\n[pyodide]: Memory usage has grown to 202.8MiB (from 49.9MiB) for this notebook\n",
-          "output_type": "stream"
-        }
-      ],
-      "execution_count": 2
-    },
-    {
-      "id": "5885a858-f6a6-429b-ae83-69e3f965f032",
-      "cell_type": "markdown",
-      "source": "The `load_hplp_data` function simplifies remote access to GRIB datasets stored on the S3 bucket. It validates input parameters, constructs the correct URL for the requested data, and loads it efficiently as an xarray dataset using the corresponding `.ref` file.\n",
-      "metadata": {}
-    },
+  {
+   "cell_type": "markdown",
+   "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
+   "metadata": {},
+   "source": [
+    "> [!NOTE]\n",
+    "> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n",
+    "\n",
+    "> [!CAUTION]\n",
+    "> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "463bf2c0-ac3f-4786-9687-362393802dce",
+   "metadata": {},
+   "source": [
+    "This notebook is a comprehensive guide to accessing GRIB files for the [hplp experiment](https://apps.ecmwf.int/ifs-experiments/rd/hplp/). It includes details about the available parameters, levels, and steps, along with instructions for using the `load_hplp_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/). The GRIB files are not downloaded locally; instead, they are accessed remotely using `gribscan`, `fsspec`, and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `gribscan`.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Experiment Details and Available Data Fields\n",
+    "\n",
+    "The dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point single-time atmospheric and wave model (long window 4Dvar) outputs from the IFS.\n",
+    "The simulation was conducted on a reduced Gaussian octahedral grid (Tco399), corresponding to a horizontal resolution of approximately 28 km, and with 137 vertical model levels. The 10-day weather forecast is provided in 12-hour intervals, beginning from 2020-07-21 at 00:00:00 UTC.\n",
+    "The total volume of the available dataset is about 187 GB.\n",
+    "\n",
+    "The GRIB files are organized into different types of data fields based on the levels at which they are stored.\n",
+    "\n",
+    "### Pressure Level Parameters\n",
+    "\n",
+    "The following parameters are available as pressure level fields:\n",
+    "```python\n",
+    "[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n",
+    "```\n",
+    "\n",
+    "These parameters are stored at the following pressure levels (in hPa):\n",
+    "```python\n",
+    "[1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000]\n",
+    "```\n",
+    "\n",
+    "The corresponding IDs and more information are given in the table below:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c1d220c-d8b8-4e7c-90f4-0625b4cbc15b",
+   "metadata": {},
+   "source": [
+    "|   paramId | gridName   | gridType   | typeOfLevel   | shortName   | packingType      |\n",
+    "|----------:|:-----------|:-----------|:--------------|:------------|:-----------------|\n",
+    "|       129 | not_found  | sh         | isobaricInhPa | z           | spectral_complex |\n",
+    "|       130 | not_found  | sh         | isobaricInhPa | t           | spectral_complex |\n",
+    "|       133 | O400       | reduced_gg | isobaricInhPa | q           | spectral_complex |\n",
+    "|       135 | not_found  | sh         | isobaricInhPa | w           | spectral_complex |\n",
+    "|       138 | not_found  | sh         | isobaricInhPa | vo          | spectral_complex |\n",
+    "|       155 | not_found  | sh         | isobaricInhPa | d           | spectral_complex |\n",
+    "|       131 | not_found  | sh         | isobaricInhPa | u           | spectral_complex |\n",
+    "|       132 | not_found  | sh         | isobaricInhPa | v           | spectral_complex |\n",
+    "|       157 | not_found  | sh         | isobaricInhPa | r           | spectral_complex |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "789d34ac-2fdb-4d92-8c0c-c62b363a57a0",
+   "metadata": {},
+   "source": [
+    "### Model Level Parameters\n",
+    "\n",
+    "The following parameters are available as model level fields:\n",
+    "```python\n",
+    "[\"crwc\", \"cswc\", \"z\", \"t\", \"q\", \"w\", \"vo\", \"lnsp\", \"d\", \"u\", \"v\", \"o3\", \"clwc\", \"ciwc\", \"cc\"]\n",
+    "```\n",
+    "These parameters are stored at the following levels:\n",
+    "```python\n",
+    "[list(range(1, 138))] # Levels 1 to 137\n",
+    "```\n",
+    "For more information about the model levels please see [here](https://confluence.ecmwf.int/display/UDOC/Model+level+definitions) and [here](https://confluence.ecmwf.int/display/UDOC/L137+model+level+definitions).\n",
+    "\n",
+    "The corresponding IDs and more information are given in the table below:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d96a88c4-7954-42b5-989b-c433d4c87f4e",
+   "metadata": {},
+   "source": [
+    "|   paramId | gridName   | gridType   | typeOfLevel   | shortName   | packingType   |\n",
+    "|----------:|:-----------|:-----------|:--------------|:------------|:--------------|\n",
+    "|        75 | O400       | reduced_gg | hybrid        | crwc        | grid_ieee     |\n",
+    "|        76 | O400       | reduced_gg | hybrid        | cswc        | grid_ieee     |\n",
+    "|       129 | not_found  | sh         | hybrid        | z           | grid_ieee     |\n",
+    "|       130 | not_found  | sh         | hybrid        | t           | grid_ieee     |\n",
+    "|       133 | O400       | reduced_gg | hybrid        | q           | grid_ieee     |\n",
+    "|       135 | not_found  | sh         | hybrid        | w           | grid_ieee     |\n",
+    "|       138 | not_found  | sh         | hybrid        | vo          | grid_ieee     |\n",
+    "|       152 | not_found  | sh         | hybrid        | lnsp        | grid_ieee     |\n",
+    "|       155 | not_found  | sh         | hybrid        | d           | spectral_ieee |\n",
+    "|       131 | not_found  | sh         | hybrid        | u           | spectral_ieee |\n",
+    "|       132 | not_found  | sh         | sh            | v           | spectral_ieee |\n",
+    "|       203 | O400       | reduced_gg | sh            | o3          | spectral_ieee |\n",
+    "|       246 | O400       | reduced_gg | sh            | clwc        | spectral_ieee |\n",
+    "|       247 | O400       | reduced_gg | sh            | ciwc        | spectral_ieee |\n",
+    "|       248 | O400       | reduced_gg | sh            | cc          | spectral_ieee |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8539c000-1d96-439e-bc4d-2076c6f3f442",
+   "metadata": {},
+   "source": [
+    "### Surface Level Parameters\n",
+    "\n",
+    "The following parameters are available as surface level fields:\n",
+    "```python\n",
+    "['cin', 'sro', 'ssro', 'vis', 'i10fg', 'ci', 'asn', 'rsn', 'sst', 'istl1', 'istl2', 'istl3', 'istl4', 'swvl1', 'swvl2', 'swvl3', 'swvl4', 'es', 'smlt', 'dsrp', '10fg', 'lspf', 'uvb', 'par', 'cape', 'tclw', 'tciw', 'tcrw', 'tcsw', 'z', 'ocu', 'ocv', 'tcw', 'tcwv', 'stl1', 'sd', 'lsp', 'cp', 'sf', 'bld', 'sshf', 'slhf', 'chnk', 'msl', 'blh', 'tcc', '10u', '10v', '2t', '2d', 'ssrd', 'stl2', 'lsm', 'strd', 'ssr', 'str', 'tsr', 'ttr', 'ewss', 'nsss', 'e', 'stl3', 'lcc', 'mcc', 'hcc', 'sund', 'lgws', 'mgws', 'gwd', 'src', 'mx2t', 'mn2t', 'ro', 'tco3', 'tsrc', 'ttrc', 'ssrc', 'strc',  'tisr', 'vimd', 'fzra', 'ilspf', 'crr', 'lsrr', 'csfr', 'lssfr', 'tp', 'iews', 'inss', 'ishf', 'ie', 'skt', 'stl4', 'tsn', 'fal', 'fsr', 'flsr', 'pev', 'ptype', 'kx', 'totalx']\n",
+    "```\n",
+    "\n",
+    "The corresponding IDs and more information are given in the (hidden) table below:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "557c2ea2-1790-4af2-921d-46110b5502d3",
+   "metadata": {},
+   "source": [
+    "|   paramId | gridName   | gridType   | typeOfLevel         | shortName   | packingType   |\n",
+    "|----------:|:-----------|:-----------|:--------------------|:------------|:--------------|\n",
+    "|    228001 | O400       | reduced_gg | surface             | cin         | grid_ieee     |\n",
+    "|         8 | O400       | reduced_gg | surface             | sro         | grid_ieee     |\n",
+    "|         9 | O400       | reduced_gg | surface             | ssro        | grid_ieee     |\n",
+    "|      3020 | O400       | reduced_gg | surface             | vis         | grid_ieee     |\n",
+    "|    228029 | O400       | reduced_gg | surface             | i10fg       | grid_ieee     |\n",
+    "|        31 | O400       | reduced_gg | surface             | ci          | grid_ieee     |\n",
+    "|        32 | O400       | reduced_gg | surface             | asn         | grid_ieee     |\n",
+    "|        33 | O400       | reduced_gg | surface             | rsn         | grid_ieee     |\n",
+    "|        34 | O400       | reduced_gg | surface             | sst         | grid_ieee     |\n",
+    "|        35 | O400       | reduced_gg | surface             | istl1       | grid_ieee     |\n",
+    "|        36 | O400       | reduced_gg | surface             | istl2       | grid_ieee     |\n",
+    "|        37 | O400       | reduced_gg | surface             | istl3       | grid_ieee     |\n",
+    "|        38 | O400       | reduced_gg | surface             | istl4       | grid_ieee     |\n",
+    "|        39 | O400       | reduced_gg | surface             | swvl1       | grid_ieee     |\n",
+    "|        40 | O400       | reduced_gg | surface             | swvl2       | grid_ieee     |\n",
+    "|        41 | O400       | reduced_gg | surface             | swvl3       | grid_ieee     |\n",
+    "|        42 | O400       | reduced_gg | surface             | swvl4       | grid_ieee     |\n",
+    "|        44 | O400       | reduced_gg | surface             | es          | grid_ieee     |\n",
+    "|        45 | O400       | reduced_gg | surface             | smlt        | grid_ieee     |\n",
+    "|        47 | O400       | reduced_gg | surface             | dsrp        | grid_ieee     |\n",
+    "|        49 | O400       | reduced_gg | surface             | 10fg        | grid_ieee     |\n",
+    "|        50 | O400       | reduced_gg | surface             | lspf        | grid_ieee     |\n",
+    "|        57 | O400       | reduced_gg | surface             | uvb         | grid_ieee     |\n",
+    "|        58 | O400       | reduced_gg | surface             | par         | grid_ieee     |\n",
+    "|        59 | O400       | reduced_gg | surface             | cape        | grid_ieee     |\n",
+    "|        78 | O400       | reduced_gg | surface             | tclw        | grid_ieee     |\n",
+    "|        79 | O400       | reduced_gg | surface             | tciw        | grid_ieee     |\n",
+    "|    228089 | O400       | reduced_gg | surface             | tcrw        | grid_ieee     |\n",
+    "|    228090 | O400       | reduced_gg | surface             | tcsw        | grid_ieee     |\n",
+    "|       129 | O400       | reduced_gg | surface             | z           | grid_ieee     |\n",
+    "|    151131 | O400       | reduced_gg | surface             | ocu         | grid_ieee     |\n",
+    "|    151132 | O400       | reduced_gg | surface             | ocv         | grid_ieee     |\n",
+    "|       136 | O400       | reduced_gg | surface             | tcw         | grid_ieee     |\n",
+    "|       137 | O400       | reduced_gg | surface             | tcwv        | grid_ieee     |\n",
+    "|       139 | O400       | reduced_gg | surface             | stl1        | grid_ieee     |\n",
+    "|       141 | O400       | reduced_gg | surface             | sd          | grid_ieee     |\n",
+    "|       142 | O400       | reduced_gg | surface             | lsp         | grid_ieee     |\n",
+    "|       143 | O400       | reduced_gg | surface             | cp          | grid_ieee     |\n",
+    "|       144 | O400       | reduced_gg | surface             | sf          | grid_ieee     |\n",
+    "|       145 | O400       | reduced_gg | surface             | bld         | grid_ieee     |\n",
+    "|       146 | O400       | reduced_gg | surface             | sshf        | grid_ieee     |\n",
+    "|       147 | O400       | reduced_gg | surface             | slhf        | grid_ieee     |\n",
+    "|       148 | O400       | reduced_gg | surface             | chnk        | grid_ieee     |\n",
+    "|       151 | O400       | reduced_gg | surface             | msl         | grid_ieee     |\n",
+    "|       159 | O400       | reduced_gg | surface             | blh         | grid_ieee     |\n",
+    "|       164 | O400       | reduced_gg | surface             | tcc         | grid_ieee     |\n",
+    "|       165 | O400       | reduced_gg | surface             | 10u         | grid_ieee     |\n",
+    "|       166 | O400       | reduced_gg | surface             | 10v         | grid_ieee     |\n",
+    "|       167 | O400       | reduced_gg | surface             | 2t          | grid_ieee     |\n",
+    "|       168 | O400       | reduced_gg | surface             | 2d          | grid_ieee     |\n",
+    "|       169 | O400       | reduced_gg | surface             | ssrd        | grid_ieee     |\n",
+    "|       170 | O400       | reduced_gg | surface             | stl2        | grid_ieee     |\n",
+    "|       172 | O400       | reduced_gg | surface             | lsm         | grid_ieee     |\n",
+    "|       175 | O400       | reduced_gg | surface             | strd        | grid_ieee     |\n",
+    "|       176 | O400       | reduced_gg | surface             | ssr         | grid_ieee     |\n",
+    "|       177 | O400       | reduced_gg | surface             | str         | grid_ieee     |\n",
+    "|       178 | O400       | reduced_gg | surface             | tsr         | grid_ieee     |\n",
+    "|       179 | O400       | reduced_gg | surface             | ttr         | grid_ieee     |\n",
+    "|       180 | O400       | reduced_gg | surface             | ewss        | grid_ieee     |\n",
+    "|       181 | O400       | reduced_gg | surface             | nsss        | grid_ieee     |\n",
+    "|       182 | O400       | reduced_gg | surface             | e           | grid_ieee     |\n",
+    "|       183 | O400       | reduced_gg | surface             | stl3        | grid_ieee     |\n",
+    "|       186 | O400       | reduced_gg | surface             | lcc         | grid_ieee     |\n",
+    "|       187 | O400       | reduced_gg | surface             | mcc         | grid_ieee     |\n",
+    "|       188 | O400       | reduced_gg | surface             | hcc         | grid_ieee     |\n",
+    "|       189 | O400       | reduced_gg | surface             | sund        | grid_ieee     |\n",
+    "|       195 | O400       | reduced_gg | surface             | lgws        | grid_ieee     |\n",
+    "|       196 | O400       | reduced_gg | surface             | mgws        | grid_ieee     |\n",
+    "|       197 | O400       | reduced_gg | surface             | gwd         | grid_ieee     |\n",
+    "|       198 | O400       | reduced_gg | surface             | src         | grid_ieee     |\n",
+    "|       201 | O400       | reduced_gg | surface             | mx2t        | grid_ieee     |\n",
+    "|       202 | O400       | reduced_gg | surface             | mn2t        | grid_ieee     |\n",
+    "|       205 | O400       | reduced_gg | surface             | ro          | grid_ieee     |\n",
+    "|       206 | O400       | reduced_gg | surface             | tco3        | grid_ieee     |\n",
+    "|       208 | O400       | reduced_gg | surface             | tsrc        | grid_ieee     |\n",
+    "|       209 | O400       | reduced_gg | surface             | ttrc        | grid_ieee     |\n",
+    "|       210 | O400       | reduced_gg | surface             | ssrc        | grid_ieee     |\n",
+    "|       211 | O400       | reduced_gg | surface             | strc        | grid_ieee     |\n",
+    "|       212 | O400       | reduced_gg | surface             | tisr        | grid_ieee     |\n",
+    "|       213 | O400       | reduced_gg | surface             | vimd        | grid_ieee     |\n",
+    "|    228216 | O400       | reduced_gg | surface             | fzra        | grid_ieee     |\n",
+    "|    228217 | O400       | reduced_gg | surface             | ilspf       | grid_ieee     |\n",
+    "|    228218 | O400       | reduced_gg | surface             | crr         | grid_ieee     |\n",
+    "|    228219 | O400       | reduced_gg | surface             | lsrr        | grid_ieee     |\n",
+    "|    228220 | O400       | reduced_gg | depthBelowLandLayer | csfr        | grid_ieee     |\n",
+    "|    228221 | O400       | reduced_gg | depthBelowLandLayer | lssfr       | grid_ieee     |\n",
+    "|       228 | O400       | reduced_gg | depthBelowLandLayer | tp          | grid_ieee     |\n",
+    "|       229 | O400       | reduced_gg | depthBelowLandLayer | iews        | grid_ieee     |\n",
+    "|       230 | O400       | reduced_gg | depthBelowLandLayer | inss        | grid_ieee     |\n",
+    "|       231 | O400       | reduced_gg | depthBelowLandLayer | ishf        | grid_ieee     |\n",
+    "|       232 | O400       | reduced_gg | depthBelowLandLayer | ie          | grid_ieee     |\n",
+    "|       235 | O400       | reduced_gg | depthBelowLandLayer | skt         | grid_ieee     |\n",
+    "|       236 | O400       | reduced_gg | depthBelowLandLayer | stl4        | grid_ieee     |\n",
+    "|       238 | O400       | reduced_gg | depthBelowLandLayer | tsn         | grid_ieee     |\n",
+    "|       243 | O400       | reduced_gg | depthBelowLandLayer | fal         | grid_ieee     |\n",
+    "|       244 | O400       | reduced_gg | depthBelowLandLayer | fsr         | grid_ieee     |\n",
+    "|       245 | O400       | reduced_gg | depthBelowLandLayer | flsr        | grid_ieee     |\n",
+    "|    228251 | O400       | reduced_gg | depthBelowLandLayer | pev         | grid_ieee     |\n",
+    "|    260015 | O400       | reduced_gg | depthBelowLandLayer | ptype       | grid_ieee     |\n",
+    "|    260121 | O400       | reduced_gg | depthBelowLandLayer | kx          | grid_ieee     |\n",
+    "|    260123 | O400       | reduced_gg | depthBelowLandLayer | totalx      | grid_ieee     |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f18ec120-3193-42d4-bcda-5a8a80bc574c",
+   "metadata": {},
+   "source": [
+    "### Wave Model Parameters\n",
+    "\n",
+    "The following parameters are available for the wave model data:\n",
+    "```python\n",
+    "['wstar', 'rhoao', 'phioc', 'tauoc', 'ust', 'vst', 'hmax', 'wmb', 'mp1', 'mp2', 'wdw', 'swh', 'mwd', 'pp1d', 'mwp', 'cdww', 'wind','dwi']\n",
+    "```\n",
+    "\n",
+    "The corresponding IDs and more information are given in the table below:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "968e5b37-1165-4396-b0eb-8b9e03dd7d2b",
+   "metadata": {},
+   "source": [
+    "|   paramId | gridName   | gridType   | typeOfLevel       | shortName   | packingType   |\n",
+    "|----------:|:-----------|:-----------|:------------------|:------------|:--------------|\n",
+    "|    140208 | not_found  | reduced_ll | meanSea           | wstar       | grid_simple   |\n",
+    "|    140209 | not_found  | reduced_ll | meanSea           | rhoao       | grid_simple   |\n",
+    "|    140212 | not_found  | reduced_ll | meanSea           | phioc       | grid_simple   |\n",
+    "|    140214 | not_found  | reduced_ll | meanSea           | tauoc       | grid_simple   |\n",
+    "|    140215 | not_found  | reduced_ll | meanSea           | ust         | grid_simple   |\n",
+    "|    140216 | not_found  | reduced_ll | meanSea           | vst         | grid_simple   |\n",
+    "|    140218 | not_found  | reduced_ll | meanSea           | hmax        | grid_simple   |\n",
+    "|    140219 | not_found  | reduced_ll | meanSea           | wmb         | grid_simple   |\n",
+    "|    140220 | not_found  | reduced_ll | meanSea           | mp1         | grid_simple   |\n",
+    "|    140221 | not_found  | reduced_ll | meanSea           | mp2         | grid_simple   |\n",
+    "|    140222 | not_found  | reduced_ll | meanSea           | wdw         | grid_simple   |\n",
+    "|    140229 | not_found  | reduced_ll | meanSea           | swh         | grid_simple   |\n",
+    "|    140230 | not_found  | reduced_ll | meanSea           | mwd         | grid_simple   |\n",
+    "|    140231 | not_found  | reduced_ll | meanSea           | pp1d        | grid_simple   |\n",
+    "|    140232 | not_found  | reduced_ll | meanSea           | mwp         | grid_simple   |\n",
+    "|    140233 | not_found  | reduced_ll | heightAboveGround | cdww        | grid_simple   |\n",
+    "|    140245 | not_found  | reduced_ll | heightAboveGround | wind        | grid_simple   |\n",
+    "|    140249 | not_found  | reduced_ll | heightAboveGround | dwi         | grid_simple   |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
+   "metadata": {},
+   "source": [
+    "## Available Steps \n",
+    "\n",
+    "The following timesteps are available: \n",
+    "\n",
+    "```python \n",
+    "[0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144, 156, 168, 180, 192, 204, 216, 228, 240]\n",
+    " ```\n",
+    "Timestep 0 is 2020-07-21T 00:00:00 UTC, increasing in 12-hour steps to 240  which is 2020-07-31T 00:00:00 UTC.\n",
+    "\n",
+    "## Using the `load_hplp_data` Function\n",
+    "\n",
+    "To load the GRIB files, you can use the following function:\n",
+    "\n",
+    "```python\n",
+    "load_hplp_data(leveltype=None, gridtype=None, step=None, remap=False)\n",
+    "```\n",
+    "\n",
+    "### Parameters\n",
+    "\n",
+    "- **`leveltype`**: Defines the type of data to load. Choose one of the following:\n",
+    "  - `\"pl\"`: Pressure level fields.\n",
+    "  - `\"ml\"`: Model level fields.\n",
+    "  - `\"sfc\"`: Surface level fields.\n",
+    "  - `\"wave\"`: Wave model fields.\n",
+    "- **`gridtype`**: Specifies the grid type for the data. Options include:\n",
+    "  - `\"reduced_gg\"`: Reduced Gaussian grid (available for `pl`, `ml`, and `sfc`).\n",
+    "  - `\"sh\"`: Spherical harmonic coefficients (available for `pl` and `ml`).\n",
+    "  - `\"reduced_ll\"`: Reduced lat-long grid (available for `wave`).\n",
+    "- **`step`**: The `ml` data on the `reduced_gg` grid is split into two files. Specify the timestep (`step`) to load the corresponding file. Valid steps are:\n",
+    "  - `0-120`: First file (2020-07-21T00:00:00 to 2020-07-26T00:00:00).\n",
+    "  - `132-240`: Second file (2020-07-26T12:00:00 to 2020-07-31T00:00:00).\n",
+    "- **`remap`**: When using gridtype=`sh`, data cannot be directly plotted. To load remapped data on an O400 grid, set `remap=True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# URL of S3 bucket\n",
+    "BASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
+   "metadata": {},
+   "source": [
+    "First, the package `zarr` (modern dataset format that is specifically designed for chunked access), its dependencies, and `gribscan` need to be imported for the remote access."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "3a68e663-c6c8-49c2-9678-929d87f772b4",
-      "cell_type": "code",
-      "source": "import json\n\nimport requests\nimport xarray as xr\n\ndef load_hplp_data(leveltype=None, gridtype=None, step=None, remap=False):\n    if leveltype not in {\"pl\", \"ml\", \"sfc\", \"wave\"}:\n        raise ValueError(f\"Invalid leveltype: {leveltype}\")\n\n    if leveltype in {\"ml\", \"pl\"} and not gridtype:\n        raise ValueError(f\"Gridtype is required for leveltype '{leveltype}'. Available: reduced_gg, sh\")\n\n    if remap and gridtype != \"sh\":\n        raise ValueError(f\"Only 'sh' fields can be remapped.\")\n\n    if leveltype == \"wave\" and gridtype != \"reduced_ll\":\n        print(\"Warning: Wave model data is stored on a reduced_ll grid.\")\n\n    if leveltype == \"sfc\" and gridtype != \"reduced_gg\":\n        print(\"Warning: Surface level data is stored on a reduced_gg grid.\")\n\n    if step and not (leveltype == \"ml\" and gridtype == \"reduced_gg\"):\n        print(\"Warning: Specifying 'step' is unnecessary for this configuration and will be ignored.\")\n\n    if leveltype in {\"sfc\", \"wave\"}:\n        url = f\"{BASE_URL}/hplp/hplp_{leveltype}.grib\"\n    elif leveltype == \"ml\" and gridtype == \"reduced_gg\":\n        if step is None:\n            raise ValueError(\n                \"The ml reduced_gg data is split into two parts:\\n\"\n                \"  - Steps: 0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120 (2020-07-21T00:00:00 to 2020-07-26T00:00:00)\\n\"\n                \"  - Steps: 132, 144, 156, 168, 180, 192, 204, 216, 228, 240 (2020-07-26T12:00:00 to 2020-07-31T00:00:00)\\n\"\n                \"Specify a step smaller than 120 for accessing the first part, \\n\"\n                \"and a step greater or equal to 132 for accessing the second part.\"\n            )          \n        if step <= 120:\n            url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_0_120.grib\"\n        else:\n            url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_132_240.grib\"\n    else:\n        url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}\" + (\"_O400.grib\" if remap else \".grib\")\n    ref = requests.get(f\"{url}.ref\").json()\n\n    print(f\"Loading dataset {url}\")\n\n    return xr.open_dataset(\n        \"reference://\",\n        engine=\"zarr\",\n        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n        consolidated=False,\n    )",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [],
-      "execution_count": 3
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyodide]: Loading aiohttp, aiosignal, asciitree, async-timeout, attrs, cffi, cfgrib, click, eccodes, findlibs, frozenlist, fsspec, gribscan, msgpack, multidict, numcodecs, numpy, openblas, pandas, propcache, pycparser, python-dateutil, pytz, scipy, tzdata, xarray, yarl, zarr\n",
+      "[pyodide]: Loaded aiohttp, aiosignal, asciitree, async-timeout, attrs, cffi, cfgrib, click, eccodes, findlibs, frozenlist, fsspec, gribscan, msgpack, multidict, numcodecs, numpy, openblas, pandas, propcache, pycparser, python-dateutil, pytz, scipy, tzdata, xarray, yarl, zarr\n",
+      "[pyodide]: Memory usage has grown to 202.8MiB (from 49.9MiB) for this notebook\n"
+     ]
+    }
+   ],
+   "source": [
+    "import aiohttp\n",
+    "import fsspec\n",
+    "import gribscan\n",
+    "import zarr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5885a858-f6a6-429b-ae83-69e3f965f032",
+   "metadata": {},
+   "source": [
+    "The `load_hplp_data` function simplifies remote access to GRIB datasets stored on the S3 bucket. It validates input parameters, constructs the correct URL for the requested data, and loads it efficiently as an xarray dataset using the corresponding `.ref` file.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3a68e663-c6c8-49c2-9678-929d87f772b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "import requests\n",
+    "import xarray as xr\n",
+    "\n",
+    "def load_hplp_data(leveltype=None, gridtype=None, step=None, remap=False):\n",
+    "    if leveltype not in {\"pl\", \"ml\", \"sfc\", \"wave\"}:\n",
+    "        raise ValueError(f\"Invalid leveltype: '{leveltype}'. Available leveltypes: pl, ml, sfc, wave\")\n",
+    "\n",
+    "    if leveltype in {\"ml\", \"pl\"} and not gridtype:\n",
+    "        raise ValueError(f\"Gridtype is required for leveltype '{leveltype}'. Available: reduced_gg, sh\")\n",
+    "\n",
+    "    if remap and gridtype != \"sh\":\n",
+    "        raise ValueError(f\"Only 'sh' fields can be remapped.\")\n",
+    "\n",
+    "    if leveltype == \"wave\" and gridtype != \"reduced_ll\":\n",
+    "        print(\"Warning: Wave model data is stored on a reduced_ll grid.\")\n",
+    "\n",
+    "    if leveltype == \"sfc\" and gridtype != \"reduced_gg\":\n",
+    "        print(\"Warning: Surface level data is stored on a reduced_gg grid.\")\n",
+    "\n",
+    "    if step and not (leveltype == \"ml\" and gridtype == \"reduced_gg\"):\n",
+    "        print(\"Warning: Specifying 'step' is unnecessary for this configuration and will be ignored.\")\n",
+    "\n",
+    "    if leveltype in {\"sfc\", \"wave\"}:\n",
+    "        url = f\"{BASE_URL}/hplp/hplp_{leveltype}.grib\"\n",
+    "    elif leveltype == \"ml\" and gridtype == \"reduced_gg\":\n",
+    "        if step is None:\n",
+    "            raise ValueError(\n",
+    "                \"The ml reduced_gg data is split into two parts:\\n\"\n",
+    "                \"  - Steps: 0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120 (2020-07-21T00:00:00 to 2020-07-26T00:00:00)\\n\"\n",
+    "                \"  - Steps: 132, 144, 156, 168, 180, 192, 204, 216, 228, 240 (2020-07-26T12:00:00 to 2020-07-31T00:00:00)\\n\"\n",
+    "                \"Specify a step smaller than 120 for accessing the first part, \\n\"\n",
+    "                \"and a step greater or equal to 132 for accessing the second part.\"\n",
+    "            )          \n",
+    "        if step <= 120:\n",
+    "            url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_0_120.grib\"\n",
+    "        else:\n",
+    "            url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_132_240.grib\"\n",
+    "    else:\n",
+    "        url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}\" + (\"_O400.grib\" if remap else \".grib\")\n",
+    "    ref = requests.get(f\"{url}.ref\").json()\n",
+    "\n",
+    "    print(f\"Loading dataset {url}\")\n",
+    "\n",
+    "    return xr.open_dataset(\n",
+    "        \"reference://\",\n",
+    "        engine=\"zarr\",\n",
+    "        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n",
+    "        consolidated=False,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10b46f55-ec2a-45ea-87c6-46173ce58696",
+   "metadata": {},
+   "source": [
+    "## Example Usage\n",
+    "\n",
+    "To load a GRIB file for pressure level parameters, you can use the following example. This will load the pressure level data stored on a `reduced_gg` grid. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "37c31fe0-661a-4b71-afb0-61f7fb804f5f",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "10b46f55-ec2a-45ea-87c6-46173ce58696",
-      "cell_type": "markdown",
-      "source": "## Example Usage\n\nTo load a GRIB file for pressure level parameters, you can use the following example. This will load the pressure level data stored on a `reduced_gg` grid. \n",
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_pl_reduced_gg.grib\n"
+     ]
     },
     {
-      "id": "37c31fe0-661a-4b71-afb0-61f7fb804f5f",
-      "cell_type": "code",
-      "source": "ds_pl = load_hplp_data(leveltype=\"pl\", gridtype=\"reduced_gg\")\nds_pl",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_pl_reduced_gg.grib\n",
-          "output_type": "stream"
-        },
-        {
-          "execution_count": 4,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "<xarray.Dataset> Size: 2GB\nDimensions:  (value: 654400, level: 21, time: 21)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    q        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\nDimensions:  (value: 654400, level: 21, time: 21)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    q        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8d6e63da-6a18-44b8-8f61-2736502626c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8d6e63da-6a18-44b8-8f61-2736502626c0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>value</span>: 654400</li><li><span class='xr-has-index'>level</span>: 21</li><li><span class='xr-has-index'>time</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-43f5ce37-a2f9-4b70-85d4-79fbc2fa8bf8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43f5ce37-a2f9-4b70-85d4-79fbc2fa8bf8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-01fb177b-facf-4779-b220-2fc346cb6572' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01fb177b-facf-4779-b220-2fc346cb6572' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44e3996a-dddb-441e-b1ab-998f4c3c46dc' class='xr-var-data-in' type='checkbox'><label for='data-44e3996a-dddb-441e-b1ab-998f4c3c46dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 500 700 850 925 1000</div><input id='attrs-36c00e96-1d19-45eb-9bd9-075d676e3994' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36c00e96-1d19-45eb-9bd9-075d676e3994' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd631c10-41aa-4e84-9963-87c8d340becf' class='xr-var-data-in' type='checkbox'><label for='data-bd631c10-41aa-4e84-9963-87c8d340becf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-21570a56-59ad-4fdc-8bb0-560a7d61c990' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21570a56-59ad-4fdc-8bb0-560a7d61c990' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eacfc7fa-9d0c-4450-a87d-fc5b9f47d6ad' class='xr-var-data-in' type='checkbox'><label for='data-eacfc7fa-9d0c-4450-a87d-fc5b9f47d6ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-610ac5b8-bdb9-4880-8cf7-7eeb61adae56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-610ac5b8-bdb9-4880-8cf7-7eeb61adae56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfb4ec5d-3764-4091-9db4-191e27b40025' class='xr-var-data-in' type='checkbox'><label for='data-bfb4ec5d-3764-4091-9db4-191e27b40025' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ddf1ad7c-3bda-487e-ba9b-623cc5ad98a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ddf1ad7c-3bda-487e-ba9b-623cc5ad98a3' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9093ea3d-07ef-4e28-bace-3d259bd5913c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9093ea3d-07ef-4e28-bace-3d259bd5913c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1673522-1a16-4b97-a0e0-94dc130f76fc' class='xr-var-data-in' type='checkbox'><label for='data-f1673522-1a16-4b97-a0e0-94dc130f76fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>133</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>q</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>specific_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>q</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c56e0a4-0bd0-43b7-84b2-c30331550c98' class='xr-section-summary-in' type='checkbox'  ><label for='section-4c56e0a4-0bd0-43b7-84b2-c30331550c98' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c7600c2-6de3-4b92-be03-e4e456277b20' class='xr-index-data-in' type='checkbox'/><label for='index-2c7600c2-6de3-4b92-be03-e4e456277b20' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000],\n      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e650c38c-6253-483d-b6d3-ee2eae70191b' class='xr-index-data-in' type='checkbox'/><label for='index-e650c38c-6253-483d-b6d3-ee2eae70191b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a6f703b-89ff-41c1-8f79-ff29efd1bbcd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5a6f703b-89ff-41c1-8f79-ff29efd1bbcd' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
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+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\n",
+       "Dimensions:  (value: 654400, level: 21, time: 21)\n",
+       "Coordinates:\n",
+       "    lat      (value) float64 5MB ...\n",
+       "  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n",
+       "    lon      (value) float64 5MB ...\n",
+       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
+       "Dimensions without coordinates: value\n",
+       "Data variables:\n",
+       "    q        (time, level, value) float64 2GB ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8d6e63da-6a18-44b8-8f61-2736502626c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8d6e63da-6a18-44b8-8f61-2736502626c0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>value</span>: 654400</li><li><span class='xr-has-index'>level</span>: 21</li><li><span class='xr-has-index'>time</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-43f5ce37-a2f9-4b70-85d4-79fbc2fa8bf8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43f5ce37-a2f9-4b70-85d4-79fbc2fa8bf8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-01fb177b-facf-4779-b220-2fc346cb6572' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01fb177b-facf-4779-b220-2fc346cb6572' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44e3996a-dddb-441e-b1ab-998f4c3c46dc' class='xr-var-data-in' type='checkbox'><label for='data-44e3996a-dddb-441e-b1ab-998f4c3c46dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 500 700 850 925 1000</div><input id='attrs-36c00e96-1d19-45eb-9bd9-075d676e3994' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36c00e96-1d19-45eb-9bd9-075d676e3994' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd631c10-41aa-4e84-9963-87c8d340becf' class='xr-var-data-in' type='checkbox'><label for='data-bd631c10-41aa-4e84-9963-87c8d340becf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n",
+       "        200,  250,  300,  400,  500,  700,  850,  925, 1000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-21570a56-59ad-4fdc-8bb0-560a7d61c990' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21570a56-59ad-4fdc-8bb0-560a7d61c990' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eacfc7fa-9d0c-4450-a87d-fc5b9f47d6ad' class='xr-var-data-in' type='checkbox'><label for='data-eacfc7fa-9d0c-4450-a87d-fc5b9f47d6ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-610ac5b8-bdb9-4880-8cf7-7eeb61adae56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-610ac5b8-bdb9-4880-8cf7-7eeb61adae56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfb4ec5d-3764-4091-9db4-191e27b40025' class='xr-var-data-in' type='checkbox'><label for='data-bfb4ec5d-3764-4091-9db4-191e27b40025' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ddf1ad7c-3bda-487e-ba9b-623cc5ad98a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ddf1ad7c-3bda-487e-ba9b-623cc5ad98a3' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9093ea3d-07ef-4e28-bace-3d259bd5913c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9093ea3d-07ef-4e28-bace-3d259bd5913c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1673522-1a16-4b97-a0e0-94dc130f76fc' class='xr-var-data-in' type='checkbox'><label for='data-f1673522-1a16-4b97-a0e0-94dc130f76fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>133</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>q</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>specific_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>q</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c56e0a4-0bd0-43b7-84b2-c30331550c98' class='xr-section-summary-in' type='checkbox'  ><label for='section-4c56e0a4-0bd0-43b7-84b2-c30331550c98' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c7600c2-6de3-4b92-be03-e4e456277b20' class='xr-index-data-in' type='checkbox'/><label for='index-2c7600c2-6de3-4b92-be03-e4e456277b20' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n",
+       "        200,  250,  300,  400,  500,  700,  850,  925, 1000],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e650c38c-6253-483d-b6d3-ee2eae70191b' class='xr-index-data-in' type='checkbox'/><label for='index-e650c38c-6253-483d-b6d3-ee2eae70191b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-31 00:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a6f703b-89ff-41c1-8f79-ff29efd1bbcd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5a6f703b-89ff-41c1-8f79-ff29efd1bbcd' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n",
+       "</dd></dl></div></li></ul></div></div>"
       ],
-      "execution_count": 4
-    },
+      "text/plain": [
+       "<xarray.Dataset> Size: 2GB\n",
+       "Dimensions:  (value: 654400, level: 21, time: 21)\n",
+       "Coordinates:\n",
+       "    lat      (value) float64 5MB ...\n",
+       "  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n",
+       "    lon      (value) float64 5MB ...\n",
+       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
+       "Dimensions without coordinates: value\n",
+       "Data variables:\n",
+       "    q        (time, level, value) float64 2GB ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc..."
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_pl = load_hplp_data(leveltype=\"pl\", gridtype=\"reduced_gg\")\n",
+    "ds_pl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33418b16-9735-4eb9-b540-942325d552df",
+   "metadata": {},
+   "source": [
+    "The `earthkit` package is used for plotting the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "169008a5-cfa4-4078-8bad-32510c9a91e7",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "33418b16-9735-4eb9-b540-942325d552df",
-      "cell_type": "markdown",
-      "source": "The `earthkit` package is used for plotting the data.",
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyodide]: Loading Cartopy, Jinja2, MarkupSafe, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cftime, cligj, cloudpickle, cycler, dask, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, importlib_metadata, jsonschema, jsonschema_specifications, kiwisolver, locket, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, partd, pdbufr, pint, pkgconfig, pyparsing, pyproj, pyrsistent, pyshp, pyyaml, rasterio, referencing, rpds-py, setuptools, shapely, toolz, tqdm, typing-extensions, zipp\n",
+      "[pyodide]: Loaded Cartopy, Jinja2, MarkupSafe, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cftime, cligj, cloudpickle, cycler, dask, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, importlib_metadata, jsonschema, jsonschema_specifications, kiwisolver, locket, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, partd, pdbufr, pint, pkgconfig, pyparsing, pyproj, pyrsistent, pyshp, pyyaml, rasterio, referencing, rpds-py, setuptools, shapely, toolz, tqdm, typing-extensions, zipp\n"
+     ]
     },
     {
-      "id": "169008a5-cfa4-4078-8bad-32510c9a91e7",
-      "cell_type": "code",
-      "source": "import earthkit.plots\nimport earthkit.plots.quickmap",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Loading Cartopy, Jinja2, MarkupSafe, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cftime, cligj, cloudpickle, cycler, dask, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, importlib_metadata, jsonschema, jsonschema_specifications, kiwisolver, locket, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, partd, pdbufr, pint, pkgconfig, pyparsing, pyproj, pyrsistent, pyshp, pyyaml, rasterio, referencing, rpds-py, setuptools, shapely, toolz, tqdm, typing-extensions, zipp\n[pyodide]: Loaded Cartopy, Jinja2, MarkupSafe, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cftime, cligj, cloudpickle, cycler, dask, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, importlib_metadata, jsonschema, jsonschema_specifications, kiwisolver, locket, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, partd, pdbufr, pint, pkgconfig, pyparsing, pyproj, pyrsistent, pyshp, pyyaml, rasterio, referencing, rpds-py, setuptools, shapely, toolz, tqdm, typing-extensions, zipp\n",
-          "output_type": "stream"
-        },
-        {
-          "name": "stderr",
-          "text": "/lib/python3.12/site-packages/earthkit/regrid/utils/caching.py:27: UserWarning: only soft file lock is available\n  from filelock import FileLock\n",
-          "output_type": "stream"
-        },
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 292.0MiB (from 202.8MiB) for this notebook\n",
-          "output_type": "stream"
-        }
-      ],
-      "execution_count": 5
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lib/python3.12/site-packages/earthkit/regrid/utils/caching.py:27: UserWarning: only soft file lock is available\n",
+      "  from filelock import FileLock\n"
+     ]
     },
     {
-      "id": "6ee55166-f151-474c-b36c-1e07bc516906",
-      "cell_type": "markdown",
-      "source": "The data can be visualized as demonstrated below. For example, the specific humidity at **pressure level 1000hPa** on **2020-07-31 at 00:00:00 UTC** is plotted.\n",
-      "metadata": {}
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyodide]: Memory usage has grown to 292.0MiB (from 202.8MiB) for this notebook\n"
+     ]
+    }
+   ],
+   "source": [
+    "import earthkit.plots\n",
+    "import earthkit.plots.quickmap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ee55166-f151-474c-b36c-1e07bc516906",
+   "metadata": {},
+   "source": [
+    "The data can be visualized as demonstrated below. For example, the specific humidity at **pressure level 1000hPa** on **2020-07-31 at 00:00:00 UTC** is plotted.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2e12e256",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "2e12e256",
-      "cell_type": "code",
-      "source": "earthkit.plots.quickmap.plot(\n    ds_pl[\"q\"].sel(level=1000).sel(time=\"2020-07-31T00:00:00\"),\n    x=\"lon\", y=\"lat\",\n);",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "text": "/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip\n  warnings.warn(f'Downloading: {url}', DownloadWarning)\n",
-          "output_type": "stream"
-        },
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 793.9MiB (from 292.0MiB) for this notebook\n",
-          "output_type": "stream"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 700x600 with 2 Axes>",
-            "image/png": 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-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 6
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip\n",
+      "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
+     ]
     },
     {
-      "id": "7731f334-8487-4256-ac41-8b693a504b35",
-      "cell_type": "markdown",
-      "source": "The following example will load GRIB files for pressure level data stored as **spherical harmonic coefficients (sh)**. Since this data requires remapping to an **O400 grid** for visualization, the remapping step is included.\n",
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyodide]: Memory usage has grown to 793.9MiB (from 292.0MiB) for this notebook\n"
+     ]
     },
     {
-      "id": "5a574d92-3c0a-429b-97e8-d024d78c5daf",
-      "cell_type": "code",
-      "source": "ds_pl_O400 = load_hplp_data(leveltype=\"pl\", gridtype=\"sh\", remap=True)\nds_pl_O400",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_pl_sh_O400.grib\n",
-          "output_type": "stream"
-        },
-        {
-          "execution_count": 7,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "<xarray.Dataset> Size: 18GB\nDimensions:  (time: 21, level: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    d        (time, level, value) float64 2GB ...\n    r        (time, level, value) float64 2GB ...\n    t        (time, level, value) float64 2GB ...\n    u        (time, level, value) float64 2GB ...\n    v        (time, level, value) float64 2GB ...\n    vo       (time, level, value) float64 2GB ...\n    w        (time, level, value) float64 2GB ...\n    z        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 18GB\nDimensions:  (time: 21, level: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    d        (time, level, value) float64 2GB ...\n    r        (time, level, value) float64 2GB ...\n    t        (time, level, value) float64 2GB ...\n    u        (time, level, value) float64 2GB ...\n    v        (time, level, value) float64 2GB ...\n    vo       (time, level, value) float64 2GB ...\n    w        (time, level, value) float64 2GB ...\n    z        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4e8cf0f9-ac10-4de8-83bb-07808e533ee8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4e8cf0f9-ac10-4de8-83bb-07808e533ee8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span class='xr-has-index'>level</span>: 21</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-35b6a18d-2cec-4761-af34-3de25e69a74b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35b6a18d-2cec-4761-af34-3de25e69a74b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-82c0e70f-19a2-4a7c-a0a7-81e3af27c769' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82c0e70f-19a2-4a7c-a0a7-81e3af27c769' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb71df00-ba5e-4654-bf53-56f54c39148f' class='xr-var-data-in' type='checkbox'><label for='data-bb71df00-ba5e-4654-bf53-56f54c39148f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 500 700 850 925 1000</div><input id='attrs-585ef21b-f7ca-417a-a7d6-811da5045e35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-585ef21b-f7ca-417a-a7d6-811da5045e35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21edf3ce-16ff-45f3-9438-b5c92da107cf' class='xr-var-data-in' type='checkbox'><label for='data-21edf3ce-16ff-45f3-9438-b5c92da107cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-918e9901-c227-411f-9bf4-47b0a7ebfa6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-918e9901-c227-411f-9bf4-47b0a7ebfa6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2422212-d2c4-4dc2-8a74-f452dff1e90b' class='xr-var-data-in' type='checkbox'><label for='data-f2422212-d2c4-4dc2-8a74-f452dff1e90b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-e1d7eb9b-8cb2-4583-9a81-5185a3b010fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1d7eb9b-8cb2-4583-9a81-5185a3b010fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de19481b-dd0b-455a-9c38-1c33974d2796' class='xr-var-data-in' type='checkbox'><label for='data-de19481b-dd0b-455a-9c38-1c33974d2796' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4f925b9d-2efb-48ea-9ce9-f55b32e020db' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4f925b9d-2efb-48ea-9ce9-f55b32e020db' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>d</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-609760c9-c819-4d24-9b42-6006fd80ec4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-609760c9-c819-4d24-9b42-6006fd80ec4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3a5df56-abcd-4823-9629-c18712e03366' class='xr-var-data-in' type='checkbox'><label for='data-f3a5df56-abcd-4823-9629-c18712e03366' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>155</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>d</dd><dt><span>GRIB_units :</span></dt><dd>s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Divergence</dd><dt><span>GRIB_cfName :</span></dt><dd>divergence_of_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s**-1</dd><dt><span>long_name :</span></dt><dd>Divergence</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3d8af75b-b0ed-4c97-8df0-66a5040d6a30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d8af75b-b0ed-4c97-8df0-66a5040d6a30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c94b95c-6867-47be-9ad8-9fa3219e4f90' class='xr-var-data-in' type='checkbox'><label for='data-1c94b95c-6867-47be-9ad8-9fa3219e4f90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>157</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>r</dd><dt><span>GRIB_units :</span></dt><dd>%</dd><dt><span>GRIB_name :</span></dt><dd>Relative humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>relative_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>r</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Relative humidity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-37c24bfe-27db-4351-805d-0230628906d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37c24bfe-27db-4351-805d-0230628906d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b38a2b98-4464-40b3-8d6d-0fbaf71cc9ec' class='xr-var-data-in' type='checkbox'><label for='data-b38a2b98-4464-40b3-8d6d-0fbaf71cc9ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>130</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Temperature</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49d8e546-9b54-4846-bbef-5606ced2e7a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49d8e546-9b54-4846-bbef-5606ced2e7a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-949dd32e-92e8-40e1-8439-12697a82d88b' class='xr-var-data-in' type='checkbox'><label for='data-949dd32e-92e8-40e1-8439-12697a82d88b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>131</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>u</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>U component of wind</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>U component of wind</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-58369383-d9d3-4fb4-b13e-b1da2943a630' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58369383-d9d3-4fb4-b13e-b1da2943a630' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93de18dd-5e19-4d13-999d-e7816bbe3284' class='xr-var-data-in' type='checkbox'><label for='data-93de18dd-5e19-4d13-999d-e7816bbe3284' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>132</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>v</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>V component of wind</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>V component of wind</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vo</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b05ab3c-6fde-4903-9ffd-c6b747f83941' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b05ab3c-6fde-4903-9ffd-c6b747f83941' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77413552-0afa-44dc-8f42-958d656073f0' class='xr-var-data-in' type='checkbox'><label for='data-77413552-0afa-44dc-8f42-958d656073f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>138</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vo</dd><dt><span>GRIB_units :</span></dt><dd>s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Vorticity (relative)</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_relative_vorticity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vo</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s**-1</dd><dt><span>long_name :</span></dt><dd>Vorticity (relative)</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6051ead6-4a2b-463e-9658-63f979849af8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6051ead6-4a2b-463e-9658-63f979849af8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-382c0893-769b-4b79-afc2-8adc4ce4f13d' class='xr-var-data-in' type='checkbox'><label for='data-382c0893-769b-4b79-afc2-8adc4ce4f13d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>135</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>w</dd><dt><span>GRIB_units :</span></dt><dd>Pa s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Vertical velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd><dt><span>GRIB_cfVarName :</span></dt><dd>w</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Pa s**-1</dd><dt><span>long_name :</span></dt><dd>Vertical velocity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e991dac9-ad83-429c-8577-8e7f9dcfaed0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e991dac9-ad83-429c-8577-8e7f9dcfaed0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81490b7e-25c6-4958-b2bf-da2a0a818dde' class='xr-var-data-in' type='checkbox'><label for='data-81490b7e-25c6-4958-b2bf-da2a0a818dde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>long_name :</span></dt><dd>Geopotential</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a063e59e-2972-4360-a210-2b0868da8c83' class='xr-section-summary-in' type='checkbox'  ><label for='section-a063e59e-2972-4360-a210-2b0868da8c83' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-21b367d4-2904-4213-8d80-ce38d6e85f20' class='xr-index-data-in' type='checkbox'/><label for='index-21b367d4-2904-4213-8d80-ce38d6e85f20' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000],\n      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f306d80f-eaf6-4cdb-b8ce-84f85f51b28c' class='xr-index-data-in' type='checkbox'/><label for='index-f306d80f-eaf6-4cdb-b8ce-84f85f51b28c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-45c8227b-87c4-437e-bf70-6c9123ca24e2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45c8227b-87c4-437e-bf70-6c9123ca24e2' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 7
-    },
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "earthkit.plots.quickmap.plot(\n",
+    "    ds_pl[\"q\"].sel(level=1000).sel(time=\"2020-07-31T00:00:00\"),\n",
+    "    x=\"lon\", y=\"lat\",\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7731f334-8487-4256-ac41-8b693a504b35",
+   "metadata": {},
+   "source": [
+    "The following example will load GRIB files for pressure level data stored as **spherical harmonic coefficients (sh)**. Since this data requires remapping to an **O400 grid** for visualization, the remapping step is included.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5a574d92-3c0a-429b-97e8-d024d78c5daf",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "32f314be-e6dc-4a81-b12c-c740fcc043d9",
-      "cell_type": "markdown",
-      "source": "The data can be visualized as demonstrated below. For example, the temperature at **pressure level 1hPa** on **2020-07-31 at 00:00:00 UTC** is plotted.\n",
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_pl_sh_O400.grib\n"
+     ]
     },
     {
-      "id": "36e11d63-baec-4897-a5d1-26195cf5545f",
-      "cell_type": "code",
-      "source": "earthkit.plots.quickmap.plot(\n    ds_pl_O400[\"t\"].sel(level=1).sel(time=\"2020-07-31T00:00:00\"),\n    x=\"lon\", y=\"lat\",\n);",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 923.7MiB (from 793.9MiB) for this notebook\n",
-          "output_type": "stream"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 700x600 with 2 Axes>",
-            "image/png": 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+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 18GB\n",
+       "Dimensions:  (time: 21, level: 21, value: 654400)\n",
+       "Coordinates:\n",
+       "    lat      (value) float64 5MB ...\n",
+       "  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n",
+       "    lon      (value) float64 5MB ...\n",
+       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
+       "Dimensions without coordinates: value\n",
+       "Data variables:\n",
+       "    d        (time, level, value) float64 2GB ...\n",
+       "    r        (time, level, value) float64 2GB ...\n",
+       "    t        (time, level, value) float64 2GB ...\n",
+       "    u        (time, level, value) float64 2GB ...\n",
+       "    v        (time, level, value) float64 2GB ...\n",
+       "    vo       (time, level, value) float64 2GB ...\n",
+       "    w        (time, level, value) float64 2GB ...\n",
+       "    z        (time, level, value) float64 2GB ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4e8cf0f9-ac10-4de8-83bb-07808e533ee8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4e8cf0f9-ac10-4de8-83bb-07808e533ee8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span class='xr-has-index'>level</span>: 21</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-35b6a18d-2cec-4761-af34-3de25e69a74b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35b6a18d-2cec-4761-af34-3de25e69a74b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-82c0e70f-19a2-4a7c-a0a7-81e3af27c769' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82c0e70f-19a2-4a7c-a0a7-81e3af27c769' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb71df00-ba5e-4654-bf53-56f54c39148f' class='xr-var-data-in' type='checkbox'><label for='data-bb71df00-ba5e-4654-bf53-56f54c39148f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 500 700 850 925 1000</div><input id='attrs-585ef21b-f7ca-417a-a7d6-811da5045e35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-585ef21b-f7ca-417a-a7d6-811da5045e35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21edf3ce-16ff-45f3-9438-b5c92da107cf' class='xr-var-data-in' type='checkbox'><label for='data-21edf3ce-16ff-45f3-9438-b5c92da107cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n",
+       "        200,  250,  300,  400,  500,  700,  850,  925, 1000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-918e9901-c227-411f-9bf4-47b0a7ebfa6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-918e9901-c227-411f-9bf4-47b0a7ebfa6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2422212-d2c4-4dc2-8a74-f452dff1e90b' class='xr-var-data-in' type='checkbox'><label for='data-f2422212-d2c4-4dc2-8a74-f452dff1e90b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-e1d7eb9b-8cb2-4583-9a81-5185a3b010fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1d7eb9b-8cb2-4583-9a81-5185a3b010fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de19481b-dd0b-455a-9c38-1c33974d2796' class='xr-var-data-in' type='checkbox'><label for='data-de19481b-dd0b-455a-9c38-1c33974d2796' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4f925b9d-2efb-48ea-9ce9-f55b32e020db' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4f925b9d-2efb-48ea-9ce9-f55b32e020db' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>d</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-609760c9-c819-4d24-9b42-6006fd80ec4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-609760c9-c819-4d24-9b42-6006fd80ec4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3a5df56-abcd-4823-9629-c18712e03366' class='xr-var-data-in' type='checkbox'><label for='data-f3a5df56-abcd-4823-9629-c18712e03366' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>155</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>d</dd><dt><span>GRIB_units :</span></dt><dd>s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Divergence</dd><dt><span>GRIB_cfName :</span></dt><dd>divergence_of_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s**-1</dd><dt><span>long_name :</span></dt><dd>Divergence</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3d8af75b-b0ed-4c97-8df0-66a5040d6a30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d8af75b-b0ed-4c97-8df0-66a5040d6a30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c94b95c-6867-47be-9ad8-9fa3219e4f90' class='xr-var-data-in' type='checkbox'><label for='data-1c94b95c-6867-47be-9ad8-9fa3219e4f90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>157</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>r</dd><dt><span>GRIB_units :</span></dt><dd>%</dd><dt><span>GRIB_name :</span></dt><dd>Relative humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>relative_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>r</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Relative humidity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-37c24bfe-27db-4351-805d-0230628906d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37c24bfe-27db-4351-805d-0230628906d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b38a2b98-4464-40b3-8d6d-0fbaf71cc9ec' class='xr-var-data-in' type='checkbox'><label for='data-b38a2b98-4464-40b3-8d6d-0fbaf71cc9ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>130</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Temperature</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49d8e546-9b54-4846-bbef-5606ced2e7a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49d8e546-9b54-4846-bbef-5606ced2e7a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-949dd32e-92e8-40e1-8439-12697a82d88b' class='xr-var-data-in' type='checkbox'><label for='data-949dd32e-92e8-40e1-8439-12697a82d88b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>131</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>u</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>U component of wind</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>U component of wind</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-58369383-d9d3-4fb4-b13e-b1da2943a630' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58369383-d9d3-4fb4-b13e-b1da2943a630' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93de18dd-5e19-4d13-999d-e7816bbe3284' class='xr-var-data-in' type='checkbox'><label for='data-93de18dd-5e19-4d13-999d-e7816bbe3284' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>132</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>v</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>V component of wind</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>V component of wind</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vo</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b05ab3c-6fde-4903-9ffd-c6b747f83941' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b05ab3c-6fde-4903-9ffd-c6b747f83941' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77413552-0afa-44dc-8f42-958d656073f0' class='xr-var-data-in' type='checkbox'><label for='data-77413552-0afa-44dc-8f42-958d656073f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>138</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vo</dd><dt><span>GRIB_units :</span></dt><dd>s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Vorticity (relative)</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_relative_vorticity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vo</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s**-1</dd><dt><span>long_name :</span></dt><dd>Vorticity (relative)</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6051ead6-4a2b-463e-9658-63f979849af8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6051ead6-4a2b-463e-9658-63f979849af8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-382c0893-769b-4b79-afc2-8adc4ce4f13d' class='xr-var-data-in' type='checkbox'><label for='data-382c0893-769b-4b79-afc2-8adc4ce4f13d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>135</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>w</dd><dt><span>GRIB_units :</span></dt><dd>Pa s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Vertical velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd><dt><span>GRIB_cfVarName :</span></dt><dd>w</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 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1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 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888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Pa s**-1</dd><dt><span>long_name :</span></dt><dd>Vertical velocity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e991dac9-ad83-429c-8577-8e7f9dcfaed0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e991dac9-ad83-429c-8577-8e7f9dcfaed0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81490b7e-25c6-4958-b2bf-da2a0a818dde' class='xr-var-data-in' type='checkbox'><label for='data-81490b7e-25c6-4958-b2bf-da2a0a818dde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>long_name :</span></dt><dd>Geopotential</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a063e59e-2972-4360-a210-2b0868da8c83' class='xr-section-summary-in' type='checkbox'  ><label for='section-a063e59e-2972-4360-a210-2b0868da8c83' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-21b367d4-2904-4213-8d80-ce38d6e85f20' class='xr-index-data-in' type='checkbox'/><label for='index-21b367d4-2904-4213-8d80-ce38d6e85f20' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n",
+       "        200,  250,  300,  400,  500,  700,  850,  925, 1000],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f306d80f-eaf6-4cdb-b8ce-84f85f51b28c' class='xr-index-data-in' type='checkbox'/><label for='index-f306d80f-eaf6-4cdb-b8ce-84f85f51b28c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-31 00:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-45c8227b-87c4-437e-bf70-6c9123ca24e2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45c8227b-87c4-437e-bf70-6c9123ca24e2' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n",
+       "</dd></dl></div></li></ul></div></div>"
       ],
-      "execution_count": 8
-    },
+      "text/plain": [
+       "<xarray.Dataset> Size: 18GB\n",
+       "Dimensions:  (time: 21, level: 21, value: 654400)\n",
+       "Coordinates:\n",
+       "    lat      (value) float64 5MB ...\n",
+       "  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n",
+       "    lon      (value) float64 5MB ...\n",
+       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
+       "Dimensions without coordinates: value\n",
+       "Data variables:\n",
+       "    d        (time, level, value) float64 2GB ...\n",
+       "    r        (time, level, value) float64 2GB ...\n",
+       "    t        (time, level, value) float64 2GB ...\n",
+       "    u        (time, level, value) float64 2GB ...\n",
+       "    v        (time, level, value) float64 2GB ...\n",
+       "    vo       (time, level, value) float64 2GB ...\n",
+       "    w        (time, level, value) float64 2GB ...\n",
+       "    z        (time, level, value) float64 2GB ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc..."
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_pl_O400 = load_hplp_data(leveltype=\"pl\", gridtype=\"sh\", remap=True)\n",
+    "ds_pl_O400"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32f314be-e6dc-4a81-b12c-c740fcc043d9",
+   "metadata": {},
+   "source": [
+    "The data can be visualized as demonstrated below. For example, the temperature at **pressure level 1hPa** on **2020-07-31 at 00:00:00 UTC** is plotted.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "36e11d63-baec-4897-a5d1-26195cf5545f",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "92cc1ccd-bb9b-48f2-81ab-9641c75d84ec",
-      "cell_type": "markdown",
-      "source": "The model level data stored on a **reduced_gg** grid is split into two files. Specifying a timestep of `240` ensures that the second part of the data is loaded, covering the time range from **2020-07-26 12:00:00 UTC** to **2020-07-31 00:00:00 UTC**.",
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyodide]: Memory usage has grown to 923.7MiB (from 793.9MiB) for this notebook\n"
+     ]
     },
     {
-      "id": "b287eccb-eb9b-47b1-95c2-7c66dc02b033",
-      "cell_type": "code",
-      "source": "ds_ml = load_hplp_data(leveltype=\"ml\", gridtype=\"reduced_gg\", step=240)\nds_ml",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_ml_reduced_gg_levels_132_240.grib\n",
-          "output_type": "stream"
-        },
-        {
-          "execution_count": 9,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "<xarray.Dataset> Size: 50GB\nDimensions:  (time: 10, level: 137, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 1kB 1 2 3 4 5 6 7 8 ... 131 132 133 134 135 136 137\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 80B 2020-07-26T12:00:00 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    cc       (time, level, value) float64 7GB ...\n    ciwc     (time, level, value) float64 7GB ...\n    clwc     (time, level, value) float64 7GB ...\n    crwc     (time, level, value) float64 7GB ...\n    cswc     (time, level, value) float64 7GB ...\n    o3       (time, level, value) float64 7GB ...\n    q        (time, level, value) float64 7GB ...\nAttributes:\n    GRIB_edition:            2\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 50GB\nDimensions:  (time: 10, level: 137, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 1kB 1 2 3 4 5 6 7 8 ... 131 132 133 134 135 136 137\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 80B 2020-07-26T12:00:00 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    cc       (time, level, value) float64 7GB ...\n    ciwc     (time, level, value) float64 7GB ...\n    clwc     (time, level, value) float64 7GB ...\n    crwc     (time, level, value) float64 7GB ...\n    cswc     (time, level, value) float64 7GB ...\n    o3       (time, level, value) float64 7GB ...\n    q        (time, level, value) float64 7GB ...\nAttributes:\n    GRIB_edition:            2\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7c5f2836-fb72-4671-8555-e31df1cc7786' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c5f2836-fb72-4671-8555-e31df1cc7786' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10</li><li><span class='xr-has-index'>level</span>: 137</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6406049d-7491-4d8c-9ecf-4610480141da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6406049d-7491-4d8c-9ecf-4610480141da' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5e394cf0-ac79-429e-9042-08759f5e2f17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e394cf0-ac79-429e-9042-08759f5e2f17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfb8a6ac-fed6-4633-898b-5be2ace9d7d4' class='xr-var-data-in' type='checkbox'><label for='data-cfb8a6ac-fed6-4633-898b-5be2ace9d7d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 133 134 135 136 137</div><input id='attrs-3c11a5fe-48e5-4941-a589-90276fdedb41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c11a5fe-48e5-4941-a589-90276fdedb41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b7aa516-3779-4332-94d6-53dad1348ff7' class='xr-var-data-in' type='checkbox'><label for='data-9b7aa516-3779-4332-94d6-53dad1348ff7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,\n        29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,\n        43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,\n        57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,\n        71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,\n        85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,\n        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,\n       113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,\n       127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a95c2422-f0d9-4f72-a69e-16cae239b7d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a95c2422-f0d9-4f72-a69e-16cae239b7d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b715b39d-f15e-457c-a009-04b5a0c789db' class='xr-var-data-in' type='checkbox'><label for='data-b715b39d-f15e-457c-a009-04b5a0c789db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-26T12:00:00 ... 2020-07-31</div><input id='attrs-687092df-e04e-4f27-a6ce-5a44368fed88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-687092df-e04e-4f27-a6ce-5a44368fed88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf0be958-532d-4f58-8d0d-1a4a78fa8ed1' class='xr-var-data-in' type='checkbox'><label for='data-cf0be958-532d-4f58-8d0d-1a4a78fa8ed1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-26T12:00:00.000000000&#x27;, &#x27;2020-07-27T00:00:00.000000000&#x27;,\n       &#x27;2020-07-27T12:00:00.000000000&#x27;, &#x27;2020-07-28T00:00:00.000000000&#x27;,\n       &#x27;2020-07-28T12:00:00.000000000&#x27;, &#x27;2020-07-29T00:00:00.000000000&#x27;,\n       &#x27;2020-07-29T12:00:00.000000000&#x27;, &#x27;2020-07-30T00:00:00.000000000&#x27;,\n       &#x27;2020-07-30T12:00:00.000000000&#x27;, &#x27;2020-07-31T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d055675e-1231-40e8-9ac1-7b03c27b26bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d055675e-1231-40e8-9ac1-7b03c27b26bf' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3d83118-1b4b-4b61-b87f-cb18f6171aa5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3d83118-1b4b-4b61-b87f-cb18f6171aa5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ccffce3-cd0d-47f9-b4f8-6f0414f8ddc5' class='xr-var-data-in' type='checkbox'><label for='data-7ccffce3-cd0d-47f9-b4f8-6f0414f8ddc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>248</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Fraction of cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Fraction of cloud cover</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ciwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bbda85ea-5e28-41dd-93b5-6c12d7187074' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbda85ea-5e28-41dd-93b5-6c12d7187074' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd791ba0-94eb-4a10-bbc8-919505033502' class='xr-var-data-in' type='checkbox'><label for='data-cd791ba0-94eb-4a10-bbc8-919505033502' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>247</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ciwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific cloud ice water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ciwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific cloud ice water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1684b8ea-96dc-4868-8a18-2e934dd87ea0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1684b8ea-96dc-4868-8a18-2e934dd87ea0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa5daa39-ebed-4076-97fb-fafb082dd6d1' class='xr-var-data-in' type='checkbox'><label for='data-fa5daa39-ebed-4076-97fb-fafb082dd6d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>246</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>clwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific cloud liquid water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>clwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific cloud liquid water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ce2c19b-1b4d-4fa2-9f01-3ae40ee44448' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ce2c19b-1b4d-4fa2-9f01-3ae40ee44448' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff1ef3b2-b72a-46d6-b629-9c03a284e300' class='xr-var-data-in' type='checkbox'><label for='data-ff1ef3b2-b72a-46d6-b629-9c03a284e300' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>75</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>crwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific rain water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>crwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific rain water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cswc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84f0a623-95d9-406a-a806-bf3efdc401ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84f0a623-95d9-406a-a806-bf3efdc401ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3cfa228-0d1e-41af-8002-52c108669bf2' class='xr-var-data-in' type='checkbox'><label for='data-c3cfa228-0d1e-41af-8002-52c108669bf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>76</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cswc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific snow water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cswc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific snow water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-840a23fe-0b7a-4a38-936d-5982815672a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-840a23fe-0b7a-4a38-936d-5982815672a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f99438c7-8863-4013-a5cb-95de50489d08' class='xr-var-data-in' type='checkbox'><label for='data-f99438c7-8863-4013-a5cb-95de50489d08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>203</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>o3</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Ozone mass mixing ratio</dd><dt><span>GRIB_cfName :</span></dt><dd>mass_fraction_of_ozone_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>o3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Ozone mass mixing ratio</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e8088310-d08a-48d1-9530-ab041cef70c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8088310-d08a-48d1-9530-ab041cef70c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33cfea28-3b60-4e9b-8968-2c841290a39f' class='xr-var-data-in' type='checkbox'><label for='data-33cfea28-3b60-4e9b-8968-2c841290a39f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>133</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>q</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>specific_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>q</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-016040a9-d7c9-4e24-be0f-8da030f4f4b5' class='xr-section-summary-in' type='checkbox'  ><label for='section-016040a9-d7c9-4e24-be0f-8da030f4f4b5' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fab40ce2-ab97-4b26-b32c-3266a224825f' class='xr-index-data-in' type='checkbox'/><label for='index-fab40ce2-ab97-4b26-b32c-3266a224825f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n       ...\n       128, 129, 130, 131, 132, 133, 134, 135, 136, 137],\n      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;, length=137))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f9e4ee9d-6b9c-452c-9ffb-25c2548dcbaa' class='xr-index-data-in' type='checkbox'/><label for='index-f9e4ee9d-6b9c-452c-9ffb-25c2548dcbaa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-26 12:00:00&#x27;, &#x27;2020-07-27 00:00:00&#x27;,\n               &#x27;2020-07-27 12:00:00&#x27;, &#x27;2020-07-28 00:00:00&#x27;,\n               &#x27;2020-07-28 12:00:00&#x27;, &#x27;2020-07-29 00:00:00&#x27;,\n               &#x27;2020-07-29 12:00:00&#x27;, &#x27;2020-07-30 00:00:00&#x27;,\n               &#x27;2020-07-30 12:00:00&#x27;, &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2ccc49fd-ad02-47fe-9608-081bc2e5f82e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ccc49fd-ad02-47fe-9608-081bc2e5f82e' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 9
-    },
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "earthkit.plots.quickmap.plot(\n",
+    "    ds_pl_O400[\"t\"].sel(level=1).sel(time=\"2020-07-31T00:00:00\"),\n",
+    "    x=\"lon\", y=\"lat\",\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92cc1ccd-bb9b-48f2-81ab-9641c75d84ec",
+   "metadata": {},
+   "source": [
+    "The model level data stored on a **reduced_gg** grid is split into two files. Specifying a timestep of `240` ensures that the second part of the data is loaded, covering the time range from **2020-07-26 12:00:00 UTC** to **2020-07-31 00:00:00 UTC**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b287eccb-eb9b-47b1-95c2-7c66dc02b033",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "1343d519-ec42-4fd3-88c9-8b3fea2bfc38",
-      "cell_type": "markdown",
-      "source": "The data can be visualized as demonstrated below. For example, again, the specific humidity at **level 137** on **2020-07-31 at 00:00:00 UTC** is plotted.",
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_ml_reduced_gg_levels_132_240.grib\n"
+     ]
     },
     {
-      "id": "0ad8e22f-66ea-4711-8607-e392815e4594",
-      "cell_type": "code",
-      "source": "earthkit.plots.quickmap.plot(\n    ds_ml[\"q\"].sel(level=137).sel(time=\"2020-07-31T00:00:00\"),\n    x=\"lon\", y=\"lat\",\n);",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 700x600 with 2 Axes>",
-            "image/png": 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"
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-          "metadata": {}
-        }
+     "data": {
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+       "<defs>\n",
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+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
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+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
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+       "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
+       "body[data-theme=dark],\n",
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+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 50GB\n",
+       "Dimensions:  (time: 10, level: 137, value: 654400)\n",
+       "Coordinates:\n",
+       "    lat      (value) float64 5MB ...\n",
+       "  * level    (level) int64 1kB 1 2 3 4 5 6 7 8 ... 131 132 133 134 135 136 137\n",
+       "    lon      (value) float64 5MB ...\n",
+       "  * time     (time) datetime64[ns] 80B 2020-07-26T12:00:00 ... 2020-07-31\n",
+       "Dimensions without coordinates: value\n",
+       "Data variables:\n",
+       "    cc       (time, level, value) float64 7GB ...\n",
+       "    ciwc     (time, level, value) float64 7GB ...\n",
+       "    clwc     (time, level, value) float64 7GB ...\n",
+       "    crwc     (time, level, value) float64 7GB ...\n",
+       "    cswc     (time, level, value) float64 7GB ...\n",
+       "    o3       (time, level, value) float64 7GB ...\n",
+       "    q        (time, level, value) float64 7GB ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            2\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7c5f2836-fb72-4671-8555-e31df1cc7786' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c5f2836-fb72-4671-8555-e31df1cc7786' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10</li><li><span class='xr-has-index'>level</span>: 137</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6406049d-7491-4d8c-9ecf-4610480141da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6406049d-7491-4d8c-9ecf-4610480141da' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5e394cf0-ac79-429e-9042-08759f5e2f17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e394cf0-ac79-429e-9042-08759f5e2f17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfb8a6ac-fed6-4633-898b-5be2ace9d7d4' class='xr-var-data-in' type='checkbox'><label for='data-cfb8a6ac-fed6-4633-898b-5be2ace9d7d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 133 134 135 136 137</div><input id='attrs-3c11a5fe-48e5-4941-a589-90276fdedb41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c11a5fe-48e5-4941-a589-90276fdedb41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b7aa516-3779-4332-94d6-53dad1348ff7' class='xr-var-data-in' type='checkbox'><label for='data-9b7aa516-3779-4332-94d6-53dad1348ff7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n",
+       "        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,\n",
+       "        29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,\n",
+       "        43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,\n",
+       "        57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,\n",
+       "        71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,\n",
+       "        85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,\n",
+       "        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,\n",
+       "       113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,\n",
+       "       127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a95c2422-f0d9-4f72-a69e-16cae239b7d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a95c2422-f0d9-4f72-a69e-16cae239b7d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b715b39d-f15e-457c-a009-04b5a0c789db' class='xr-var-data-in' type='checkbox'><label for='data-b715b39d-f15e-457c-a009-04b5a0c789db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-26T12:00:00 ... 2020-07-31</div><input id='attrs-687092df-e04e-4f27-a6ce-5a44368fed88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-687092df-e04e-4f27-a6ce-5a44368fed88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf0be958-532d-4f58-8d0d-1a4a78fa8ed1' class='xr-var-data-in' type='checkbox'><label for='data-cf0be958-532d-4f58-8d0d-1a4a78fa8ed1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-26T12:00:00.000000000&#x27;, &#x27;2020-07-27T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-27T12:00:00.000000000&#x27;, &#x27;2020-07-28T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-28T12:00:00.000000000&#x27;, &#x27;2020-07-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-29T12:00:00.000000000&#x27;, &#x27;2020-07-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-30T12:00:00.000000000&#x27;, &#x27;2020-07-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d055675e-1231-40e8-9ac1-7b03c27b26bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d055675e-1231-40e8-9ac1-7b03c27b26bf' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3d83118-1b4b-4b61-b87f-cb18f6171aa5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3d83118-1b4b-4b61-b87f-cb18f6171aa5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ccffce3-cd0d-47f9-b4f8-6f0414f8ddc5' class='xr-var-data-in' type='checkbox'><label for='data-7ccffce3-cd0d-47f9-b4f8-6f0414f8ddc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>248</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Fraction of cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Fraction of cloud cover</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ciwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bbda85ea-5e28-41dd-93b5-6c12d7187074' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbda85ea-5e28-41dd-93b5-6c12d7187074' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd791ba0-94eb-4a10-bbc8-919505033502' class='xr-var-data-in' type='checkbox'><label for='data-cd791ba0-94eb-4a10-bbc8-919505033502' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>247</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ciwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific cloud ice water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ciwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific cloud ice water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1684b8ea-96dc-4868-8a18-2e934dd87ea0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1684b8ea-96dc-4868-8a18-2e934dd87ea0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa5daa39-ebed-4076-97fb-fafb082dd6d1' class='xr-var-data-in' type='checkbox'><label for='data-fa5daa39-ebed-4076-97fb-fafb082dd6d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>246</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>clwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific cloud liquid water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>clwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific cloud liquid water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ce2c19b-1b4d-4fa2-9f01-3ae40ee44448' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ce2c19b-1b4d-4fa2-9f01-3ae40ee44448' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff1ef3b2-b72a-46d6-b629-9c03a284e300' class='xr-var-data-in' type='checkbox'><label for='data-ff1ef3b2-b72a-46d6-b629-9c03a284e300' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>75</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>crwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific rain water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>crwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific rain water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cswc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84f0a623-95d9-406a-a806-bf3efdc401ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84f0a623-95d9-406a-a806-bf3efdc401ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3cfa228-0d1e-41af-8002-52c108669bf2' class='xr-var-data-in' type='checkbox'><label for='data-c3cfa228-0d1e-41af-8002-52c108669bf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>76</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cswc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific snow water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cswc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific snow water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-840a23fe-0b7a-4a38-936d-5982815672a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-840a23fe-0b7a-4a38-936d-5982815672a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f99438c7-8863-4013-a5cb-95de50489d08' class='xr-var-data-in' type='checkbox'><label for='data-f99438c7-8863-4013-a5cb-95de50489d08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>203</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>o3</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Ozone mass mixing ratio</dd><dt><span>GRIB_cfName :</span></dt><dd>mass_fraction_of_ozone_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>o3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Ozone mass mixing ratio</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e8088310-d08a-48d1-9530-ab041cef70c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8088310-d08a-48d1-9530-ab041cef70c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33cfea28-3b60-4e9b-8968-2c841290a39f' class='xr-var-data-in' type='checkbox'><label for='data-33cfea28-3b60-4e9b-8968-2c841290a39f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>133</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>q</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>specific_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>q</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-016040a9-d7c9-4e24-be0f-8da030f4f4b5' class='xr-section-summary-in' type='checkbox'  ><label for='section-016040a9-d7c9-4e24-be0f-8da030f4f4b5' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fab40ce2-ab97-4b26-b32c-3266a224825f' class='xr-index-data-in' type='checkbox'/><label for='index-fab40ce2-ab97-4b26-b32c-3266a224825f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
+       "       ...\n",
+       "       128, 129, 130, 131, 132, 133, 134, 135, 136, 137],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;, length=137))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f9e4ee9d-6b9c-452c-9ffb-25c2548dcbaa' class='xr-index-data-in' type='checkbox'/><label for='index-f9e4ee9d-6b9c-452c-9ffb-25c2548dcbaa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-26 12:00:00&#x27;, &#x27;2020-07-27 00:00:00&#x27;,\n",
+       "               &#x27;2020-07-27 12:00:00&#x27;, &#x27;2020-07-28 00:00:00&#x27;,\n",
+       "               &#x27;2020-07-28 12:00:00&#x27;, &#x27;2020-07-29 00:00:00&#x27;,\n",
+       "               &#x27;2020-07-29 12:00:00&#x27;, &#x27;2020-07-30 00:00:00&#x27;,\n",
+       "               &#x27;2020-07-30 12:00:00&#x27;, &#x27;2020-07-31 00:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2ccc49fd-ad02-47fe-9608-081bc2e5f82e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ccc49fd-ad02-47fe-9608-081bc2e5f82e' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n",
+       "</dd></dl></div></li></ul></div></div>"
       ],
-      "execution_count": 10
-    },
-    {
-      "id": "0320963f-0d01-4ee8-9cd4-d2aa765ebac6",
-      "cell_type": "markdown",
-      "source": "The surface level data is stored on a **reduced_gg** grid and can be loaded as in the following example.",
-      "metadata": {}
-    },
+      "text/plain": [
+       "<xarray.Dataset> Size: 50GB\n",
+       "Dimensions:  (time: 10, level: 137, value: 654400)\n",
+       "Coordinates:\n",
+       "    lat      (value) float64 5MB ...\n",
+       "  * level    (level) int64 1kB 1 2 3 4 5 6 7 8 ... 131 132 133 134 135 136 137\n",
+       "    lon      (value) float64 5MB ...\n",
+       "  * time     (time) datetime64[ns] 80B 2020-07-26T12:00:00 ... 2020-07-31\n",
+       "Dimensions without coordinates: value\n",
+       "Data variables:\n",
+       "    cc       (time, level, value) float64 7GB ...\n",
+       "    ciwc     (time, level, value) float64 7GB ...\n",
+       "    clwc     (time, level, value) float64 7GB ...\n",
+       "    crwc     (time, level, value) float64 7GB ...\n",
+       "    cswc     (time, level, value) float64 7GB ...\n",
+       "    o3       (time, level, value) float64 7GB ...\n",
+       "    q        (time, level, value) float64 7GB ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            2\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc..."
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_ml = load_hplp_data(leveltype=\"ml\", gridtype=\"reduced_gg\", step=240)\n",
+    "ds_ml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1343d519-ec42-4fd3-88c9-8b3fea2bfc38",
+   "metadata": {},
+   "source": [
+    "The data can be visualized as demonstrated below. For example, again, the specific humidity at **level 137** on **2020-07-31 at 00:00:00 UTC** is plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0ad8e22f-66ea-4711-8607-e392815e4594",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "989d5d36-6eb2-4bdc-804f-48cdc9deec9c",
-      "cell_type": "code",
-      "source": "ds_sfc = load_hplp_data(leveltype=\"sfc\", gridtype=\"reduced_gg\")\nds_sfc",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_sfc.grib\n",
-          "output_type": "stream"
-        },
-        {
-          "execution_count": 11,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "<xarray.Dataset> Size: 11GB\nDimensions:  (time: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables: (12/101)\n    10fg     (time, value) float64 110MB ...\n    10u      (time, value) float64 110MB ...\n    10v      (time, value) float64 110MB ...\n    2d       (time, value) float64 110MB ...\n    2t       (time, value) float64 110MB ...\n    asn      (time, value) float64 110MB ...\n    ...       ...\n    ttr      (time, value) float64 110MB ...\n    ttrc     (time, value) float64 110MB ...\n    uvb      (time, value) float64 110MB ...\n    vimd     (time, value) float64 110MB ...\n    vis      (time, value) float64 110MB ...\n    z        (time, value) float64 110MB ...\nAttributes:\n    GRIB_edition:            2\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 11GB\nDimensions:  (time: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables: (12/101)\n    10fg     (time, value) float64 110MB ...\n    10u      (time, value) float64 110MB ...\n    10v      (time, value) float64 110MB ...\n    2d       (time, value) float64 110MB ...\n    2t       (time, value) float64 110MB ...\n    asn      (time, value) float64 110MB ...\n    ...       ...\n    ttr      (time, value) float64 110MB ...\n    ttrc     (time, value) float64 110MB ...\n    uvb      (time, value) float64 110MB ...\n    vimd     (time, value) float64 110MB ...\n    vis      (time, value) float64 110MB ...\n    z        (time, value) float64 110MB ...\nAttributes:\n    GRIB_edition:            2\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d6e20773-0fbd-44d8-827c-0bb005c569b7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d6e20773-0fbd-44d8-827c-0bb005c569b7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1030ebe0-064e-4e70-a328-572a1e9948c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1030ebe0-064e-4e70-a328-572a1e9948c8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aae59fd7-117a-49ad-a9c8-93e13362cf4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aae59fd7-117a-49ad-a9c8-93e13362cf4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef154508-65a5-4f61-a6ac-2957b0e5490a' class='xr-var-data-in' type='checkbox'><label for='data-ef154508-65a5-4f61-a6ac-2957b0e5490a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf7b3ec2-5e8a-4576-9ec3-ae77e26d5b02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf7b3ec2-5e8a-4576-9ec3-ae77e26d5b02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-041984fa-47b9-4173-b5c0-12b64d089ae1' class='xr-var-data-in' type='checkbox'><label for='data-041984fa-47b9-4173-b5c0-12b64d089ae1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-95740a49-11e0-4b7e-adbd-337c03a5b8b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95740a49-11e0-4b7e-adbd-337c03a5b8b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82dbf6a3-d086-4d56-b9e4-06d3f3a48e49' class='xr-var-data-in' type='checkbox'><label for='data-82dbf6a3-d086-4d56-b9e4-06d3f3a48e49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf47ba93-7905-4a9e-8f3c-681e99ff936c' class='xr-section-summary-in' type='checkbox'  ><label for='section-cf47ba93-7905-4a9e-8f3c-681e99ff936c' class='xr-section-summary' >Data variables: <span>(101)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10fg</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4330fec5-039a-49c9-9ab5-63c7c979cd2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4330fec5-039a-49c9-9ab5-63c7c979cd2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-743351a8-3723-49a9-93e2-483a92d31547' class='xr-var-data-in' type='checkbox'><label for='data-743351a8-3723-49a9-93e2-483a92d31547' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>49</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10fg</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Maximum 10 metre wind gust since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fg10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Maximum 10 metre wind gust since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8ba373db-0f35-4c33-bdbe-8b591dcf6226' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ba373db-0f35-4c33-bdbe-8b591dcf6226' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68dab872-2b07-4a54-899c-f69318b7e38d' class='xr-var-data-in' type='checkbox'><label for='data-68dab872-2b07-4a54-899c-f69318b7e38d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>165</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10u</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre U wind component</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f17e00b-a8be-42c7-a74c-1a8de0e39fb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f17e00b-a8be-42c7-a74c-1a8de0e39fb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a3327d3-9045-4f43-84fc-0bff4d095609' class='xr-var-data-in' type='checkbox'><label for='data-6a3327d3-9045-4f43-84fc-0bff4d095609' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>166</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10v</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre V wind component</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a7973a04-4618-4024-9adf-812147ece4ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7973a04-4618-4024-9adf-812147ece4ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6538ad77-929e-4f80-90c0-99f419424f60' class='xr-var-data-in' type='checkbox'><label for='data-6538ad77-929e-4f80-90c0-99f419424f60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-21210fd6-e469-4adf-b6b0-2c8925433b86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21210fd6-e469-4adf-b6b0-2c8925433b86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e20733-d545-404c-a1d0-18921f9e995a' class='xr-var-data-in' type='checkbox'><label for='data-f3e20733-d545-404c-a1d0-18921f9e995a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>asn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5580b684-15ea-4412-83a3-d21edc956927' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5580b684-15ea-4412-83a3-d21edc956927' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00b1a9b8-c259-4d26-a93b-27cd46d51660' class='xr-var-data-in' type='checkbox'><label for='data-00b1a9b8-c259-4d26-a93b-27cd46d51660' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>32</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>asn</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Snow albedo</dd><dt><span>GRIB_cfVarName :</span></dt><dd>asn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 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464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Snow albedo</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bld</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e60e67c0-6694-4313-8259-b48dcfefe609' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e60e67c0-6694-4313-8259-b48dcfefe609' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92ff03ea-9476-413e-b2fc-fde42c102caf' class='xr-var-data-in' type='checkbox'><label for='data-92ff03ea-9476-413e-b2fc-fde42c102caf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>145</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>bld</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated boundary layer dissipation</dd><dt><span>GRIB_cfName :</span></dt><dd>kinetic_energy_dissipation_in_atmosphere_boundary_layer</dd><dt><span>GRIB_cfVarName :</span></dt><dd>bld</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 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1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated boundary layer dissipation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blh</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d1889f68-5666-4825-98a8-bdd54f52968e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1889f68-5666-4825-98a8-bdd54f52968e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c103f950-3607-44a5-b61f-b980a0ed83fc' class='xr-var-data-in' type='checkbox'><label for='data-c103f950-3607-44a5-b61f-b980a0ed83fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>159</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>blh</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Boundary layer height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>blh</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Boundary layer height</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cape</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5187718-a1cf-4be6-84cb-605911632026' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5187718-a1cf-4be6-84cb-605911632026' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bddae4a7-3576-4a35-aaf5-b02efa9da2ff' class='xr-var-data-in' type='checkbox'><label for='data-bddae4a7-3576-4a35-aaf5-b02efa9da2ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>59</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cape</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective available potential energy</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cape</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective available potential energy</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chnk</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b8e21707-ea42-48b2-8cf1-217543d78194' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8e21707-ea42-48b2-8cf1-217543d78194' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5892a35e-f0e7-45b5-bb82-9450662cc9c2' class='xr-var-data-in' type='checkbox'><label for='data-5892a35e-f0e7-45b5-bb82-9450662cc9c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>148</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>chnk</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Charnock</dd><dt><span>GRIB_cfVarName :</span></dt><dd>chnk</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Charnock</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ci</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cc05fd9b-7706-4c46-803f-e2dbbb34e3e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc05fd9b-7706-4c46-803f-e2dbbb34e3e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87461e7a-35be-449c-ac8f-748514051088' class='xr-var-data-in' type='checkbox'><label for='data-87461e7a-35be-449c-ac8f-748514051088' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>31</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ci</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>GRIB_cfName :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>siconc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cin</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7912ac99-2f5e-4878-bc76-bd6f567e3cd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7912ac99-2f5e-4878-bc76-bd6f567e3cd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2a39c65-5841-4202-8e6d-c478a3adc277' class='xr-var-data-in' type='checkbox'><label for='data-a2a39c65-5841-4202-8e6d-c478a3adc277' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228001</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cin</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective inhibition</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cin</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective inhibition</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-85a8e3af-e2a8-4637-8379-ecc4b732f606' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85a8e3af-e2a8-4637-8379-ecc4b732f606' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53848e1c-2276-4306-9ed2-98887110da2d' class='xr-var-data-in' type='checkbox'><label for='data-53848e1c-2276-4306-9ed2-98887110da2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>143</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Convective precipitation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Convective precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0dface96-9d7a-4361-b62d-4660d84f7893' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dface96-9d7a-4361-b62d-4660d84f7893' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c55d404-5586-4c01-aee5-f7bd5b408daf' class='xr-var-data-in' type='checkbox'><label for='data-7c55d404-5586-4c01-aee5-f7bd5b408daf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228218</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>crr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective rain rate</dd><dt><span>GRIB_cfVarName :</span></dt><dd>crr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Convective rain rate</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>csfr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-38b23da5-6b3a-4fac-9c6f-04071f910631' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38b23da5-6b3a-4fac-9c6f-04071f910631' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba7c7c02-9603-40f3-9556-ba88bd514ba3' class='xr-var-data-in' type='checkbox'><label for='data-ba7c7c02-9603-40f3-9556-ba88bd514ba3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228220</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>csfr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective snowfall rate water equivalent</dd><dt><span>GRIB_cfVarName :</span></dt><dd>csfr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Convective snowfall rate water equivalent</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dsrp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b3473e5b-566d-463e-8f83-b5c780f88f28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3473e5b-566d-463e-8f83-b5c780f88f28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85f4fe30-a443-4aa1-9e35-30cd72440df5' class='xr-var-data-in' type='checkbox'><label for='data-85f4fe30-a443-4aa1-9e35-30cd72440df5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>47</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>dsrp</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface direct normal short-wave (solar) radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dsrp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface direct normal short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5bf9b557-7b28-40b1-8b21-5ebc0d7cae12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bf9b557-7b28-40b1-8b21-5ebc0d7cae12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43ee563b-2355-402c-b418-246f90796540' class='xr-var-data-in' type='checkbox'><label for='data-43ee563b-2355-402c-b418-246f90796540' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>182</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>e</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Evaporation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_water_evaporation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>e</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b3fbf1fd-a9d9-45c8-be51-5a9ac089cc3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3fbf1fd-a9d9-45c8-be51-5a9ac089cc3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88b0f3ca-89a5-4fc5-b7c7-5b2b6afbf7b7' class='xr-var-data-in' type='checkbox'><label for='data-88b0f3ca-89a5-4fc5-b7c7-5b2b6afbf7b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>44</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>es</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow evaporation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>es</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snow evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d146fdf8-4b7d-4cf6-b8af-0e3d182ca71c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d146fdf8-4b7d-4cf6-b8af-0e3d182ca71c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1937e3c4-d6b2-401b-b467-38d351ff3348' class='xr-var-data-in' type='checkbox'><label for='data-1937e3c4-d6b2-401b-b467-38d351ff3348' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>180</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ewss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated eastward turbulent surface stress</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downward_eastward_stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ewss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Time-integrated eastward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fal</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ffa74af9-cddd-4a8e-8d19-1c7200ddb745' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffa74af9-cddd-4a8e-8d19-1c7200ddb745' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc964044-8090-4c91-bc40-32558c310907' class='xr-var-data-in' type='checkbox'><label for='data-bc964044-8090-4c91-bc40-32558c310907' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>243</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fal</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Forecast albedo</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fal</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Forecast albedo</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0734c2e2-aa5a-4ecf-ac9a-b738df83f59e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0734c2e2-aa5a-4ecf-ac9a-b738df83f59e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94edd35e-2143-4792-ab8c-aed7cb858ae9' class='xr-var-data-in' type='checkbox'><label for='data-94edd35e-2143-4792-ab8c-aed7cb858ae9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>245</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>flsr</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Forecast logarithm of surface roughness for heat</dd><dt><span>GRIB_cfVarName :</span></dt><dd>flsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Forecast logarithm of surface roughness for heat</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d76e9437-06dc-4d0d-83bd-6f9261abc220' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d76e9437-06dc-4d0d-83bd-6f9261abc220' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe5f6e7b-276a-47ce-94ed-537254bd7811' class='xr-var-data-in' type='checkbox'><label for='data-fe5f6e7b-276a-47ce-94ed-537254bd7811' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>244</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fsr</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Forecast surface roughness</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Forecast surface roughness</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fzra</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dc51dff6-933b-48d6-b4b7-02d8e9dc65a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc51dff6-933b-48d6-b4b7-02d8e9dc65a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8040c01d-6ee2-453b-b6ac-0911caba14f3' class='xr-var-data-in' type='checkbox'><label for='data-8040c01d-6ee2-453b-b6ac-0911caba14f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228216</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fzra</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Accumulated freezing rain</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fzra</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Accumulated freezing rain</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gwd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e247a28d-e39e-44fb-b461-4e2dcd1d32b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e247a28d-e39e-44fb-b461-4e2dcd1d32b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eb0518c-4af1-4ace-bbb2-25ba3cf62c57' class='xr-var-data-in' type='checkbox'><label for='data-3eb0518c-4af1-4ace-bbb2-25ba3cf62c57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>197</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>gwd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Gravity wave dissipation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>gwd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Gravity wave dissipation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e48a4375-1a79-44cc-af3d-9f893f45bd64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e48a4375-1a79-44cc-af3d-9f893f45bd64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3955749f-9928-40fd-8b1a-b0eca96eddc5' class='xr-var-data-in' type='checkbox'><label for='data-3955749f-9928-40fd-8b1a-b0eca96eddc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>188</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>hcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>High cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>High cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>i10fg</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71f103af-ed59-4e51-b665-efb8b8700007' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71f103af-ed59-4e51-b665-efb8b8700007' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a432187-56b6-4594-b1ba-7981e6f7800c' class='xr-var-data-in' type='checkbox'><label for='data-8a432187-56b6-4594-b1ba-7981e6f7800c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228029</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>i10fg</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous 10 metre wind gust</dd><dt><span>GRIB_cfVarName :</span></dt><dd>i10fg</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Instantaneous 10 metre wind gust</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ie</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ea6be8e6-c414-4d26-9eed-a7b19ab60fba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea6be8e6-c414-4d26-9eed-a7b19ab60fba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3a1adc6-6f06-47d1-8b61-b5709c93a230' class='xr-var-data-in' type='checkbox'><label for='data-c3a1adc6-6f06-47d1-8b61-b5709c93a230' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>232</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ie</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous moisture flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ie</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Instantaneous moisture flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iews</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8545f0ca-8158-4424-b926-fbccf7257257' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8545f0ca-8158-4424-b926-fbccf7257257' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97902f2b-504e-467a-90bb-c0253770f3aa' class='xr-var-data-in' type='checkbox'><label for='data-97902f2b-504e-467a-90bb-c0253770f3aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>229</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>iews</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous eastward turbulent surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>iews</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous eastward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ilspf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-033f75f8-02ff-46e9-8ba1-6d78193aef72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-033f75f8-02ff-46e9-8ba1-6d78193aef72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cdd3ad4-8566-41ec-be5f-bacacfbe5144' class='xr-var-data-in' type='checkbox'><label for='data-6cdd3ad4-8566-41ec-be5f-bacacfbe5144' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228217</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ilspf</dd><dt><span>GRIB_units :</span></dt><dd>Proportion</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous large-scale precipitation fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ilspf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Proportion</dd><dt><span>long_name :</span></dt><dd>Instantaneous large-scale precipitation fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45a10835-9d31-49e0-b8a6-a622fee660cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45a10835-9d31-49e0-b8a6-a622fee660cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9f23c2d-f5b0-452d-89af-cf39e6c87315' class='xr-var-data-in' type='checkbox'><label for='data-d9f23c2d-f5b0-452d-89af-cf39e6c87315' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>230</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>inss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous northward turbulent surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>inss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous northward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ishf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f0773b4f-2753-4910-9697-5985da9ca053' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0773b4f-2753-4910-9697-5985da9ca053' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-123658a1-c4dd-4557-abbb-e16a6e880ac1' class='xr-var-data-in' type='checkbox'><label for='data-123658a1-c4dd-4557-abbb-e16a6e880ac1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>231</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ishf</dd><dt><span>GRIB_units :</span></dt><dd>W m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous surface sensible heat net flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ishf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>W m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous surface sensible heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-34e5e4dc-700d-4d77-b0f9-b5bd21dbcea8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34e5e4dc-700d-4d77-b0f9-b5bd21dbcea8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7015e57-24d5-4297-ae66-45159a40229a' class='xr-var-data-in' type='checkbox'><label for='data-f7015e57-24d5-4297-ae66-45159a40229a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>35</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl1</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 1</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d548bd7e-3c9a-4957-aa75-7c15c37fa9c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d548bd7e-3c9a-4957-aa75-7c15c37fa9c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c7a0ead-71f0-42e9-ad0a-db15e8efd596' class='xr-var-data-in' type='checkbox'><label for='data-6c7a0ead-71f0-42e9-ad0a-db15e8efd596' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>36</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl2</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-62603528-c6c8-43d4-b9e4-eae9f4bfda5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62603528-c6c8-43d4-b9e4-eae9f4bfda5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2326bf87-bd6d-4358-a272-c185155787b7' class='xr-var-data-in' type='checkbox'><label for='data-2326bf87-bd6d-4358-a272-c185155787b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>37</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl3</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0cb5fe80-76c6-4917-ac70-0d20c446728b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cb5fe80-76c6-4917-ac70-0d20c446728b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-047bc4d9-47e1-41b8-9b65-34560d7f12e8' class='xr-var-data-in' type='checkbox'><label for='data-047bc4d9-47e1-41b8-9b65-34560d7f12e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>38</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl4</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kx</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df63305e-268e-43f3-a933-ecce12c47d74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df63305e-268e-43f3-a933-ecce12c47d74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36d04360-7e0d-4cf6-893c-4c1ccc42b751' class='xr-var-data-in' type='checkbox'><label for='data-36d04360-7e0d-4cf6-893c-4c1ccc42b751' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260121</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>kx</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>K index</dd><dt><span>GRIB_cfVarName :</span></dt><dd>kx</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>K index</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-06171991-86f9-4328-a481-a4eb9be73352' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06171991-86f9-4328-a481-a4eb9be73352' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca9e8885-a25c-494d-b809-c8f17873a708' class='xr-var-data-in' type='checkbox'><label for='data-ca9e8885-a25c-494d-b809-c8f17873a708' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>186</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Low cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Low cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lgws</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-360cda36-93ff-43ff-978a-29c1bcd21066' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-360cda36-93ff-43ff-978a-29c1bcd21066' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e40087d6-fdec-4159-840b-e19e44293d84' class='xr-var-data-in' type='checkbox'><label for='data-e40087d6-fdec-4159-840b-e19e44293d84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>195</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lgws</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Eastward gravity wave surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lgws</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 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1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 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class='xr-var-name'><span>lsm</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0cf13f02-46f9-4260-944f-8b0cb49506f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cf13f02-46f9-4260-944f-8b0cb49506f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-860dd268-856d-4a2d-803a-bfa5d5f9192d' class='xr-var-data-in' type='checkbox'><label for='data-860dd268-856d-4a2d-803a-bfa5d5f9192d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>172</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsm</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Land-sea mask</dd><dt><span>GRIB_cfName :</span></dt><dd>land_binary_mask</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsm</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 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1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Land-sea mask</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8939a644-cfce-4cf7-8b39-32f6528eefac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8939a644-cfce-4cf7-8b39-32f6528eefac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-547fcf75-42e9-44f4-9971-052e6dbc3b1d' class='xr-var-data-in' type='checkbox'><label for='data-547fcf75-42e9-44f4-9971-052e6dbc3b1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>142</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Large-scale precipitation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_stratiform_precipitation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Large-scale precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lspf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d794c20d-7296-49a6-b493-0dbae2cf4c43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d794c20d-7296-49a6-b493-0dbae2cf4c43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6556e85a-fab4-4c12-a32f-8748bcfa78f3' class='xr-var-data-in' type='checkbox'><label for='data-6556e85a-fab4-4c12-a32f-8748bcfa78f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>50</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lspf</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Large-scale precipitation fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lspf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Large-scale precipitation fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsrr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4a3d8e58-70ab-4628-be6c-17356a808fb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a3d8e58-70ab-4628-be6c-17356a808fb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0efd73a-2178-497b-943a-999923ac056c' class='xr-var-data-in' type='checkbox'><label for='data-d0efd73a-2178-497b-943a-999923ac056c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228219</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsrr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Large scale rain rate</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsrr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Large scale rain rate</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lssfr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-391fb502-0585-4be3-bd04-4d3f274a1265' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-391fb502-0585-4be3-bd04-4d3f274a1265' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01b7ca5e-4205-412d-be1f-e89787f9890d' class='xr-var-data-in' type='checkbox'><label for='data-01b7ca5e-4205-412d-be1f-e89787f9890d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228221</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lssfr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Large scale snowfall rate water equivalent</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lssfr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Large scale snowfall rate water equivalent</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca8de65f-977a-4a13-aaac-d863abbfc463' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca8de65f-977a-4a13-aaac-d863abbfc463' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-286038eb-10f4-4cc9-a006-2a038b3dd5c2' class='xr-var-data-in' type='checkbox'><label for='data-286038eb-10f4-4cc9-a006-2a038b3dd5c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>187</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Medium cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Medium cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mgws</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3babd423-6077-43b8-844c-84d641b4a2d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3babd423-6077-43b8-844c-84d641b4a2d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ce0da29-c69e-4eb4-9396-bd0927db6b22' class='xr-var-data-in' type='checkbox'><label for='data-8ce0da29-c69e-4eb4-9396-bd0927db6b22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>196</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mgws</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Northward gravity wave surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mgws</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Northward gravity wave surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mn2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32cb8fbb-a6e2-4517-ba25-751911cb5824' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32cb8fbb-a6e2-4517-ba25-751911cb5824' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5bce66f-43eb-4dfd-96d4-52b4b04b863d' class='xr-var-data-in' type='checkbox'><label for='data-e5bce66f-43eb-4dfd-96d4-52b4b04b863d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>202</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mn2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mn2t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f48502e5-2432-4ca5-958a-6e805ef038d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f48502e5-2432-4ca5-958a-6e805ef038d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e49e8cd-4f2f-4914-bb85-98a03332648a' class='xr-var-data-in' type='checkbox'><label for='data-6e49e8cd-4f2f-4914-bb85-98a03332648a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>msl</dd><dt><span>GRIB_units :</span></dt><dd>Pa</dd><dt><span>GRIB_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>GRIB_cfName :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>GRIB_cfVarName :</span></dt><dd>msl</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>Mean sea level pressure</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mx2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ceb2d97f-9912-4bdc-a907-e3a981313aaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ceb2d97f-9912-4bdc-a907-e3a981313aaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-707f55c2-8a18-4dca-a1a4-b52174eee2fa' class='xr-var-data-in' type='checkbox'><label for='data-707f55c2-8a18-4dca-a1a4-b52174eee2fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>201</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mx2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mx2t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b957b58b-ae9d-4dd2-ac26-6c8dbc37b0e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b957b58b-ae9d-4dd2-ac26-6c8dbc37b0e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6775af3a-07ba-4916-9797-fa25f75a77fb' class='xr-var-data-in' type='checkbox'><label for='data-6775af3a-07ba-4916-9797-fa25f75a77fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>181</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>nsss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated northward turbulent surface stress</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downward_northward_stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>nsss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Time-integrated northward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocu</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-add3e0be-a7bd-4057-9c8c-5b758a92b9b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-add3e0be-a7bd-4057-9c8c-5b758a92b9b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8fbb419-5c00-4947-bbb0-ce9930cd0265' class='xr-var-data-in' type='checkbox'><label for='data-c8fbb419-5c00-4947-bbb0-ce9930cd0265' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151131</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ocu</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Eastward surface sea water velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>uoe</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Eastward surface sea water velocity</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocv</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45f410ba-d703-4df4-baa6-2b74e36eefc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45f410ba-d703-4df4-baa6-2b74e36eefc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5f2b829-12d6-485f-931b-c16524e4fefe' class='xr-var-data-in' type='checkbox'><label for='data-c5f2b829-12d6-485f-931b-c16524e4fefe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151132</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ocv</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Northward surface sea water velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>von</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Northward surface sea water velocity</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>par</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a6072bf6-5bdd-4a92-a5c3-dc8748356560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6072bf6-5bdd-4a92-a5c3-dc8748356560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34cd21a5-f6c5-4372-b605-bbf4e46a096a' class='xr-var-data-in' type='checkbox'><label for='data-34cd21a5-f6c5-4372-b605-bbf4e46a096a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>58</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>par</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Photosynthetically active radiation at the surface</dd><dt><span>GRIB_cfVarName :</span></dt><dd>par</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Photosynthetically active radiation at the surface</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pev</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cbd8ec4a-5e25-490a-b4d6-ced99b5da25e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbd8ec4a-5e25-490a-b4d6-ced99b5da25e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cccf4c5-4b95-434f-b734-a08d919a2f22' class='xr-var-data-in' type='checkbox'><label for='data-5cccf4c5-4b95-434f-b734-a08d919a2f22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228251</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>pev</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Potential evaporation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>pev</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Potential evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ptype</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3a7109bc-b747-481a-b7a0-3509e4abf34c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a7109bc-b747-481a-b7a0-3509e4abf34c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f4a61c0-f2bc-4487-86ea-d4f375577e74' class='xr-var-data-in' type='checkbox'><label for='data-0f4a61c0-f2bc-4487-86ea-d4f375577e74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260015</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ptype</dd><dt><span>GRIB_units :</span></dt><dd>(Code table 4.201)</dd><dt><span>GRIB_name :</span></dt><dd>Precipitation type</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ptype</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(Code table 4.201)</dd><dt><span>long_name :</span></dt><dd>Precipitation type</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a1db728f-9603-4862-9e0f-1d9b0e2e52d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1db728f-9603-4862-9e0f-1d9b0e2e52d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30f245bc-2445-4966-935d-94cc7b3c14f4' class='xr-var-data-in' type='checkbox'><label for='data-30f245bc-2445-4966-935d-94cc7b3c14f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>205</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-64083807-54fe-48f4-969a-d35da34bd114' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64083807-54fe-48f4-969a-d35da34bd114' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be432ccc-0bc6-4140-90a7-1b663c0cd0cc' class='xr-var-data-in' type='checkbox'><label for='data-be432ccc-0bc6-4140-90a7-1b663c0cd0cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>33</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>rsn</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Snow density</dd><dt><span>GRIB_cfVarName :</span></dt><dd>rsn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-3</dd><dt><span>long_name :</span></dt><dd>Snow density</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5adf497f-1771-4942-9057-c8f967715a67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5adf497f-1771-4942-9057-c8f967715a67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb8f85cf-4892-471f-88f3-40f5d50f1c9a' class='xr-var-data-in' type='checkbox'><label for='data-fb8f85cf-4892-471f-88f3-40f5d50f1c9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>141</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sd</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow depth</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snow depth</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-95450a9a-e9aa-499e-8a57-32e094827532' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95450a9a-e9aa-499e-8a57-32e094827532' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49ee2ac1-05ef-4002-8859-dd790c324333' class='xr-var-data-in' type='checkbox'><label for='data-49ee2ac1-05ef-4002-8859-dd790c324333' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>144</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sf</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snowfall</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snowfall</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6d09aa6c-72f6-4b88-a8c7-6a9e2d0d3a47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d09aa6c-72f6-4b88-a8c7-6a9e2d0d3a47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47782e4e-76e8-4c9d-b393-1b49caeb490d' class='xr-var-data-in' type='checkbox'><label for='data-47782e4e-76e8-4c9d-b393-1b49caeb490d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>235</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>skt</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Skin temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>skt</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Skin temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-38796003-83a3-4c28-be13-b6069acc0350' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38796003-83a3-4c28-be13-b6069acc0350' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29b69f46-fead-4b26-ba2d-0492a4ee2eca' class='xr-var-data-in' type='checkbox'><label for='data-29b69f46-fead-4b26-ba2d-0492a4ee2eca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>147</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>slhf</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated surface latent heat net flux</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_upward_latent_heat_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>slhf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated surface latent heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-34eb1b49-d2d8-4422-b4de-7041ce6c5988' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34eb1b49-d2d8-4422-b4de-7041ce6c5988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c722d396-6aba-47e6-9d95-138f3824aaec' class='xr-var-data-in' type='checkbox'><label for='data-c722d396-6aba-47e6-9d95-138f3824aaec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>45</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>smlt</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snowmelt</dd><dt><span>GRIB_cfVarName :</span></dt><dd>smlt</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snowmelt</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>src</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c897c184-6925-438c-b421-87ca79843173' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c897c184-6925-438c-b421-87ca79843173' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dade50ac-1837-48c9-b3b5-0de471aab10e' class='xr-var-data-in' type='checkbox'><label for='data-dade50ac-1837-48c9-b3b5-0de471aab10e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>198</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>src</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Skin reservoir content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>src</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Skin reservoir content</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bc63dae2-9779-4328-b296-df5f88f39c48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc63dae2-9779-4328-b296-df5f88f39c48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96e39ff4-4a3c-4388-a720-8005d075ecbe' class='xr-var-data-in' type='checkbox'><label for='data-96e39ff4-4a3c-4388-a720-8005d075ecbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>8</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Surface runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Surface runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fd886f92-7196-4a77-a051-d7ffb38d4767' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd886f92-7196-4a77-a051-d7ffb38d4767' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7eb73835-e10d-4ddc-9e82-b9ea12879a0f' class='xr-var-data-in' type='checkbox'><label for='data-7eb73835-e10d-4ddc-9e82-b9ea12879a0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>146</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sshf</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated surface sensible heat net flux</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_upward_sensible_heat_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sshf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated surface sensible heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-735c0747-262c-4c36-afca-2287fa7fe5d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-735c0747-262c-4c36-afca-2287fa7fe5d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7897ac5a-a61a-4596-9231-a0ae9bc5e6de' class='xr-var-data-in' type='checkbox'><label for='data-7897ac5a-a61a-4596-9231-a0ae9bc5e6de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>176</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net short-wave (solar) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6960824b-d6f2-465d-9d50-767594873ec2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6960824b-d6f2-465d-9d50-767594873ec2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebce7b91-a51a-4ed3-862e-d5f8224f1a89' class='xr-var-data-in' type='checkbox'><label for='data-ebce7b91-a51a-4ed3-862e-d5f8224f1a89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>210</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net short-wave (solar) radiation, clear sky</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net short-wave (solar) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cec36b8c-8fb9-4605-9d19-7f7e869151ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cec36b8c-8fb9-4605-9d19-7f7e869151ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cde63e2b-a5a6-4319-8cd0-95c0ccdd4a1b' class='xr-var-data-in' type='checkbox'><label for='data-cde63e2b-a5a6-4319-8cd0-95c0ccdd4a1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>169</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssrd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface short-wave (solar) radiation downwards</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downwelling_shortwave_flux_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssrd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface short-wave (solar) radiation downwards</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-19fc16e7-d9a4-4d3f-afb7-73ea5b6eedf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19fc16e7-d9a4-4d3f-afb7-73ea5b6eedf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ceb9db13-9c40-40cd-80c6-b6d85f483419' class='xr-var-data-in' type='checkbox'><label for='data-ceb9db13-9c40-40cd-80c6-b6d85f483419' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>9</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Sub-surface runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Sub-surface runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d7f786e-0372-4f18-a514-6dd8a6596af9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d7f786e-0372-4f18-a514-6dd8a6596af9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-229e7a74-972a-499b-980b-7d440de940e6' class='xr-var-data-in' type='checkbox'><label for='data-229e7a74-972a-499b-980b-7d440de940e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>34</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sst</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Sea surface temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sst</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Sea surface temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e6a98d73-d19d-46bd-b8e2-1cf985673d3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6a98d73-d19d-46bd-b8e2-1cf985673d3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41380e9c-b2bb-4fbd-b2d6-2796d5c9f773' class='xr-var-data-in' type='checkbox'><label for='data-41380e9c-b2bb-4fbd-b2d6-2796d5c9f773' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>139</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl1</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 1</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9f110e1-aaa5-4eae-9fd5-1e1c2cc03560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9f110e1-aaa5-4eae-9fd5-1e1c2cc03560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc7def63-368b-4439-b32a-f20fd5e6bd33' class='xr-var-data-in' type='checkbox'><label for='data-fc7def63-368b-4439-b32a-f20fd5e6bd33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>170</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl2</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e805f259-2203-4592-8e5d-36c0bcd27cb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e805f259-2203-4592-8e5d-36c0bcd27cb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc594351-9475-47e9-930f-7007da19d461' class='xr-var-data-in' type='checkbox'><label for='data-cc594351-9475-47e9-930f-7007da19d461' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>183</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl3</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5b58a18f-a517-4954-a542-d430618c7e1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b58a18f-a517-4954-a542-d430618c7e1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdb1d0e2-544f-4e3d-b605-1145db42096d' class='xr-var-data-in' type='checkbox'><label for='data-fdb1d0e2-544f-4e3d-b605-1145db42096d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>236</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl4</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2c3bec16-dc63-4736-a2cf-f8897a662c44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c3bec16-dc63-4736-a2cf-f8897a662c44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-172bf16f-a9b3-4b59-9348-a1b813adb162' class='xr-var-data-in' type='checkbox'><label for='data-172bf16f-a9b3-4b59-9348-a1b813adb162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>177</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>str</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net long-wave (thermal) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_upward_longwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>str</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net long-wave (thermal) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c02ae11c-d947-44e4-a811-1fdd24ca41e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c02ae11c-d947-44e4-a811-1fdd24ca41e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e8ed450-790e-483d-9715-3abd7e75b8d9' class='xr-var-data-in' type='checkbox'><label for='data-2e8ed450-790e-483d-9715-3abd7e75b8d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>211</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>strc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net long-wave (thermal) radiation, clear sky</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>strc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net long-wave (thermal) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9c02985a-8fdd-4203-b619-6b20349ca705' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c02985a-8fdd-4203-b619-6b20349ca705' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9168707-1a0b-4462-aa66-39308740a63e' class='xr-var-data-in' type='checkbox'><label for='data-b9168707-1a0b-4462-aa66-39308740a63e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>175</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>strd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface long-wave (thermal) radiation downwards</dd><dt><span>GRIB_cfVarName :</span></dt><dd>strd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface long-wave (thermal) radiation downwards</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sund</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-717648be-d3ec-4724-8f23-9c381bd9505f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-717648be-d3ec-4724-8f23-9c381bd9505f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5da470d8-2a21-4ca6-b1dd-b8403df2a8cc' class='xr-var-data-in' type='checkbox'><label for='data-5da470d8-2a21-4ca6-b1dd-b8403df2a8cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>189</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sund</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Sunshine duration</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sund</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Sunshine duration</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-625e31c8-6c8e-4a8b-84e2-4f977d2c3046' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-625e31c8-6c8e-4a8b-84e2-4f977d2c3046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c028ff6a-3e7b-4550-9311-67e3b866c30d' class='xr-var-data-in' type='checkbox'><label for='data-c028ff6a-3e7b-4550-9311-67e3b866c30d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>39</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl1</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-98ee7ab4-51e1-4c14-98ea-764dc6850d98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98ee7ab4-51e1-4c14-98ea-764dc6850d98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b810f896-1095-4e30-bf56-c7ce6f672845' class='xr-var-data-in' type='checkbox'><label for='data-b810f896-1095-4e30-bf56-c7ce6f672845' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>40</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl2</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-89930f05-7b73-4084-af5a-2677b3272dbc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89930f05-7b73-4084-af5a-2677b3272dbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b79ce420-09ae-49cc-85be-0a668358401f' class='xr-var-data-in' type='checkbox'><label for='data-b79ce420-09ae-49cc-85be-0a668358401f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>41</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl3</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-489f6467-36b4-49d7-87d3-379d2f95b876' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-489f6467-36b4-49d7-87d3-379d2f95b876' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2c19650-d602-4fe5-a8af-1b235e94b28c' class='xr-var-data-in' type='checkbox'><label for='data-e2c19650-d602-4fe5-a8af-1b235e94b28c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>42</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl4</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d03152e-4416-425c-8b22-1ba25b5fcd2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d03152e-4416-425c-8b22-1ba25b5fcd2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b02eec91-48ac-4329-ac88-ca5936f50ae5' class='xr-var-data-in' type='checkbox'><label for='data-b02eec91-48ac-4329-ac88-ca5936f50ae5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>164</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Total cloud cover</dd><dt><span>GRIB_cfName :</span></dt><dd>cloud_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Total cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9849e3df-cead-43f5-a729-607062bc585b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9849e3df-cead-43f5-a729-607062bc585b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25a0449c-3f29-4ea8-99b9-f1c63f63e8cb' class='xr-var-data-in' type='checkbox'><label for='data-25a0449c-3f29-4ea8-99b9-f1c63f63e8cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>79</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tciw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud ice water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tciw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud ice water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-90c7ca71-c09a-4719-be3b-bc42913f7f1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90c7ca71-c09a-4719-be3b-bc42913f7f1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7956e51-dce2-4e2d-aa91-9e9caec5ad28' class='xr-var-data-in' type='checkbox'><label for='data-b7956e51-dce2-4e2d-aa91-9e9caec5ad28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>78</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tclw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud liquid water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tclw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud liquid water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tco3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-858fa190-6c5a-4719-ab61-881ae850555a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-858fa190-6c5a-4719-ab61-881ae850555a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a118f38-4eb7-40b9-8bcc-30e1fd7cba48' class='xr-var-data-in' type='checkbox'><label for='data-9a118f38-4eb7-40b9-8bcc-30e1fd7cba48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>206</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tco3</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column ozone</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_mass_content_of_ozone</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tco3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column ozone</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcrw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e6aaaa62-1814-4b52-b3a2-9a39075c1ad1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6aaaa62-1814-4b52-b3a2-9a39075c1ad1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af3fe057-9fc8-4984-a7b6-ba283db31f55' class='xr-var-data-in' type='checkbox'><label for='data-af3fe057-9fc8-4984-a7b6-ba283db31f55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228089</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcrw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column rain water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcrw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column rain water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcsw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-046599eb-177c-4c67-bd41-7e606b226ac1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-046599eb-177c-4c67-bd41-7e606b226ac1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e6777b-2e19-4de4-bb4a-915366c8a535' class='xr-var-data-in' type='checkbox'><label for='data-85e6777b-2e19-4de4-bb4a-915366c8a535' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228090</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcsw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column snow water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcsw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column snow water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5cdd0942-4cbb-432e-a9fd-b94acce7c23d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cdd0942-4cbb-432e-a9fd-b94acce7c23d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd76410d-5804-4901-8565-9b93fd621588' class='xr-var-data-in' type='checkbox'><label for='data-cd76410d-5804-4901-8565-9b93fd621588' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>136</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcwv</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-896640aa-d553-47a9-a340-2f4e5f894b86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-896640aa-d553-47a9-a340-2f4e5f894b86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99078f07-e996-45de-b4b7-694db69a22b5' class='xr-var-data-in' type='checkbox'><label for='data-99078f07-e996-45de-b4b7-694db69a22b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>137</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcwv</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column vertically-integrated water vapour</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_atmosphere_mass_content_of_water_vapor</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcwv</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column vertically-integrated water vapour</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1710dd02-1ab3-4cbb-a9a8-2526303a4269' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1710dd02-1ab3-4cbb-a9a8-2526303a4269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a3e5f6b-933f-4098-9453-3119cc9b2cfd' class='xr-var-data-in' type='checkbox'><label for='data-4a3e5f6b-933f-4098-9453-3119cc9b2cfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>212</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tisr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>TOA incident short-wave (solar) radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tisr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>TOA incident short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>totalx</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9faf3a86-5f41-4005-8a67-7d47d455bad0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9faf3a86-5f41-4005-8a67-7d47d455bad0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e16f0f25-d72c-45c5-b40e-727cce256d32' class='xr-var-data-in' type='checkbox'><label for='data-e16f0f25-d72c-45c5-b40e-727cce256d32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260123</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>totalx</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Total totals index</dd><dt><span>GRIB_cfVarName :</span></dt><dd>totalx</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Total totals index</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f1395563-36c6-4e9e-99c5-013725eb1b1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1395563-36c6-4e9e-99c5-013725eb1b1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44b6fd27-935e-47f2-816a-9edfae922fae' class='xr-var-data-in' type='checkbox'><label for='data-44b6fd27-935e-47f2-816a-9edfae922fae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Total precipitation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3032e4f2-d519-4fda-a1d0-7dfeb01e481a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3032e4f2-d519-4fda-a1d0-7dfeb01e481a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d091b4ab-7e4d-47d3-9654-589d6405f796' class='xr-var-data-in' type='checkbox'><label for='data-d091b4ab-7e4d-47d3-9654-589d6405f796' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>238</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsn</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Temperature of snow layer</dd><dt><span>GRIB_cfName :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature of snow layer</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c313d8ec-d82b-46f6-8e85-3f8bd09f6f35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c313d8ec-d82b-46f6-8e85-3f8bd09f6f35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5459ee50-9570-4944-9dc0-310726886dd5' class='xr-var-data-in' type='checkbox'><label for='data-5459ee50-9570-4944-9dc0-310726886dd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>178</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net short-wave (solar) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>toa_net_upward_shortwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a54d965e-64bc-4007-95ef-92b3bc2a3541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a54d965e-64bc-4007-95ef-92b3bc2a3541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e81f5cbf-053f-4577-bd5a-5275fa115fc5' class='xr-var-data-in' type='checkbox'><label for='data-e81f5cbf-053f-4577-bd5a-5275fa115fc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>208</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net short-wave (solar) radiation, clear sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net short-wave (solar) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9f6c42ec-0034-4f3b-b182-3327c35e1016' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f6c42ec-0034-4f3b-b182-3327c35e1016' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80959366-7b76-4db1-b0a0-7283b7015e24' class='xr-var-data-in' type='checkbox'><label for='data-80959366-7b76-4db1-b0a0-7283b7015e24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>179</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ttr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net long-wave (thermal) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>toa_outgoing_longwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ttr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 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1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net long-wave (thermal) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3346f4f5-a52f-4a4c-8a8c-85f48d0c34ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3346f4f5-a52f-4a4c-8a8c-85f48d0c34ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4ccd850-2b1d-43f4-a383-1f7b264108fe' class='xr-var-data-in' type='checkbox'><label for='data-b4ccd850-2b1d-43f4-a383-1f7b264108fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>209</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ttrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net long-wave (thermal) radiation, clear sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ttrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net long-wave (thermal) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uvb</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2c508b2d-66e2-4ddc-a782-bc7bc8fc118a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c508b2d-66e2-4ddc-a782-bc7bc8fc118a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb511a9a-ed46-4133-af29-799caf0c58c9' class='xr-var-data-in' type='checkbox'><label for='data-eb511a9a-ed46-4133-af29-799caf0c58c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>57</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>uvb</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface downward UV radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>uvb</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface downward UV radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vimd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4cbee88b-845c-466b-9276-4c817e0dea00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cbee88b-845c-466b-9276-4c817e0dea00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-372d688b-f36a-44b8-b2db-2aefe17ae79e' class='xr-var-data-in' type='checkbox'><label for='data-372d688b-f36a-44b8-b2db-2aefe17ae79e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>213</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vimd</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Vertically integrated moisture divergence</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vimd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Vertically integrated moisture divergence</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vis</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f3282000-ead3-4bd2-8a85-838d7eb1ce1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3282000-ead3-4bd2-8a85-838d7eb1ce1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-021397cf-b021-4e15-8b48-02b4750405c4' class='xr-var-data-in' type='checkbox'><label for='data-021397cf-b021-4e15-8b48-02b4750405c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>3020</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vis</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Visibility</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vis</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Visibility</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d8cd4b3-4c11-4fe4-975b-a90e261d77db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d8cd4b3-4c11-4fe4-975b-a90e261d77db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-974e217c-8e51-4c8e-9c08-28b74aa42511' class='xr-var-data-in' type='checkbox'><label for='data-974e217c-8e51-4c8e-9c08-28b74aa42511' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>long_name :</span></dt><dd>Geopotential</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-414662f6-2075-407a-8d32-d45147a33446' class='xr-section-summary-in' type='checkbox'  ><label for='section-414662f6-2075-407a-8d32-d45147a33446' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-879a5a36-beb2-4265-ada3-f6f0915d5b08' class='xr-index-data-in' type='checkbox'/><label for='index-879a5a36-beb2-4265-ada3-f6f0915d5b08' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-07373ff8-447c-4bb6-bd35-75b5e0c8cfc1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-07373ff8-447c-4bb6-bd35-75b5e0c8cfc1' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 11
-    },
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "earthkit.plots.quickmap.plot(\n",
+    "    ds_ml[\"q\"].sel(level=137).sel(time=\"2020-07-31T00:00:00\"),\n",
+    "    x=\"lon\", y=\"lat\",\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0320963f-0d01-4ee8-9cd4-d2aa765ebac6",
+   "metadata": {},
+   "source": [
+    "The surface level data is stored on a **reduced_gg** grid and can be loaded as in the following example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "989d5d36-6eb2-4bdc-804f-48cdc9deec9c",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "af588355-687f-43b1-a487-91a5f78e074b",
-      "cell_type": "markdown",
-      "source": "The data can be visualized as demonstrated below. For example, the 2 metre temperature in degrees Celsius on **2020-07-31 at 00:00:00 UTC** is plotted.",
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_sfc.grib\n"
+     ]
     },
     {
-      "id": "5638d36a-f0ca-442f-8879-36411fc61a75",
-      "cell_type": "code",
-      "source": "earthkit.plots.quickmap.plot(\n    ds_sfc[\"2t\"].sel(time=\"2020-07-31T00:00:00\"),\n    units=\"degC\", x=\"lon\", y=\"lat\",\n);",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 700x600 with 2 Axes>",
-            "image/png": 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+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 11GB\n",
+       "Dimensions:  (time: 21, value: 654400)\n",
+       "Coordinates:\n",
+       "    lat      (value) float64 5MB ...\n",
+       "    lon      (value) float64 5MB ...\n",
+       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
+       "Dimensions without coordinates: value\n",
+       "Data variables: (12/101)\n",
+       "    10fg     (time, value) float64 110MB ...\n",
+       "    10u      (time, value) float64 110MB ...\n",
+       "    10v      (time, value) float64 110MB ...\n",
+       "    2d       (time, value) float64 110MB ...\n",
+       "    2t       (time, value) float64 110MB ...\n",
+       "    asn      (time, value) float64 110MB ...\n",
+       "    ...       ...\n",
+       "    ttr      (time, value) float64 110MB ...\n",
+       "    ttrc     (time, value) float64 110MB ...\n",
+       "    uvb      (time, value) float64 110MB ...\n",
+       "    vimd     (time, value) float64 110MB ...\n",
+       "    vis      (time, value) float64 110MB ...\n",
+       "    z        (time, value) float64 110MB ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            2\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d6e20773-0fbd-44d8-827c-0bb005c569b7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d6e20773-0fbd-44d8-827c-0bb005c569b7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1030ebe0-064e-4e70-a328-572a1e9948c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1030ebe0-064e-4e70-a328-572a1e9948c8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aae59fd7-117a-49ad-a9c8-93e13362cf4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aae59fd7-117a-49ad-a9c8-93e13362cf4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef154508-65a5-4f61-a6ac-2957b0e5490a' class='xr-var-data-in' type='checkbox'><label for='data-ef154508-65a5-4f61-a6ac-2957b0e5490a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf7b3ec2-5e8a-4576-9ec3-ae77e26d5b02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf7b3ec2-5e8a-4576-9ec3-ae77e26d5b02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-041984fa-47b9-4173-b5c0-12b64d089ae1' class='xr-var-data-in' type='checkbox'><label for='data-041984fa-47b9-4173-b5c0-12b64d089ae1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-95740a49-11e0-4b7e-adbd-337c03a5b8b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95740a49-11e0-4b7e-adbd-337c03a5b8b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82dbf6a3-d086-4d56-b9e4-06d3f3a48e49' class='xr-var-data-in' type='checkbox'><label for='data-82dbf6a3-d086-4d56-b9e4-06d3f3a48e49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf47ba93-7905-4a9e-8f3c-681e99ff936c' class='xr-section-summary-in' type='checkbox'  ><label for='section-cf47ba93-7905-4a9e-8f3c-681e99ff936c' class='xr-section-summary' >Data variables: <span>(101)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10fg</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4330fec5-039a-49c9-9ab5-63c7c979cd2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4330fec5-039a-49c9-9ab5-63c7c979cd2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-743351a8-3723-49a9-93e2-483a92d31547' class='xr-var-data-in' type='checkbox'><label for='data-743351a8-3723-49a9-93e2-483a92d31547' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>49</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10fg</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Maximum 10 metre wind gust since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fg10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Maximum 10 metre wind gust since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8ba373db-0f35-4c33-bdbe-8b591dcf6226' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ba373db-0f35-4c33-bdbe-8b591dcf6226' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68dab872-2b07-4a54-899c-f69318b7e38d' class='xr-var-data-in' type='checkbox'><label for='data-68dab872-2b07-4a54-899c-f69318b7e38d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>165</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10u</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre U wind component</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f17e00b-a8be-42c7-a74c-1a8de0e39fb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f17e00b-a8be-42c7-a74c-1a8de0e39fb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a3327d3-9045-4f43-84fc-0bff4d095609' class='xr-var-data-in' type='checkbox'><label for='data-6a3327d3-9045-4f43-84fc-0bff4d095609' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>166</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10v</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre V wind component</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a7973a04-4618-4024-9adf-812147ece4ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7973a04-4618-4024-9adf-812147ece4ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6538ad77-929e-4f80-90c0-99f419424f60' class='xr-var-data-in' type='checkbox'><label for='data-6538ad77-929e-4f80-90c0-99f419424f60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-21210fd6-e469-4adf-b6b0-2c8925433b86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21210fd6-e469-4adf-b6b0-2c8925433b86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e20733-d545-404c-a1d0-18921f9e995a' class='xr-var-data-in' type='checkbox'><label for='data-f3e20733-d545-404c-a1d0-18921f9e995a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>asn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5580b684-15ea-4412-83a3-d21edc956927' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5580b684-15ea-4412-83a3-d21edc956927' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00b1a9b8-c259-4d26-a93b-27cd46d51660' class='xr-var-data-in' type='checkbox'><label for='data-00b1a9b8-c259-4d26-a93b-27cd46d51660' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>32</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>asn</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Snow albedo</dd><dt><span>GRIB_cfVarName :</span></dt><dd>asn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Snow albedo</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bld</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e60e67c0-6694-4313-8259-b48dcfefe609' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e60e67c0-6694-4313-8259-b48dcfefe609' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92ff03ea-9476-413e-b2fc-fde42c102caf' class='xr-var-data-in' type='checkbox'><label for='data-92ff03ea-9476-413e-b2fc-fde42c102caf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>145</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>bld</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated boundary layer dissipation</dd><dt><span>GRIB_cfName :</span></dt><dd>kinetic_energy_dissipation_in_atmosphere_boundary_layer</dd><dt><span>GRIB_cfVarName :</span></dt><dd>bld</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated boundary layer dissipation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blh</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d1889f68-5666-4825-98a8-bdd54f52968e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1889f68-5666-4825-98a8-bdd54f52968e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c103f950-3607-44a5-b61f-b980a0ed83fc' class='xr-var-data-in' type='checkbox'><label for='data-c103f950-3607-44a5-b61f-b980a0ed83fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>159</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>blh</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Boundary layer height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>blh</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Boundary layer height</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cape</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5187718-a1cf-4be6-84cb-605911632026' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5187718-a1cf-4be6-84cb-605911632026' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bddae4a7-3576-4a35-aaf5-b02efa9da2ff' class='xr-var-data-in' type='checkbox'><label for='data-bddae4a7-3576-4a35-aaf5-b02efa9da2ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>59</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cape</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective available potential energy</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cape</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective available potential energy</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chnk</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b8e21707-ea42-48b2-8cf1-217543d78194' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8e21707-ea42-48b2-8cf1-217543d78194' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5892a35e-f0e7-45b5-bb82-9450662cc9c2' class='xr-var-data-in' type='checkbox'><label for='data-5892a35e-f0e7-45b5-bb82-9450662cc9c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>148</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>chnk</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Charnock</dd><dt><span>GRIB_cfVarName :</span></dt><dd>chnk</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Charnock</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ci</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cc05fd9b-7706-4c46-803f-e2dbbb34e3e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc05fd9b-7706-4c46-803f-e2dbbb34e3e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87461e7a-35be-449c-ac8f-748514051088' class='xr-var-data-in' type='checkbox'><label for='data-87461e7a-35be-449c-ac8f-748514051088' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>31</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ci</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>GRIB_cfName :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>siconc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cin</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7912ac99-2f5e-4878-bc76-bd6f567e3cd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7912ac99-2f5e-4878-bc76-bd6f567e3cd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2a39c65-5841-4202-8e6d-c478a3adc277' class='xr-var-data-in' type='checkbox'><label for='data-a2a39c65-5841-4202-8e6d-c478a3adc277' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228001</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cin</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective inhibition</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cin</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective inhibition</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-85a8e3af-e2a8-4637-8379-ecc4b732f606' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85a8e3af-e2a8-4637-8379-ecc4b732f606' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53848e1c-2276-4306-9ed2-98887110da2d' class='xr-var-data-in' type='checkbox'><label for='data-53848e1c-2276-4306-9ed2-98887110da2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>143</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Convective precipitation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Convective precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0dface96-9d7a-4361-b62d-4660d84f7893' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dface96-9d7a-4361-b62d-4660d84f7893' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c55d404-5586-4c01-aee5-f7bd5b408daf' class='xr-var-data-in' type='checkbox'><label for='data-7c55d404-5586-4c01-aee5-f7bd5b408daf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228218</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>crr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective rain rate</dd><dt><span>GRIB_cfVarName :</span></dt><dd>crr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 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1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Convective rain rate</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>csfr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-38b23da5-6b3a-4fac-9c6f-04071f910631' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38b23da5-6b3a-4fac-9c6f-04071f910631' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba7c7c02-9603-40f3-9556-ba88bd514ba3' class='xr-var-data-in' type='checkbox'><label for='data-ba7c7c02-9603-40f3-9556-ba88bd514ba3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228220</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>csfr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective snowfall rate water equivalent</dd><dt><span>GRIB_cfVarName :</span></dt><dd>csfr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Convective snowfall rate water equivalent</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dsrp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b3473e5b-566d-463e-8f83-b5c780f88f28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3473e5b-566d-463e-8f83-b5c780f88f28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85f4fe30-a443-4aa1-9e35-30cd72440df5' class='xr-var-data-in' type='checkbox'><label for='data-85f4fe30-a443-4aa1-9e35-30cd72440df5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>47</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>dsrp</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface direct normal short-wave (solar) radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dsrp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface direct normal short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5bf9b557-7b28-40b1-8b21-5ebc0d7cae12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bf9b557-7b28-40b1-8b21-5ebc0d7cae12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43ee563b-2355-402c-b418-246f90796540' class='xr-var-data-in' type='checkbox'><label for='data-43ee563b-2355-402c-b418-246f90796540' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>182</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>e</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Evaporation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_water_evaporation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>e</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b3fbf1fd-a9d9-45c8-be51-5a9ac089cc3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3fbf1fd-a9d9-45c8-be51-5a9ac089cc3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88b0f3ca-89a5-4fc5-b7c7-5b2b6afbf7b7' class='xr-var-data-in' type='checkbox'><label for='data-88b0f3ca-89a5-4fc5-b7c7-5b2b6afbf7b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>44</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>es</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow evaporation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>es</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snow evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d146fdf8-4b7d-4cf6-b8af-0e3d182ca71c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d146fdf8-4b7d-4cf6-b8af-0e3d182ca71c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1937e3c4-d6b2-401b-b467-38d351ff3348' class='xr-var-data-in' type='checkbox'><label for='data-1937e3c4-d6b2-401b-b467-38d351ff3348' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>180</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ewss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated eastward turbulent surface stress</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downward_eastward_stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ewss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Time-integrated eastward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fal</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ffa74af9-cddd-4a8e-8d19-1c7200ddb745' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffa74af9-cddd-4a8e-8d19-1c7200ddb745' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc964044-8090-4c91-bc40-32558c310907' class='xr-var-data-in' type='checkbox'><label for='data-bc964044-8090-4c91-bc40-32558c310907' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>243</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fal</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Forecast albedo</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fal</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Forecast albedo</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0734c2e2-aa5a-4ecf-ac9a-b738df83f59e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0734c2e2-aa5a-4ecf-ac9a-b738df83f59e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94edd35e-2143-4792-ab8c-aed7cb858ae9' class='xr-var-data-in' type='checkbox'><label for='data-94edd35e-2143-4792-ab8c-aed7cb858ae9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>245</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>flsr</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Forecast logarithm of surface roughness for heat</dd><dt><span>GRIB_cfVarName :</span></dt><dd>flsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Forecast logarithm of surface roughness for heat</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d76e9437-06dc-4d0d-83bd-6f9261abc220' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d76e9437-06dc-4d0d-83bd-6f9261abc220' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe5f6e7b-276a-47ce-94ed-537254bd7811' class='xr-var-data-in' type='checkbox'><label for='data-fe5f6e7b-276a-47ce-94ed-537254bd7811' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>244</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fsr</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Forecast surface roughness</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Forecast surface roughness</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fzra</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dc51dff6-933b-48d6-b4b7-02d8e9dc65a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc51dff6-933b-48d6-b4b7-02d8e9dc65a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8040c01d-6ee2-453b-b6ac-0911caba14f3' class='xr-var-data-in' type='checkbox'><label for='data-8040c01d-6ee2-453b-b6ac-0911caba14f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228216</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fzra</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Accumulated freezing rain</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fzra</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Accumulated freezing rain</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gwd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e247a28d-e39e-44fb-b461-4e2dcd1d32b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e247a28d-e39e-44fb-b461-4e2dcd1d32b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eb0518c-4af1-4ace-bbb2-25ba3cf62c57' class='xr-var-data-in' type='checkbox'><label for='data-3eb0518c-4af1-4ace-bbb2-25ba3cf62c57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>197</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>gwd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Gravity wave dissipation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>gwd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Gravity wave dissipation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e48a4375-1a79-44cc-af3d-9f893f45bd64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e48a4375-1a79-44cc-af3d-9f893f45bd64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3955749f-9928-40fd-8b1a-b0eca96eddc5' class='xr-var-data-in' type='checkbox'><label for='data-3955749f-9928-40fd-8b1a-b0eca96eddc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>188</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>hcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>High cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>High cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>i10fg</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71f103af-ed59-4e51-b665-efb8b8700007' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71f103af-ed59-4e51-b665-efb8b8700007' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a432187-56b6-4594-b1ba-7981e6f7800c' class='xr-var-data-in' type='checkbox'><label for='data-8a432187-56b6-4594-b1ba-7981e6f7800c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228029</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>i10fg</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous 10 metre wind gust</dd><dt><span>GRIB_cfVarName :</span></dt><dd>i10fg</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Instantaneous 10 metre wind gust</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ie</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ea6be8e6-c414-4d26-9eed-a7b19ab60fba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea6be8e6-c414-4d26-9eed-a7b19ab60fba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3a1adc6-6f06-47d1-8b61-b5709c93a230' class='xr-var-data-in' type='checkbox'><label for='data-c3a1adc6-6f06-47d1-8b61-b5709c93a230' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>232</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ie</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous moisture flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ie</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Instantaneous moisture flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iews</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8545f0ca-8158-4424-b926-fbccf7257257' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8545f0ca-8158-4424-b926-fbccf7257257' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97902f2b-504e-467a-90bb-c0253770f3aa' class='xr-var-data-in' type='checkbox'><label for='data-97902f2b-504e-467a-90bb-c0253770f3aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>229</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>iews</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous eastward turbulent surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>iews</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous eastward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ilspf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-033f75f8-02ff-46e9-8ba1-6d78193aef72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-033f75f8-02ff-46e9-8ba1-6d78193aef72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cdd3ad4-8566-41ec-be5f-bacacfbe5144' class='xr-var-data-in' type='checkbox'><label for='data-6cdd3ad4-8566-41ec-be5f-bacacfbe5144' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228217</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ilspf</dd><dt><span>GRIB_units :</span></dt><dd>Proportion</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous large-scale precipitation fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ilspf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Proportion</dd><dt><span>long_name :</span></dt><dd>Instantaneous large-scale precipitation fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45a10835-9d31-49e0-b8a6-a622fee660cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45a10835-9d31-49e0-b8a6-a622fee660cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9f23c2d-f5b0-452d-89af-cf39e6c87315' class='xr-var-data-in' type='checkbox'><label for='data-d9f23c2d-f5b0-452d-89af-cf39e6c87315' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>230</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>inss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous northward turbulent surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>inss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous northward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ishf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f0773b4f-2753-4910-9697-5985da9ca053' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0773b4f-2753-4910-9697-5985da9ca053' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-123658a1-c4dd-4557-abbb-e16a6e880ac1' class='xr-var-data-in' type='checkbox'><label for='data-123658a1-c4dd-4557-abbb-e16a6e880ac1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>231</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ishf</dd><dt><span>GRIB_units :</span></dt><dd>W m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous surface sensible heat net flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ishf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>W m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous surface sensible heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-34e5e4dc-700d-4d77-b0f9-b5bd21dbcea8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34e5e4dc-700d-4d77-b0f9-b5bd21dbcea8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7015e57-24d5-4297-ae66-45159a40229a' class='xr-var-data-in' type='checkbox'><label for='data-f7015e57-24d5-4297-ae66-45159a40229a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>35</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl1</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 1</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d548bd7e-3c9a-4957-aa75-7c15c37fa9c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d548bd7e-3c9a-4957-aa75-7c15c37fa9c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c7a0ead-71f0-42e9-ad0a-db15e8efd596' class='xr-var-data-in' type='checkbox'><label for='data-6c7a0ead-71f0-42e9-ad0a-db15e8efd596' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>36</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl2</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-62603528-c6c8-43d4-b9e4-eae9f4bfda5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62603528-c6c8-43d4-b9e4-eae9f4bfda5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2326bf87-bd6d-4358-a272-c185155787b7' class='xr-var-data-in' type='checkbox'><label for='data-2326bf87-bd6d-4358-a272-c185155787b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>37</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl3</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0cb5fe80-76c6-4917-ac70-0d20c446728b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cb5fe80-76c6-4917-ac70-0d20c446728b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-047bc4d9-47e1-41b8-9b65-34560d7f12e8' class='xr-var-data-in' type='checkbox'><label for='data-047bc4d9-47e1-41b8-9b65-34560d7f12e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>38</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl4</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kx</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df63305e-268e-43f3-a933-ecce12c47d74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df63305e-268e-43f3-a933-ecce12c47d74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36d04360-7e0d-4cf6-893c-4c1ccc42b751' class='xr-var-data-in' type='checkbox'><label for='data-36d04360-7e0d-4cf6-893c-4c1ccc42b751' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260121</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>kx</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>K index</dd><dt><span>GRIB_cfVarName :</span></dt><dd>kx</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>K index</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-06171991-86f9-4328-a481-a4eb9be73352' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06171991-86f9-4328-a481-a4eb9be73352' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca9e8885-a25c-494d-b809-c8f17873a708' class='xr-var-data-in' type='checkbox'><label for='data-ca9e8885-a25c-494d-b809-c8f17873a708' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>186</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Low cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Low cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lgws</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-360cda36-93ff-43ff-978a-29c1bcd21066' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-360cda36-93ff-43ff-978a-29c1bcd21066' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e40087d6-fdec-4159-840b-e19e44293d84' class='xr-var-data-in' type='checkbox'><label for='data-e40087d6-fdec-4159-840b-e19e44293d84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>195</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lgws</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Eastward gravity wave surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lgws</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 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1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 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832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Eastward gravity wave surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsm</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0cf13f02-46f9-4260-944f-8b0cb49506f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cf13f02-46f9-4260-944f-8b0cb49506f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-860dd268-856d-4a2d-803a-bfa5d5f9192d' class='xr-var-data-in' type='checkbox'><label for='data-860dd268-856d-4a2d-803a-bfa5d5f9192d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>172</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsm</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Land-sea mask</dd><dt><span>GRIB_cfName :</span></dt><dd>land_binary_mask</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsm</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Land-sea mask</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8939a644-cfce-4cf7-8b39-32f6528eefac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8939a644-cfce-4cf7-8b39-32f6528eefac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-547fcf75-42e9-44f4-9971-052e6dbc3b1d' class='xr-var-data-in' type='checkbox'><label for='data-547fcf75-42e9-44f4-9971-052e6dbc3b1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>142</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Large-scale precipitation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_stratiform_precipitation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Large-scale precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lspf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d794c20d-7296-49a6-b493-0dbae2cf4c43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d794c20d-7296-49a6-b493-0dbae2cf4c43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6556e85a-fab4-4c12-a32f-8748bcfa78f3' class='xr-var-data-in' type='checkbox'><label for='data-6556e85a-fab4-4c12-a32f-8748bcfa78f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>50</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lspf</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Large-scale precipitation fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lspf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Large-scale precipitation fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsrr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4a3d8e58-70ab-4628-be6c-17356a808fb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a3d8e58-70ab-4628-be6c-17356a808fb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0efd73a-2178-497b-943a-999923ac056c' class='xr-var-data-in' type='checkbox'><label for='data-d0efd73a-2178-497b-943a-999923ac056c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228219</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsrr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Large scale rain rate</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsrr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Large scale rain rate</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lssfr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-391fb502-0585-4be3-bd04-4d3f274a1265' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-391fb502-0585-4be3-bd04-4d3f274a1265' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01b7ca5e-4205-412d-be1f-e89787f9890d' class='xr-var-data-in' type='checkbox'><label for='data-01b7ca5e-4205-412d-be1f-e89787f9890d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228221</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lssfr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Large scale snowfall rate water equivalent</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lssfr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Large scale snowfall rate water equivalent</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca8de65f-977a-4a13-aaac-d863abbfc463' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca8de65f-977a-4a13-aaac-d863abbfc463' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-286038eb-10f4-4cc9-a006-2a038b3dd5c2' class='xr-var-data-in' type='checkbox'><label for='data-286038eb-10f4-4cc9-a006-2a038b3dd5c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>187</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Medium cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Medium cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mgws</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3babd423-6077-43b8-844c-84d641b4a2d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3babd423-6077-43b8-844c-84d641b4a2d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ce0da29-c69e-4eb4-9396-bd0927db6b22' class='xr-var-data-in' type='checkbox'><label for='data-8ce0da29-c69e-4eb4-9396-bd0927db6b22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>196</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mgws</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Northward gravity wave surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mgws</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Northward gravity wave surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mn2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32cb8fbb-a6e2-4517-ba25-751911cb5824' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32cb8fbb-a6e2-4517-ba25-751911cb5824' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5bce66f-43eb-4dfd-96d4-52b4b04b863d' class='xr-var-data-in' type='checkbox'><label for='data-e5bce66f-43eb-4dfd-96d4-52b4b04b863d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>202</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mn2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mn2t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f48502e5-2432-4ca5-958a-6e805ef038d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f48502e5-2432-4ca5-958a-6e805ef038d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e49e8cd-4f2f-4914-bb85-98a03332648a' class='xr-var-data-in' type='checkbox'><label for='data-6e49e8cd-4f2f-4914-bb85-98a03332648a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>msl</dd><dt><span>GRIB_units :</span></dt><dd>Pa</dd><dt><span>GRIB_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>GRIB_cfName :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>GRIB_cfVarName :</span></dt><dd>msl</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>Mean sea level pressure</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mx2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ceb2d97f-9912-4bdc-a907-e3a981313aaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ceb2d97f-9912-4bdc-a907-e3a981313aaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-707f55c2-8a18-4dca-a1a4-b52174eee2fa' class='xr-var-data-in' type='checkbox'><label for='data-707f55c2-8a18-4dca-a1a4-b52174eee2fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>201</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mx2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mx2t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b957b58b-ae9d-4dd2-ac26-6c8dbc37b0e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b957b58b-ae9d-4dd2-ac26-6c8dbc37b0e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6775af3a-07ba-4916-9797-fa25f75a77fb' class='xr-var-data-in' type='checkbox'><label for='data-6775af3a-07ba-4916-9797-fa25f75a77fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>181</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>nsss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated northward turbulent surface stress</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downward_northward_stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>nsss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Time-integrated northward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocu</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-add3e0be-a7bd-4057-9c8c-5b758a92b9b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-add3e0be-a7bd-4057-9c8c-5b758a92b9b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8fbb419-5c00-4947-bbb0-ce9930cd0265' class='xr-var-data-in' type='checkbox'><label for='data-c8fbb419-5c00-4947-bbb0-ce9930cd0265' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151131</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ocu</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Eastward surface sea water velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>uoe</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Eastward surface sea water velocity</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocv</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45f410ba-d703-4df4-baa6-2b74e36eefc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45f410ba-d703-4df4-baa6-2b74e36eefc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5f2b829-12d6-485f-931b-c16524e4fefe' class='xr-var-data-in' type='checkbox'><label for='data-c5f2b829-12d6-485f-931b-c16524e4fefe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151132</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ocv</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Northward surface sea water velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>von</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Northward surface sea water velocity</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>par</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a6072bf6-5bdd-4a92-a5c3-dc8748356560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6072bf6-5bdd-4a92-a5c3-dc8748356560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34cd21a5-f6c5-4372-b605-bbf4e46a096a' class='xr-var-data-in' type='checkbox'><label for='data-34cd21a5-f6c5-4372-b605-bbf4e46a096a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>58</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>par</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Photosynthetically active radiation at the surface</dd><dt><span>GRIB_cfVarName :</span></dt><dd>par</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Photosynthetically active radiation at the surface</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pev</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cbd8ec4a-5e25-490a-b4d6-ced99b5da25e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbd8ec4a-5e25-490a-b4d6-ced99b5da25e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cccf4c5-4b95-434f-b734-a08d919a2f22' class='xr-var-data-in' type='checkbox'><label for='data-5cccf4c5-4b95-434f-b734-a08d919a2f22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228251</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>pev</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Potential evaporation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>pev</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Potential evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ptype</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3a7109bc-b747-481a-b7a0-3509e4abf34c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a7109bc-b747-481a-b7a0-3509e4abf34c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f4a61c0-f2bc-4487-86ea-d4f375577e74' class='xr-var-data-in' type='checkbox'><label for='data-0f4a61c0-f2bc-4487-86ea-d4f375577e74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260015</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ptype</dd><dt><span>GRIB_units :</span></dt><dd>(Code table 4.201)</dd><dt><span>GRIB_name :</span></dt><dd>Precipitation type</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ptype</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(Code table 4.201)</dd><dt><span>long_name :</span></dt><dd>Precipitation type</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a1db728f-9603-4862-9e0f-1d9b0e2e52d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1db728f-9603-4862-9e0f-1d9b0e2e52d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30f245bc-2445-4966-935d-94cc7b3c14f4' class='xr-var-data-in' type='checkbox'><label for='data-30f245bc-2445-4966-935d-94cc7b3c14f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>205</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-64083807-54fe-48f4-969a-d35da34bd114' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64083807-54fe-48f4-969a-d35da34bd114' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be432ccc-0bc6-4140-90a7-1b663c0cd0cc' class='xr-var-data-in' type='checkbox'><label for='data-be432ccc-0bc6-4140-90a7-1b663c0cd0cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>33</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>rsn</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Snow density</dd><dt><span>GRIB_cfVarName :</span></dt><dd>rsn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-3</dd><dt><span>long_name :</span></dt><dd>Snow density</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5adf497f-1771-4942-9057-c8f967715a67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5adf497f-1771-4942-9057-c8f967715a67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb8f85cf-4892-471f-88f3-40f5d50f1c9a' class='xr-var-data-in' type='checkbox'><label for='data-fb8f85cf-4892-471f-88f3-40f5d50f1c9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>141</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sd</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow depth</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snow depth</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-95450a9a-e9aa-499e-8a57-32e094827532' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95450a9a-e9aa-499e-8a57-32e094827532' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49ee2ac1-05ef-4002-8859-dd790c324333' class='xr-var-data-in' type='checkbox'><label for='data-49ee2ac1-05ef-4002-8859-dd790c324333' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>144</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sf</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snowfall</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snowfall</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6d09aa6c-72f6-4b88-a8c7-6a9e2d0d3a47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d09aa6c-72f6-4b88-a8c7-6a9e2d0d3a47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47782e4e-76e8-4c9d-b393-1b49caeb490d' class='xr-var-data-in' type='checkbox'><label for='data-47782e4e-76e8-4c9d-b393-1b49caeb490d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>235</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>skt</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Skin temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>skt</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Skin temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-38796003-83a3-4c28-be13-b6069acc0350' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38796003-83a3-4c28-be13-b6069acc0350' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29b69f46-fead-4b26-ba2d-0492a4ee2eca' class='xr-var-data-in' type='checkbox'><label for='data-29b69f46-fead-4b26-ba2d-0492a4ee2eca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>147</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>slhf</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated surface latent heat net flux</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_upward_latent_heat_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>slhf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated surface latent heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-34eb1b49-d2d8-4422-b4de-7041ce6c5988' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34eb1b49-d2d8-4422-b4de-7041ce6c5988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c722d396-6aba-47e6-9d95-138f3824aaec' class='xr-var-data-in' type='checkbox'><label for='data-c722d396-6aba-47e6-9d95-138f3824aaec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>45</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>smlt</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snowmelt</dd><dt><span>GRIB_cfVarName :</span></dt><dd>smlt</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snowmelt</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>src</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c897c184-6925-438c-b421-87ca79843173' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c897c184-6925-438c-b421-87ca79843173' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dade50ac-1837-48c9-b3b5-0de471aab10e' class='xr-var-data-in' type='checkbox'><label for='data-dade50ac-1837-48c9-b3b5-0de471aab10e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>198</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>src</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Skin reservoir content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>src</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Skin reservoir content</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bc63dae2-9779-4328-b296-df5f88f39c48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc63dae2-9779-4328-b296-df5f88f39c48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96e39ff4-4a3c-4388-a720-8005d075ecbe' class='xr-var-data-in' type='checkbox'><label for='data-96e39ff4-4a3c-4388-a720-8005d075ecbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>8</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Surface runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Surface runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fd886f92-7196-4a77-a051-d7ffb38d4767' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd886f92-7196-4a77-a051-d7ffb38d4767' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7eb73835-e10d-4ddc-9e82-b9ea12879a0f' class='xr-var-data-in' type='checkbox'><label for='data-7eb73835-e10d-4ddc-9e82-b9ea12879a0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>146</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sshf</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated surface sensible heat net flux</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_upward_sensible_heat_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sshf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated surface sensible heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-735c0747-262c-4c36-afca-2287fa7fe5d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-735c0747-262c-4c36-afca-2287fa7fe5d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7897ac5a-a61a-4596-9231-a0ae9bc5e6de' class='xr-var-data-in' type='checkbox'><label for='data-7897ac5a-a61a-4596-9231-a0ae9bc5e6de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>176</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net short-wave (solar) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6960824b-d6f2-465d-9d50-767594873ec2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6960824b-d6f2-465d-9d50-767594873ec2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebce7b91-a51a-4ed3-862e-d5f8224f1a89' class='xr-var-data-in' type='checkbox'><label for='data-ebce7b91-a51a-4ed3-862e-d5f8224f1a89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>210</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net short-wave (solar) radiation, clear sky</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net short-wave (solar) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cec36b8c-8fb9-4605-9d19-7f7e869151ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cec36b8c-8fb9-4605-9d19-7f7e869151ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cde63e2b-a5a6-4319-8cd0-95c0ccdd4a1b' class='xr-var-data-in' type='checkbox'><label for='data-cde63e2b-a5a6-4319-8cd0-95c0ccdd4a1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>169</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssrd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface short-wave (solar) radiation downwards</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downwelling_shortwave_flux_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssrd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 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968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 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916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface short-wave (solar) radiation downwards</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-19fc16e7-d9a4-4d3f-afb7-73ea5b6eedf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19fc16e7-d9a4-4d3f-afb7-73ea5b6eedf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ceb9db13-9c40-40cd-80c6-b6d85f483419' class='xr-var-data-in' type='checkbox'><label for='data-ceb9db13-9c40-40cd-80c6-b6d85f483419' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>9</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Sub-surface runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 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1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Sub-surface runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d7f786e-0372-4f18-a514-6dd8a6596af9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d7f786e-0372-4f18-a514-6dd8a6596af9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-229e7a74-972a-499b-980b-7d440de940e6' class='xr-var-data-in' type='checkbox'><label for='data-229e7a74-972a-499b-980b-7d440de940e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>34</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sst</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Sea surface temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sst</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Sea surface temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e6a98d73-d19d-46bd-b8e2-1cf985673d3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6a98d73-d19d-46bd-b8e2-1cf985673d3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41380e9c-b2bb-4fbd-b2d6-2796d5c9f773' class='xr-var-data-in' type='checkbox'><label for='data-41380e9c-b2bb-4fbd-b2d6-2796d5c9f773' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>139</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl1</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 1</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9f110e1-aaa5-4eae-9fd5-1e1c2cc03560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9f110e1-aaa5-4eae-9fd5-1e1c2cc03560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc7def63-368b-4439-b32a-f20fd5e6bd33' class='xr-var-data-in' type='checkbox'><label for='data-fc7def63-368b-4439-b32a-f20fd5e6bd33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>170</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl2</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e805f259-2203-4592-8e5d-36c0bcd27cb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e805f259-2203-4592-8e5d-36c0bcd27cb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc594351-9475-47e9-930f-7007da19d461' class='xr-var-data-in' type='checkbox'><label for='data-cc594351-9475-47e9-930f-7007da19d461' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>183</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl3</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5b58a18f-a517-4954-a542-d430618c7e1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b58a18f-a517-4954-a542-d430618c7e1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdb1d0e2-544f-4e3d-b605-1145db42096d' class='xr-var-data-in' type='checkbox'><label for='data-fdb1d0e2-544f-4e3d-b605-1145db42096d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>236</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl4</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2c3bec16-dc63-4736-a2cf-f8897a662c44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c3bec16-dc63-4736-a2cf-f8897a662c44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-172bf16f-a9b3-4b59-9348-a1b813adb162' class='xr-var-data-in' type='checkbox'><label for='data-172bf16f-a9b3-4b59-9348-a1b813adb162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>177</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>str</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net long-wave (thermal) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_upward_longwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>str</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net long-wave (thermal) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c02ae11c-d947-44e4-a811-1fdd24ca41e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c02ae11c-d947-44e4-a811-1fdd24ca41e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e8ed450-790e-483d-9715-3abd7e75b8d9' class='xr-var-data-in' type='checkbox'><label for='data-2e8ed450-790e-483d-9715-3abd7e75b8d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>211</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>strc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net long-wave (thermal) radiation, clear sky</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>strc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net long-wave (thermal) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9c02985a-8fdd-4203-b619-6b20349ca705' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c02985a-8fdd-4203-b619-6b20349ca705' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9168707-1a0b-4462-aa66-39308740a63e' class='xr-var-data-in' type='checkbox'><label for='data-b9168707-1a0b-4462-aa66-39308740a63e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>175</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>strd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface long-wave (thermal) radiation downwards</dd><dt><span>GRIB_cfVarName :</span></dt><dd>strd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface long-wave (thermal) radiation downwards</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sund</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-717648be-d3ec-4724-8f23-9c381bd9505f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-717648be-d3ec-4724-8f23-9c381bd9505f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5da470d8-2a21-4ca6-b1dd-b8403df2a8cc' class='xr-var-data-in' type='checkbox'><label for='data-5da470d8-2a21-4ca6-b1dd-b8403df2a8cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>189</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sund</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Sunshine duration</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sund</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Sunshine duration</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-625e31c8-6c8e-4a8b-84e2-4f977d2c3046' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-625e31c8-6c8e-4a8b-84e2-4f977d2c3046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c028ff6a-3e7b-4550-9311-67e3b866c30d' class='xr-var-data-in' type='checkbox'><label for='data-c028ff6a-3e7b-4550-9311-67e3b866c30d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>39</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl1</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-98ee7ab4-51e1-4c14-98ea-764dc6850d98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98ee7ab4-51e1-4c14-98ea-764dc6850d98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b810f896-1095-4e30-bf56-c7ce6f672845' class='xr-var-data-in' type='checkbox'><label for='data-b810f896-1095-4e30-bf56-c7ce6f672845' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>40</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl2</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-89930f05-7b73-4084-af5a-2677b3272dbc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89930f05-7b73-4084-af5a-2677b3272dbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b79ce420-09ae-49cc-85be-0a668358401f' class='xr-var-data-in' type='checkbox'><label for='data-b79ce420-09ae-49cc-85be-0a668358401f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>41</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl3</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-489f6467-36b4-49d7-87d3-379d2f95b876' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-489f6467-36b4-49d7-87d3-379d2f95b876' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2c19650-d602-4fe5-a8af-1b235e94b28c' class='xr-var-data-in' type='checkbox'><label for='data-e2c19650-d602-4fe5-a8af-1b235e94b28c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>42</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl4</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d03152e-4416-425c-8b22-1ba25b5fcd2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d03152e-4416-425c-8b22-1ba25b5fcd2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b02eec91-48ac-4329-ac88-ca5936f50ae5' class='xr-var-data-in' type='checkbox'><label for='data-b02eec91-48ac-4329-ac88-ca5936f50ae5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>164</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Total cloud cover</dd><dt><span>GRIB_cfName :</span></dt><dd>cloud_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Total cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9849e3df-cead-43f5-a729-607062bc585b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9849e3df-cead-43f5-a729-607062bc585b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25a0449c-3f29-4ea8-99b9-f1c63f63e8cb' class='xr-var-data-in' type='checkbox'><label for='data-25a0449c-3f29-4ea8-99b9-f1c63f63e8cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>79</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tciw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud ice water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tciw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud ice water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-90c7ca71-c09a-4719-be3b-bc42913f7f1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90c7ca71-c09a-4719-be3b-bc42913f7f1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7956e51-dce2-4e2d-aa91-9e9caec5ad28' class='xr-var-data-in' type='checkbox'><label for='data-b7956e51-dce2-4e2d-aa91-9e9caec5ad28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>78</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tclw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud liquid water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tclw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud liquid water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tco3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-858fa190-6c5a-4719-ab61-881ae850555a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-858fa190-6c5a-4719-ab61-881ae850555a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a118f38-4eb7-40b9-8bcc-30e1fd7cba48' class='xr-var-data-in' type='checkbox'><label for='data-9a118f38-4eb7-40b9-8bcc-30e1fd7cba48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>206</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tco3</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column ozone</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_mass_content_of_ozone</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tco3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column ozone</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcrw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e6aaaa62-1814-4b52-b3a2-9a39075c1ad1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6aaaa62-1814-4b52-b3a2-9a39075c1ad1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af3fe057-9fc8-4984-a7b6-ba283db31f55' class='xr-var-data-in' type='checkbox'><label for='data-af3fe057-9fc8-4984-a7b6-ba283db31f55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228089</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcrw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column rain water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcrw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column rain water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcsw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-046599eb-177c-4c67-bd41-7e606b226ac1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-046599eb-177c-4c67-bd41-7e606b226ac1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e6777b-2e19-4de4-bb4a-915366c8a535' class='xr-var-data-in' type='checkbox'><label for='data-85e6777b-2e19-4de4-bb4a-915366c8a535' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228090</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcsw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column snow water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcsw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column snow water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5cdd0942-4cbb-432e-a9fd-b94acce7c23d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cdd0942-4cbb-432e-a9fd-b94acce7c23d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd76410d-5804-4901-8565-9b93fd621588' class='xr-var-data-in' type='checkbox'><label for='data-cd76410d-5804-4901-8565-9b93fd621588' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>136</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcwv</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-896640aa-d553-47a9-a340-2f4e5f894b86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-896640aa-d553-47a9-a340-2f4e5f894b86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99078f07-e996-45de-b4b7-694db69a22b5' class='xr-var-data-in' type='checkbox'><label for='data-99078f07-e996-45de-b4b7-694db69a22b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>137</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcwv</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column vertically-integrated water vapour</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_atmosphere_mass_content_of_water_vapor</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcwv</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column vertically-integrated water vapour</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1710dd02-1ab3-4cbb-a9a8-2526303a4269' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1710dd02-1ab3-4cbb-a9a8-2526303a4269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a3e5f6b-933f-4098-9453-3119cc9b2cfd' class='xr-var-data-in' type='checkbox'><label for='data-4a3e5f6b-933f-4098-9453-3119cc9b2cfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>212</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tisr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>TOA incident short-wave (solar) radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tisr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>TOA incident short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>totalx</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9faf3a86-5f41-4005-8a67-7d47d455bad0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9faf3a86-5f41-4005-8a67-7d47d455bad0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e16f0f25-d72c-45c5-b40e-727cce256d32' class='xr-var-data-in' type='checkbox'><label for='data-e16f0f25-d72c-45c5-b40e-727cce256d32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260123</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>totalx</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Total totals index</dd><dt><span>GRIB_cfVarName :</span></dt><dd>totalx</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Total totals index</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f1395563-36c6-4e9e-99c5-013725eb1b1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1395563-36c6-4e9e-99c5-013725eb1b1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44b6fd27-935e-47f2-816a-9edfae922fae' class='xr-var-data-in' type='checkbox'><label for='data-44b6fd27-935e-47f2-816a-9edfae922fae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Total precipitation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3032e4f2-d519-4fda-a1d0-7dfeb01e481a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3032e4f2-d519-4fda-a1d0-7dfeb01e481a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d091b4ab-7e4d-47d3-9654-589d6405f796' class='xr-var-data-in' type='checkbox'><label for='data-d091b4ab-7e4d-47d3-9654-589d6405f796' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>238</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsn</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Temperature of snow layer</dd><dt><span>GRIB_cfName :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature of snow layer</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c313d8ec-d82b-46f6-8e85-3f8bd09f6f35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c313d8ec-d82b-46f6-8e85-3f8bd09f6f35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5459ee50-9570-4944-9dc0-310726886dd5' class='xr-var-data-in' type='checkbox'><label for='data-5459ee50-9570-4944-9dc0-310726886dd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>178</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net short-wave (solar) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>toa_net_upward_shortwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a54d965e-64bc-4007-95ef-92b3bc2a3541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a54d965e-64bc-4007-95ef-92b3bc2a3541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e81f5cbf-053f-4577-bd5a-5275fa115fc5' class='xr-var-data-in' type='checkbox'><label for='data-e81f5cbf-053f-4577-bd5a-5275fa115fc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>208</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net short-wave (solar) radiation, clear sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net short-wave (solar) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9f6c42ec-0034-4f3b-b182-3327c35e1016' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f6c42ec-0034-4f3b-b182-3327c35e1016' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80959366-7b76-4db1-b0a0-7283b7015e24' class='xr-var-data-in' type='checkbox'><label for='data-80959366-7b76-4db1-b0a0-7283b7015e24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>179</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ttr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net long-wave (thermal) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>toa_outgoing_longwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ttr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net long-wave (thermal) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3346f4f5-a52f-4a4c-8a8c-85f48d0c34ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3346f4f5-a52f-4a4c-8a8c-85f48d0c34ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4ccd850-2b1d-43f4-a383-1f7b264108fe' class='xr-var-data-in' type='checkbox'><label for='data-b4ccd850-2b1d-43f4-a383-1f7b264108fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>209</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ttrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net long-wave (thermal) radiation, clear sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ttrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net long-wave (thermal) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uvb</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2c508b2d-66e2-4ddc-a782-bc7bc8fc118a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c508b2d-66e2-4ddc-a782-bc7bc8fc118a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb511a9a-ed46-4133-af29-799caf0c58c9' class='xr-var-data-in' type='checkbox'><label for='data-eb511a9a-ed46-4133-af29-799caf0c58c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>57</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>uvb</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface downward UV radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>uvb</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface downward UV radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vimd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4cbee88b-845c-466b-9276-4c817e0dea00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cbee88b-845c-466b-9276-4c817e0dea00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-372d688b-f36a-44b8-b2db-2aefe17ae79e' class='xr-var-data-in' type='checkbox'><label for='data-372d688b-f36a-44b8-b2db-2aefe17ae79e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>213</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vimd</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Vertically integrated moisture divergence</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vimd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Vertically integrated moisture divergence</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vis</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f3282000-ead3-4bd2-8a85-838d7eb1ce1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3282000-ead3-4bd2-8a85-838d7eb1ce1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-021397cf-b021-4e15-8b48-02b4750405c4' class='xr-var-data-in' type='checkbox'><label for='data-021397cf-b021-4e15-8b48-02b4750405c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>3020</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vis</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Visibility</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vis</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Visibility</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d8cd4b3-4c11-4fe4-975b-a90e261d77db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d8cd4b3-4c11-4fe4-975b-a90e261d77db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-974e217c-8e51-4c8e-9c08-28b74aa42511' class='xr-var-data-in' type='checkbox'><label for='data-974e217c-8e51-4c8e-9c08-28b74aa42511' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 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552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>long_name :</span></dt><dd>Geopotential</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-414662f6-2075-407a-8d32-d45147a33446' class='xr-section-summary-in' type='checkbox'  ><label for='section-414662f6-2075-407a-8d32-d45147a33446' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-879a5a36-beb2-4265-ada3-f6f0915d5b08' class='xr-index-data-in' type='checkbox'/><label for='index-879a5a36-beb2-4265-ada3-f6f0915d5b08' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-31 00:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-07373ff8-447c-4bb6-bd35-75b5e0c8cfc1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-07373ff8-447c-4bb6-bd35-75b5e0c8cfc1' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n",
+       "</dd></dl></div></li></ul></div></div>"
       ],
-      "execution_count": 12
-    },
-    {
-      "id": "658ac441-3d9d-46ab-9e5a-d4c5a159679b",
-      "cell_type": "markdown",
-      "source": "The wave model data is stored on a **reduced_ll** grid and can be loaded as in the following example.\n",
-      "metadata": {}
-    },
+      "text/plain": [
+       "<xarray.Dataset> Size: 11GB\n",
+       "Dimensions:  (time: 21, value: 654400)\n",
+       "Coordinates:\n",
+       "    lat      (value) float64 5MB ...\n",
+       "    lon      (value) float64 5MB ...\n",
+       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
+       "Dimensions without coordinates: value\n",
+       "Data variables: (12/101)\n",
+       "    10fg     (time, value) float64 110MB ...\n",
+       "    10u      (time, value) float64 110MB ...\n",
+       "    10v      (time, value) float64 110MB ...\n",
+       "    2d       (time, value) float64 110MB ...\n",
+       "    2t       (time, value) float64 110MB ...\n",
+       "    asn      (time, value) float64 110MB ...\n",
+       "    ...       ...\n",
+       "    ttr      (time, value) float64 110MB ...\n",
+       "    ttrc     (time, value) float64 110MB ...\n",
+       "    uvb      (time, value) float64 110MB ...\n",
+       "    vimd     (time, value) float64 110MB ...\n",
+       "    vis      (time, value) float64 110MB ...\n",
+       "    z        (time, value) float64 110MB ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            2\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc..."
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_sfc = load_hplp_data(leveltype=\"sfc\", gridtype=\"reduced_gg\")\n",
+    "ds_sfc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af588355-687f-43b1-a487-91a5f78e074b",
+   "metadata": {},
+   "source": [
+    "The data can be visualized as demonstrated below. For example, the 2 metre temperature in degrees Celsius on **2020-07-31 at 00:00:00 UTC** is plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5638d36a-f0ca-442f-8879-36411fc61a75",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "d0fa5bad-4af2-4cb9-8f76-4814295c41e4",
-      "cell_type": "code",
-      "source": "ds_wave = load_hplp_data(leveltype=\"wave\", gridtype=\"reduced_ll\")\nds_wave",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_wave.grib\n",
-          "output_type": "stream"
-        },
-        {
-          "execution_count": 13,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "<xarray.Dataset> Size: 958MB\nDimensions:  (time: 21, value: 315258)\nCoordinates:\n    lat      (value) float64 3MB ...\n    lon      (value) float64 3MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables: (12/18)\n    cdww     (time, value) float64 53MB ...\n    dwi      (time, value) float64 53MB ...\n    hmax     (time, value) float64 53MB ...\n    mp1      (time, value) float64 53MB ...\n    mp2      (time, value) float64 53MB ...\n    mwd      (time, value) float64 53MB ...\n    ...       ...\n    ust      (time, value) float64 53MB ...\n    vst      (time, value) float64 53MB ...\n    wdw      (time, value) float64 53MB ...\n    wind     (time, value) float64 53MB ...\n    wmb      (time, value) float64 53MB ...\n    wstar    (time, value) float64 53MB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...",
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li:nth-child(odd) > .xr-var-name span {\n  background-color: var(--xr-background-color-row-odd);\n}\n\n.xr-var-name {\n  grid-column: 1;\n}\n\n.xr-var-dims {\n  grid-column: 2;\n}\n\n.xr-var-dtype {\n  grid-column: 3;\n  text-align: right;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-preview {\n  grid-column: 4;\n}\n\n.xr-index-preview {\n  grid-column: 2 / 5;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-name,\n.xr-var-dims,\n.xr-var-dtype,\n.xr-preview,\n.xr-attrs dt {\n  white-space: nowrap;\n  overflow: hidden;\n  text-overflow: ellipsis;\n  padding-right: 10px;\n}\n\n.xr-var-name:hover,\n.xr-var-dims:hover,\n.xr-var-dtype:hover,\n.xr-attrs dt:hover {\n  overflow: visible;\n  width: auto;\n  z-index: 1;\n}\n\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  display: none;\n  background-color: var(--xr-background-color) !important;\n  padding-bottom: 5px !important;\n}\n\n.xr-var-attrs-in:checked ~ .xr-var-attrs,\n.xr-var-data-in:checked ~ .xr-var-data,\n.xr-index-data-in:checked ~ .xr-index-data {\n  display: block;\n}\n\n.xr-var-data > table {\n  float: right;\n}\n\n.xr-var-name span,\n.xr-var-data,\n.xr-index-name div,\n.xr-index-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2,\n.xr-no-icon {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 958MB\nDimensions:  (time: 21, value: 315258)\nCoordinates:\n    lat      (value) float64 3MB ...\n    lon      (value) float64 3MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables: (12/18)\n    cdww     (time, value) float64 53MB ...\n    dwi      (time, value) float64 53MB ...\n    hmax     (time, value) float64 53MB ...\n    mp1      (time, value) float64 53MB ...\n    mp2      (time, value) float64 53MB ...\n    mwd      (time, value) float64 53MB ...\n    ...       ...\n    ust      (time, value) float64 53MB ...\n    vst      (time, value) float64 53MB ...\n    wdw      (time, value) float64 53MB ...\n    wind     (time, value) float64 53MB ...\n    wmb      (time, value) float64 53MB ...\n    wstar    (time, value) float64 53MB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-524328d0-a617-4af7-8a78-a1a6d7de09f9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-524328d0-a617-4af7-8a78-a1a6d7de09f9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span>value</span>: 315258</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d0cc7db7-f3ea-473e-96c7-647331ea5065' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0cc7db7-f3ea-473e-96c7-647331ea5065' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3e111003-570d-441b-8bea-b9187ea09e5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e111003-570d-441b-8bea-b9187ea09e5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb470e05-4412-41bb-a6b4-7055449851d8' class='xr-var-data-in' type='checkbox'><label for='data-eb470e05-4412-41bb-a6b4-7055449851d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[315258 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c5e2d12-94e6-433c-ae30-66db6a8c1c00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c5e2d12-94e6-433c-ae30-66db6a8c1c00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-287b7b41-5435-431a-bb95-5d1aa73ab40f' class='xr-var-data-in' type='checkbox'><label for='data-287b7b41-5435-431a-bb95-5d1aa73ab40f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[315258 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-b009a770-b4e8-4e08-8ae9-9480f04d3927' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b009a770-b4e8-4e08-8ae9-9480f04d3927' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a66cc098-28a2-4b1a-830a-a56ed32f4dd8' class='xr-var-data-in' type='checkbox'><label for='data-a66cc098-28a2-4b1a-830a-a56ed32f4dd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bc5b76a2-c23e-4eff-9840-8a574533c586' class='xr-section-summary-in' type='checkbox'  ><label for='section-bc5b76a2-c23e-4eff-9840-8a574533c586' class='xr-section-summary' >Data variables: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cdww</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d242acf-9ced-402f-b329-ea97b3bd7031' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d242acf-9ced-402f-b329-ea97b3bd7031' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f57571ae-bccc-4978-9a29-52eeb5fdb4ac' class='xr-var-data-in' type='checkbox'><label for='data-f57571ae-bccc-4978-9a29-52eeb5fdb4ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140233</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cdww</dd><dt><span>GRIB_units :</span></dt><dd>dimensionless</dd><dt><span>GRIB_name :</span></dt><dd>Coefficient of drag with waves</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cdww</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>dimensionless</dd><dt><span>long_name :</span></dt><dd>Coefficient of drag with waves</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dwi</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-56b95ac2-ad82-417e-b262-20cc13093254' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56b95ac2-ad82-417e-b262-20cc13093254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4290daba-ad32-4f6c-bf39-97a7c3b04cca' class='xr-var-data-in' type='checkbox'><label for='data-4290daba-ad32-4f6c-bf39-97a7c3b04cca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140249</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>dwi</dd><dt><span>GRIB_units :</span></dt><dd>degrees</dd><dt><span>GRIB_name :</span></dt><dd>10 metre wind direction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dwi</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>10 metre wind direction</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hmax</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42c0fec8-1fe7-41af-8a1f-7672af55726c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42c0fec8-1fe7-41af-8a1f-7672af55726c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abd21177-a004-4bf9-a123-c349a837d224' class='xr-var-data-in' type='checkbox'><label for='data-abd21177-a004-4bf9-a123-c349a837d224' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140218</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>hmax</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Envelop-maximum individual wave height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hmax</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Envelop-maximum individual wave height</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mp1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1dbc1637-de95-423e-894b-c83246ce9fbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dbc1637-de95-423e-894b-c83246ce9fbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72de101c-c18f-40b1-8a30-b7fdb18f178c' class='xr-var-data-in' type='checkbox'><label for='data-72de101c-c18f-40b1-8a30-b7fdb18f178c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140220</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mp1</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave period based on first moment</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mp1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean wave period based on first moment</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mp2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3addde0f-513d-40ce-b7e3-e6918536719e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3addde0f-513d-40ce-b7e3-e6918536719e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d10f4a1-d623-4c45-ab42-d8a2147e649e' class='xr-var-data-in' type='checkbox'><label for='data-2d10f4a1-d623-4c45-ab42-d8a2147e649e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140221</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mp2</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean zero-crossing wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mp2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean zero-crossing wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mwd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f78b9af8-c3a3-446c-afe6-9446c1679cac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f78b9af8-c3a3-446c-afe6-9446c1679cac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d6e5e3a-96b4-4fdf-ac5a-63eec1d0f8d5' class='xr-var-data-in' type='checkbox'><label for='data-5d6e5e3a-96b4-4fdf-ac5a-63eec1d0f8d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140230</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mwd</dd><dt><span>GRIB_units :</span></dt><dd>Degree true</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave direction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mwd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Degree true</dd><dt><span>long_name :</span></dt><dd>Mean wave direction</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mwp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ad7ef53-f1cd-4e39-b433-d5976e8035c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ad7ef53-f1cd-4e39-b433-d5976e8035c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0728d0b-e344-46a2-a60d-bdcce8c11829' class='xr-var-data-in' type='checkbox'><label for='data-a0728d0b-e344-46a2-a60d-bdcce8c11829' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140232</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mwp</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mwp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phioc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d0cae3f9-97c3-4b87-82c3-febe9a0783b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0cae3f9-97c3-4b87-82c3-febe9a0783b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c847303-7f93-4268-bce8-86c00d6a2f6c' class='xr-var-data-in' type='checkbox'><label for='data-4c847303-7f93-4268-bce8-86c00d6a2f6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140212</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>phioc</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Normalized energy flux into ocean</dd><dt><span>GRIB_cfVarName :</span></dt><dd>phioc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Normalized energy flux into ocean</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pp1d</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8714cf85-896b-439a-8cf7-6a8dc1cf27ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8714cf85-896b-439a-8cf7-6a8dc1cf27ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d27bc99-f450-4f40-851b-75ece611ee69' class='xr-var-data-in' type='checkbox'><label for='data-7d27bc99-f450-4f40-851b-75ece611ee69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140231</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>pp1d</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Peak wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>pp1d</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Peak wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rhoao</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f83918b-088a-439d-958f-71d4be20c9f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f83918b-088a-439d-958f-71d4be20c9f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fd31741-3556-4e85-976e-398f91139c74' class='xr-var-data-in' type='checkbox'><label for='data-1fd31741-3556-4e85-976e-398f91139c74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140209</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>rhoao</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Air density over the oceans</dd><dt><span>GRIB_cfVarName :</span></dt><dd>rhoao</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>kg m**-3</dd><dt><span>long_name :</span></dt><dd>Air density over the oceans</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swh</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fdb10a2d-7ba2-4902-bac6-7eb0eafce537' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdb10a2d-7ba2-4902-bac6-7eb0eafce537' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4b7d977-c481-4977-b43b-0127e836377a' class='xr-var-data-in' type='checkbox'><label for='data-f4b7d977-c481-4977-b43b-0127e836377a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140229</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swh</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Significant height of combined wind waves and swell</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swh</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Significant height of combined wind waves and swell</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tauoc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6ee11867-1f05-4f33-a0da-20c36b6a7356' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ee11867-1f05-4f33-a0da-20c36b6a7356' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e3c9faa-d934-4c6e-a8e3-5e7ccf5ac83f' class='xr-var-data-in' type='checkbox'><label for='data-8e3c9faa-d934-4c6e-a8e3-5e7ccf5ac83f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140214</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tauoc</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Normalized stress into ocean</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tauoc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Normalized stress into ocean</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ust</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9e7e01bb-79c9-4f88-a8af-4a43cdf5dc55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e7e01bb-79c9-4f88-a8af-4a43cdf5dc55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b65c38c-f98f-4205-bc78-d45eea6eb06d' class='xr-var-data-in' type='checkbox'><label for='data-2b65c38c-f98f-4205-bc78-d45eea6eb06d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140215</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ust</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>U-component surface stokes drift</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ust</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>U-component surface stokes drift</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vst</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c79f4a51-6b26-49f0-bac5-509a17ac2729' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c79f4a51-6b26-49f0-bac5-509a17ac2729' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-028a3f06-ee24-46fa-a7f8-5b999a702673' class='xr-var-data-in' type='checkbox'><label for='data-028a3f06-ee24-46fa-a7f8-5b999a702673' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140216</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vst</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>V-component surface stokes drift</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vst</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>V-component surface stokes drift</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wdw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8ebadb44-d4dd-4874-b0af-2e75e7e808fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ebadb44-d4dd-4874-b0af-2e75e7e808fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de32a6b3-c4be-4195-8117-247ae2144a78' class='xr-var-data-in' type='checkbox'><label for='data-de32a6b3-c4be-4195-8117-247ae2144a78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140222</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wdw</dd><dt><span>GRIB_units :</span></dt><dd>radians</dd><dt><span>GRIB_name :</span></dt><dd>Wave spectral directional width</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wdw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>radians</dd><dt><span>long_name :</span></dt><dd>Wave spectral directional width</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9723a926-ee06-4ebb-943a-1c5c99b4ea57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9723a926-ee06-4ebb-943a-1c5c99b4ea57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-881c6c65-2500-4d70-a50d-2a5155f33bbd' class='xr-var-data-in' type='checkbox'><label for='data-881c6c65-2500-4d70-a50d-2a5155f33bbd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140245</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wind</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre wind speed</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wind</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre wind speed</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wmb</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-73e0e9a2-05a6-43d9-b53d-c7ad6fbc8905' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73e0e9a2-05a6-43d9-b53d-c7ad6fbc8905' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcc43f9d-7078-46f4-9118-e36ed5ed4b52' class='xr-var-data-in' type='checkbox'><label for='data-bcc43f9d-7078-46f4-9118-e36ed5ed4b52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140219</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wmb</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Model bathymetry</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wmb</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Model bathymetry</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wstar</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32e4dbae-469b-473f-b416-ccbff9326798' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32e4dbae-469b-473f-b416-ccbff9326798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0c1dfcc-6e8f-4e1b-9afa-60a765d92541' class='xr-var-data-in' type='checkbox'><label for='data-f0c1dfcc-6e8f-4e1b-9afa-60a765d92541' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140208</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wstar</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Free convective velocity over the oceans</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wstar</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Free convective velocity over the oceans</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-01a44a1c-83d6-43bf-b801-f7d768d9e3e5' class='xr-section-summary-in' type='checkbox'  ><label for='section-01a44a1c-83d6-43bf-b801-f7d768d9e3e5' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-001f4653-f173-470a-b86b-840e919f30f6' class='xr-index-data-in' type='checkbox'/><label for='index-001f4653-f173-470a-b86b-840e919f30f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a94b7b8-a8b9-4380-99d6-1fe7190bce5d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5a94b7b8-a8b9-4380-99d6-1fe7190bce5d' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 13
-    },
+     "data": {
+      "image/png": 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ofvaznx3UtBPWo0TDfuutt2YduvX5fEkLZ3/o6OgA4IorruixL2FF7C+dnZ28+uqrvPrqq1nztT+ofQTPcblcyWHmxL1885vf7HHcoEGDek3nvvvu47HHHuM73/kOV111FYWFhTz44IPs2LFjv/LdHzo6Oti3b1/WsF25RPkzzzzDrbfeyqJFi7jkkktwuVw8//zzPPfccwO6dsJ6mWo5BJIdioSoNBgMaT7jCeLxeDKNXGkritLjnNT9BoOhx/UTx+SKgtHZ2YnT6exhNU2MSmR2prLRV5kCuOOOO/jkk0944IEHkqI6Ne993VuCcDjMVVddhdls5ve//33aN5X6DlJD56U+2z//+c9JH23QOo39OS+VWCzGf/7zHxYtWpS0qgN84xvfYN68eWlpp5IYoZk+fToej4cHHniAs846K+9jnOd/knypz5NnPwiFQklRdccddxyyBiQhfK+66iqmTZvWY39qQz6Q9H7zm98wZMiQA87brFmz0vw+ExyqEE/t7e1JP1mbzQbAkiVLenQQcomtBM8//zwnnnhi0rUEelq4s7G/1lPQntfUqVO5+uqre+zLFCoJli1bxmGHHZYWIvC9994b8LULCwsRRTHZsUiQ6GAkypHL5ephxVZVFY/Hk7OsJXxhs6Wt0+mSFnSn09nn9bPl2+v19hCGra2tadc+EB555BGWLVvG3XffzejRo9P29ffeQOtoXH/99Xg8Hh555JE0YZqZVmlpaXJ7Z2dn8v5HjBjRI3/9OS+VFStW0NnZydlnn522vaSkpEc527dvH6tXr+a4445Ly+/IkSN59tln8Xg8WX3Y8+T5/07erSJPngEiSRLXXXcdoVCIu+++OynSDgXDhg3D4XCwe/duxo0bl/w3atQoKioqkpajhIWqLyvZxIkT0ev11NfXp6VXU1OT9F3NRrb0DzvsMD7//HNGjhyZllZxcXEPl4L9JdWiGolEeO+995IC5rDDDgOgubk57fqVlZV9Cn9ZltPem6Io7N69O+2YbPecECMJcQYQjUb7dG8Bza95165dVFVV9XheucSxJEk9hH+2CAx9iXaTycSoUaOScWoTfPTRRxQVFVFRUQHAhAkTqKurY9++fclj1qxZk3SZyUZVVRVOp5MPP/wwbfvHH3/M2LFjkx3HiRMnsnHjxjSXkIQve660Dz/8cCRJ6hFNZcWKFVgsFiZPntzrfffFiy++yP33389vf/vbZNSP/bk3gN/97nds3ryZe++9N6ugTNxjalqhUIgNGzbkvP+BnpeI3jJ37txeF21J0NTUxG9/+9se97d9+3asVutB+47z5Pm6kbcc58kzQG655RbWrFnDr3/9a2pra6mtrU3us9vtWa0/+4ter+dHP/pRcoh2/vz5RKNRli5disFg4P777we6/Xgff/xxpkyZkrOxLSoq4txzz+XBBx8kGAxy+OGH4/V6efjhh5kwYULORUyypf/973+fCy64gB/+8IcsXrwYq9XKa6+9xjvvvMOyZcvSJuntLz//+c/TolV4PB7OO+88QBsGPvHEE/nDH/5AY2MjEydOpLGxkQceeIDTTz+dSy+9NGe68+bN48UXX2TkyJGUlpby1FNPsWnTpjTrsdvtxmg0snLlSgoKCpg3bx5VVVUMGjSIhx56CKvViqqq/Pvf/+4zHBvAeeedx6uvvsqll17Keeedh9vt5sMPP+Q///kPTz75JNXV1T3OOfLII3nkkUd47LHHGDFiBC+//DJvvPEGAMFgMCnwy8vLkx2H6dOn97BaApxzzjnccMMN3HzzzRx77LFs3LiRl19+mcsvvzwprhcuXMjDDz/M1VdfzaWXXoqiKPz1r39l2LBhaZM19+zZQ3l5OWazGVEUOeuss1iyZAlFRUVMmzaN999/n7Vr1/Lb3/42ec6ZZ57J008/zZVXXsm5556L1+vl7rvvZtasWdTU1GR9ZjNnzmTOnDn89re/pbW1lZqaGt577z1efPFFfvzjH6eFhRsoq1at4je/+Q0LFy7E7Xbz2Wefpe0fN24cRqOxX/e2ZMkSXnzxRX7yk5/Q0dGRtIiDNooxbtw4RowYweGHH86dd95JLBajtLSUf//73yiKwhlnnJEznwM575133qG2tpaf//zn/XoGU6dOZfz48dx+++14PB6qq6v57LPPeOGFF7jgggvyLhV5/mfJl/w8eQZIItbuDTfc0GPf1KlTc4bl2l/OOOMMrFYrjz76KCtWrMBmszFv3jyuvPLK5DELFizgtddeY8mSJSxcuLBXS9QPf/hDiouLeeqpp/jXv/6Fy+ViwYIFfP/73895Trb0R40axUMPPcS9997LTTfdhCiKTJo0ifvuu++gCGOAxYsX8/e//53a2lrKysq4+eab0yIy3HTTTcmlfR966CHKysr4xje+0WcYqp/+9KeIoshf//pXVFXl2GOP5Qc/+AEvv/wybW1tFBcXYzQaufLKK3nggQfYvHkzL774Imazmdtuu43bbruNX/7yl5SWlnLqqaf2y/e1tLSURx55hLvvvpvbbrsNSZIYM2YMd911V1ZhDHDhhRcSCoVYunQp4XCYOXPmcN111/Hkk0+yd+/eZBSLq666ijvuuIObb76ZRx99NKs4XrhwYTKtl19+maKiIi655BLOP//85DGFhYXcd9993Hnnndx8883o9XpmzJjBtddemxRKTU1NXHjhhZx33nnJ53zhhReiKArLli3jX//6FxUVFVx//fVpE+kGDx7M3Xffzd13380vf/lLzGYz8+bN45prrun1ud1+++0sWbKEpUuX4vV6GTJkSFr0iv3llVdeQZZlli9fzvLly3vsT6wA1597W7FiBaqqZo1YU1FRkUz/1ltv5c477+Rvf/sbkUiEUaNGcc899/Rp5e3veY8//jijRo3KagXPRuIbePDBB3n88cdpb2+nurqa66+/Pm0yX548/2vkl4/OkyfPV47ly5dz880355eozZMnT548Xzh5n+M8efLkyZMnT548ebrIi+M8efLkyZMnT548ebrIu1XkyZMnT548efLkydNF3nKcJ0+ePHny5MmTJ08XeXGcJ0+ePHny5MmTJ08XeXGcJ0+ePHny5MmTJ08XX+s4x/6gBymWd5nOkydPnjx58uT5X8Fg1GG3FR6y9L+24jgUCvHKKy+D8rW9hTx58uTJkydPnjwDRSdx8omnYbVaD0nyX1tlGY1GNWE8/WUo6Oj7hDxfKqa62USrP/iys5GnH+Tf1aHjON3fD1pa7+55MP+evibkv6mvD/l39TXA74bVJxGNRvPiOCcFHQjOli87F3n6QGyL5N/T14T8uzp0vMHJPbZ9U79/DbHYnH9PXxfy39TXh/y7+urzRTjTfv3FcT/YoS8FYITUd4EfyLGHmkReEoyQWnpsS2z/MtmhL03LQ2oeE9s9ohVzxv7Evszz8+T5X+JpaXby7/0Vynny5MmT5+DxPyGOByKKezu+P8ccTDIFZzZR+WWRELSp/+c6DqCK3B2PA32W/X0veRH+/xtHTMJrzF2lOWJS1u29nfNFkxDKeZGcJ0+ePF8eX51W4UumP6LpyxRW2a79VchP5v+5MCshzAdJBGcT14l9uQRwX/vzfP3JFLmZYjn171Sh3Jeo/jJ4WpqdF8h58uT5n+Sr0E5/tVqEPHl6YX8+lt6syn250GT7QL8It5avkmvP1xmvUZ/TWpx6zFeVvEDOkyfPV5lDJWK/Cm3fV7dlyJNnAKS6eOTaP5DtqWn15b7SH5GduT9b+qkW8My/B3K9/nIwRXhflWTm/X5RlV9C/OYSyanbv8pCOU+ePF9/DsQV8qsgGDP5KubpYJFvDfL8vyGX60muCimXWE3dNxA/70wrdabQzTy3t4mM2c7rSyRns5LnEuaZx6XuqxKt7OtnJZ75fHvLU66/+5P2QKz72cjlUpFK5vavglh+PXQ4C6wff9nZyJMnzwA52HOCvgquBv9LfPm1f548h5i+KpT+RNvoraLLJsD7KwT7qkCz+VPnEqSZ6fYmvPt7/b44EPG7v2n3dlw2Mt9/Nt/kbHwVxLLXqM+7V+TJ8zXjUE2W31+B/EUHE/j/QF4c58mTQi7r70DOydx2IPnIFMO5BHJfAv2rTpPqplzoSPsNJLepKqBAtBkEA5S3eagTXaCCHAHRqJ0n+QBF0H4IKgiwWl+GzgxDjB1Y3DJGq4JoAKHrsEzXi2wi2BGTvtCJez0Ee5bLmmSFyBeSmzx58vSHQ1nn9rc9Otjn/q+SF8d5/mfpbXJdwp2hP9bX3tLMJZYT22UJNreVIoggmmGo3IYgwvqtpSDAXsmCVZbx2AS81gLKy3zUbrIgdAq0mESiTQIuMcSWwgI8dhFjsYogwhBzB6IO6gxuDE4Vg1MTkYLYLST7I7APFgnxG+sA78ciogWizQJKGGzBCEpnK/WKgNGmUFAhYfI00bHDRK2oYq2QqVvVvQqSaFHZ4ShBNulAANEESlQTu+ZCCUEHvrVijzx8TlnO/I1a6Kd8cgRXTRyjXkHIOP2LthpnuoFkc6+I6nreY548eb48BtpeDDTdA7H65i3GAyMvjvP8z9NbmLyBVnCJ81QFNrWWsra9jOEF7VTa2qgvKSbWCpt+ZGQtVVnP32YuJx7pFj17KEKnVykeE6XdK9NeZwSKAHAfKdPxjo5WCpgy34c+GCXo09G0zcyODCEo6lVUQNCBEtVMpruOqKRgokKF3kfhoDg7Rvd9r6oKclATo2NsLQg62C6VorNo+yMNENotIuhUdGbQ2cBUpjI9vp0P/1KEv0lPxKPLSNXW4zql4yOUXQLtkgNfkwo+ATZH6Lx5LHJNluMNbQAU6LT/w5f7ibcLfd4PgKjTOhRrH3ERbNOhyunnzb66narDw32mc6gsy18F3+evM4fCV7Ov+O4w8Pj6/T03VxSdvPj5anCwDQz59/rlkK918/zPMJCICbkqOFWBaCPE2gWUGMghgchegViXR0CdvYLWV9LF3+7iCoSgjKCSJnxTMY4TaZtfw/z5W5kaaOAF3zh2v+PE9n4Lx1xVT01lmFuFuQhBiQs3vIqrNM768aWYf+jg5PBGBncKeK0KjpDIlo0W1n5kx1oaZ7RTomhEFG+1hCILlLULNEREnruvDM9HOjwfidShuTAsuLIZT9SAGo2z9ldGjHaZgnKJeFhEigggQywsIksCeoPKFqUSS6FMoM2ArUSiaGSMvR+kr3PvrIzhazWw1VCGHBeQ4z3vv/piiZqiTqSwyL6gA59go2JujFaLCxNgGg/++cUAFBGmJd5THLfEiyk1tNEka8eV36dtLxc6kAKw7ZcGoo3ZxbIiC2x7sSDrPoD1/yqkaGSUcluM9x9z07DTzDEXtVI0OI4jquI1CcTCAh0dBlQZgiYdoh4MVoVSUwyf+eBUs3nfY42BRln5MkJNxdpgXUcZcgRso1SGDGCS64FeOxe9habsbQJyb5N78/Qk78Lw/4O8OM7zP0+28GmgNXDeNSIIYCxWMZaqbPmJsde0xhzlxzhBT/1GzZQ6fYEX5RILn8ZGctr2D/lshYPaSdW0NxRSvWsXwc81saivFHFMl3C97eH9T4rRy+3Yf1BMxbfNTPaEIAqlBW2UF7Uxf1AMgJDUwrRYA+P2aWLca1XwmBXqRQGjU6J+h5m1e0098mgqkLEUy1SNCOMsknGOibLq327eebCY4SMjeJ0G7f4DOtp3aGnrDQoXX9GMvUCmUq8iCPDOJgvP/auYmfN8TJkeZN9eI0UzFDZ9ZiUWFxk3KcglizswB0TqYwL+RiMdXh276o3saDDS3GpgzHwfk47xsUNfSpPqTorbN+ITIdJtEU4l27YE5bqe+wQ9mCpUAAxOFZ0NRLOa5jphl6J07jHgbzAgiCrDjglicct4txmoW2dl8z8L2aqDvessRAM63niohLHjQ+iNCju2WGnZZcJSKCPqVBQE5LhAPCgix7sF+byftlFx2MH3Eu7vxMHefKq/SvQW9SX1/944mPGtVYUebjap+YtHBBrWmKkPOmBzjFhQpGWTOXnM8OvjYO8+p/ZvOtrf7O5Aj741jqlcZbSxJekHP1D629Hv78TcXHVi6j4pAKqsjUbpbN0+/KnnlNe3EwuItA52MdqQO5Rlb5GDvkgGGq3ni2AgQjvfcTl4fLVryTx5DhK54hZn/h1thvp/6vF8nN4aFlTEiQV02MvjlE+OUPuulXgo0z0Atr6dboFc/boDXgeo5XkqOf7cVqYM2ciyeTOpCUtM2NVO+1YzK1e4YOVe3qA7PyW+NsoLPThCIl6rAnRPUAvYFAb7NUtxJm8/WJLzOThrYogidOwy4qk1IupUxHdVRB1cdnUjkTFx2B2g/Pv7MLSobN1gZ8tbDsINInurwV4G3piMI6riXhDg8ulBrHYtH/YxESpMAsdGNSHqNQl8aNTjiMW139O1exhEkLC+FJ1qx4edp+VJIGnWX4COuDuZ39S/E7gN3RP3Sg1tSatxLnRmGHF972IJ9BSpKjX+euS4wItXVqCkCFtvo4GKmRFmH9uOr95APCwQ8gjEfXqGLAgydWQnOoNKqE3P2keddO4y4qqJ0blH60wZrAo6k3pQG36TrGDKIgL7Esu59mfGfM4U0wcqrvsrgrKtgJlJX+K3P0R9IlG/SCwoEvOLtG4zEfGIOIfEKR4TpXmDmeZNJlo3mRk8O8jY0/04qrqv+9lSB9tXZI449Hw2no9EWsIia1dpZaFmfgDbXGjfZiIS0bPt51pndD1VnHJPI9Yi+YDvLZug8m8U+Pw3elAFyk6XGXRO39fJJcwiTbD5ym5DwaiT/Iw8KcBLV5XzmTgIW6lEyK/ns3g5VqdEqF3P7kmVGItVxkzqZMTUlpx1crbf2eLYD0Rg5wppma2sJVZzzZX+QMXqF2FFPtBQl3l6khfHefpkIA3RV8Uq1Vt837gH4l6BzfZSjEXdxzR+Zmbt7cU90rr6F/UUlUhYfCLrNll4/1Mb8ZCOQoeEzzuw+33tnwnhupdNGNlERdbjdHYVsctDYUOZjh36CpC0iW2bqxrwGvV07jZDk8Dr92j3NWRekNbNJsxOmaKRMaIuA+HPINiix2hTcJbFKRoSpXR4FMs80Pl1HDHXTygoYjCoNJUreI16zDpRe1aV0FThxnk8RORiXqFGy5Cx6x9QXtXlylCgWW2b5OLufRm/oVsAE+/elk0A+6Ku5N+Fps7k70JTZ6/iOVMoN1GcZk1OjYgBWeI8F5YS3gfmwSpDJvqRp1mwDlcR9QJyxELteitunZ/CShlxiIoiCbx3R3eZKRwUJ9Chxz1fpuhIgbnlDdQXFyevUzrAhqo/USsGnEZie1cnxhGN4zUJyd8aUspvKXl8p17AEZNQVfhwqZvazyxYHDKtO80Mmh5m8KgQRpuCbpDKthcLqF9tYcwiH4IODJYQexxWLC4ZQa9SXtQOWXRDUoQ36WnbayTQpsfXbGDs0X6Ka2IDfwBZEOph2U8rs+6rfa/ntr0f2PDWGSgcJNG61QQqRLw9O8fZaH9Tx8iZ3b/3vGXHMlQh3NrdsS2bGMFUoGC0KQO6j4Eg+QRQBRBUhP5lPSfmcphwXww5LBBphM/vsbP9Za2jYHCrVP4QpIgEErSH7FR0BKl/XCu8a3e5CU6TQOm/0EyN1tPbMb1ZuvvT6cq1P9ff/aGvxaH6c35vvum5Jo8nohrl2T++Gkomz1eavlYZS+WrMnSbq1JQYrDh0m61NuLXcRgLigTvZhHGJ/6kmeCoOEHAWybAyAjfONZPOCBi2WVi+wYrL7xTmDMfVqNCZWEco0Vlc505bZ+oV3GWxznrrDbiE2I8bJzPT/xv4jUJXBe+mFHG7RwZ384OQynbHrahvLqHBuCFkeW4x8fY8nz6deODTVSdoGIbqSAIOjo/gLbXtPcQC4q07DLRssvEvq1xTvpTM46YRB0wWFC6LNMCE5tl9spK9/NLvEadJiwTIdYAmuTipBtE4ncqqb+TorgPUkVxtm3Z9icEs9vQkdWC3CR3C+TMkHEJUhsXSxWMuVUCzEC3YNxyjYFYq8BuwYWqJqzKKuYqBUEPQy6XsdaojJDqk2laJGW/GqgDsYymi9y+UWRY/agLQ0ykzafD06Kno1n7RibM8WOxyQybGObNlYXsWGfDWRJH1YG3SbN4Btq0/+tXW6hfbaFqQpjmXSbsbglRr9K03ox7eAy1U6B9h4mG1ebk81vwu2ZcQ+Np+UlYrZ+6Pn3SqtGmHDRxXFgiccH9tTSHjPj26fnkATdRf7piFHRq2uTM8d/08cGfu8txzVFBhswJ8s6tJSnlIeV8k4raNfnVNlph6Kw4BhdMqG5BbxrYOxoorq0efPV67GUS7UOc6AvANVvBNfvgPD8AYzHEfSq7r0l3NYu1Cmz7lR4ENDEuRvEIeq0ukSC0Q+TTs7vPKZgiM/xaORlFZ3/oTbx+VQVitnzlilmfi8yRz76s51/VZ/FVRFBV9dB+pYeIzs5OVq5cCUcvRXDmX/iXSW8NeUIkm3ecSGTEK4c0H7vesrJ+qROjXUZUwV4kUVYWo2xamICgZ1BZBKUS1u0sI7JPwLQ3jOk0E8YimBZqoLbRwualhezr8hfOxYxvdTDlZB+gCRFHSERs09FZa+KPj2d3aThsaJgLjvXgD4s8u6aANVu7J64tvK6JQeMi1LWZUd8xIbgkXnk0t2sEQM2RQawxhfaAgcIzddjHaX7AjVEXjkYPNc3tdIaNGEIKggCtu03s+tjKiT9poWxEFEjvwCQq2SbVzbjtU9k8am2fzztTDKfSlxjOtPhmE72RsCPn+WaLN/l3oakz6WqRKo6z+R9DulhOHptFMGf6u+78r401D2n5LJ8UpmCQROWUCGUToznzeSBk+65Sw7m9s/4mzNUrsp+bxd0mF7IMN107JOf+acd52fKRnZBfx4gpQcYu9LH8990jHka7TCzQLSztZXF0giaedUaFkScEkDtE2hqNKBJEPDqqZoQpGhmjenYIgJhfxOxQcMSl/fa77S8D6bh79+mJenWoQNGI2IBEraqCd6/WcSiPHUtk5MGt/7KVj8S9vfXbYlo2a51xg1XhjIcaehw7kAnK2Y7ZoS9FVcHzsUBop0h4t0C4TiDeob1Ay1CFim/KiGbQF0KH1YHSrhLdKGOaqKPjjxEUr/Y8i35rQXkpQOcH3eWo8mwJ63AV+xg1q3A+VELvYLRV/e3c9qcs9mU1Tvyd69jeJmFm/v11Ec+qpxTe/A7HHXccLlfPtuNgkLcc5zlgcvknplmcM/wje6sUVBX8DXqkWpHOsIHwbh3+Nj1ioUpxaYzKY8NYXN3Dj4mP2l9v0PwHg5ow8LUYCDca+PQtB0MHR3lvb1fEgxFRxs3zU3FehNd/VYpnt4GGUeWYSmSGjQpTPSlExCAS2GHg8/ftaXmbMCbMUeNCWFsE1n5kp/iwELXbzDzU5S5hNCjIsoAoqiiKgNy1IMWnuy18+lC36D7leA8V53jwGvXaZDRg66tWQq/nrlSrDg8x++oOPteV4t4WxFNrwN2mIn0s4HlGR6dfj6c2SCMGfMOcGBwKDkFFUUBfJnHsb1oxV8is0acPJzep7lQDqbatD+Gby8e3N1GczX0CBiaMM0UxkFUYQ+57yCaac1mUR0gt7PvYwrNLKjFYVAxWBffwGDOv6MBoO7R2hf6M2AxEBOeiPsukzYse2UPEL+LbZOLjV5wYTAr4dez4zIa9Ks5pSxoQ9Sr1qy3s+q+Ntu3dokYKiYw6xoeuTMVbb0DQgbVGYtJRYUS9ir1M4pVry/E36bEh8+aS7o5g9eQQJ/740DbQ/REuyWdfJUHV/lnxBQGcQ7qs4jv6n7fU+nSgJM477bomEmYvnyl7XTtQV4GcFtlpXf/Q6u54SGCbr5Tm5SLNy3TIYS18pOQPIhpAVwDSJyqKVyu7BZNk2n8VRl+pZ9B340n3i9AukZaXBCSvgHW4wshfS+is/c/vF8mBvq9MUtvH3u51f6zkB8td5P87eXGc54DI5UaR+jvhxxrpRRDrm1V2fGhD1IEUV1n9dFHW42qxEUZkwjf9yW0jpBYcMYkjv9WO1C6wc1W3oO30aNfs9IsUFkhUjQ+zd6uFd/9ezHHNnXTu0kwSTdvMFDRKNEYtxKJapX36DQ2MPdrPq38uJRrUGv+NWy34Hi1m7x7NKvPiM935tJhlwhHtuIQoTnDZ+S2AQF29gXGzAzAozl6jIRmloVzoIHJBFWNP2k5xow9Rr/L2rSnWY0Hl8FM6ccQk9E+EeOO1UoxWmepJYQbZZcw1EvHDAhRZ4gxxxXizaBAgMi3UkBTg69RBWlpZNF2qkByimnvszxS9A7UMZ5JNEKeSKoJTSQhi6Dkxr78kw71liORMgbxDX0pVexsf3FWExSUz/ZJOon6Bj/9WRPsOI+6hcfRWBd0hqEWzNZqOqMrH0Rkc7voEAOOBz0kDYNDgKOMmBdm8XguRZ3fGeeKKasLB7I6pn73oxDpSomxClI6dRtq2mxg+M5D87iJ+Hds+KCAm6Yh3CpSdIVM0Q6bI2YLeqAmoKSd7keMCQ6cFcd4cx+qQiQZFCkvjWa/5RXMwJvylkWPy5KG8dsICfzAjd/TnmkabykRbMxMvTd+nKBBs1hMLCihxgbBHx6p7ivCv12EsVTE6FHzrREyVKqJBJVwnIMRUQLNGq6+FWPuEE4C1VHHiHU0UDjrI7ymDg14ODvF1v2x3xv9P5J9kngNiIB9juFMk1KYnHhHQm1Q2PFWIqyhOydAYFknh46c0QWUwd1uFi0riBHw6ol2CtWhIlHGn+9PSdcQk1r9cyEdPuqkZH+LU7zcRHixg6FSpW29mw6tOPJ3a8GbDThOBLsFcV2vimiW7efsZF6tfd+L3pd/L87d0W1gvuH8PgQ49HXVGVj/nxOqQKB0RpWJMhMISiYYtZja8mtsN4MF/lqB0CeaS2QHeuacM5/QokUngFDsoDncyQvqcHb/X0+gqRGdVKZ8UwVQoM/LEAO5hcW0J46jK5KP8NG0309FmoHatlZ2xrmezLcrYcUFWflyCohOIBUTWempwD4tx9I0tkOItkvAdzmZdDSmWfvsIJ+hLDEPfgjjTWpwpkHMJY8gt1vuKdgHdIjmb5fizx7U8hTt1SZ/0wkFxfA163r2thAnf8jLuDH+P83KROmko01LT18S5Q4VOBwt/2MJCoLXegKNYwt+h54UHSmlvyO4IqioCHy9x0b7VxAlXNzN4SpijLm7jkYtrADCZFYJ12vfU/JyO5ud0bKaK79xVh9UpM+WU7ndbPETzg7U6DzxKQ56BM1AXAEXSJiNaXHLOEHfZruE16imo6L5WyyZtxKJ8UgTX0BiFVXFiQRHvXgO+BgNxVSBq0mErjeGojtO0zkzltBC+Bj1GqzaKk5n+QO4nJwPoyHzVOJRi/n9NeP9v3W2eQ0ZmbNIE+maV3autWFstvPN2OXanhLfVgKhXkWIirZjZ/i6MOdXH3O+10brbREFJnFiLns4mA6GISGlFHJ1BxVItM2qhn9ZtJsyFMo7qbqv1yDkBtr9nZ88mK3s2WTnv1/swjZewHyZRfWIYo1clZBaxRhQGO2OEndCp01OLgWEXBBh8dpDXritFVQRUBfQ6Fb1RRRG0xjtg04MdLDYJRRIIefXsWaOn0CLTXmtkR4q1eu5pHRRVxlm5tIhgl+BWFIFJc33MPb2T+xL+nRu7/Y43p6xoN/8vrRjtXZEEEpVdrFskDbEqGI0qwYAOe4FEoEsct39u4r3Pew6Rd+wy4ohJTFO7rci9YRXDSUvsgVqIE/RHGJst3uT/fZGYgLc/+cq2r1zX1sNyHN4rMPzYIBWTo2x/2U7nbk0o+uoNrPuni3Gnexl1cv+FcSqJb+TLEsS5KBmkWW6LKuKcdU0jf71GK6s2t8SUU7y4Z0bZ94qVD/9SxITjvcz+ZQdDXHGIaqH7xsz342kwYCmU6agzYimUqRgTYcSsAO6qeF4A90E0KFK/2UxnvYFghx69UcU1KMaI2UEM/fB33p9ykxqpxGvK7fCdKKufvuBg9bMuTv5ZE5Vje4/b/elyB6uf0b79mWd3MO4YHzpDV3ojJUb+IwhA+xoz/32yiEhQZNDEMGNm+rG5ZFbeW0LEo4WdBM2/3WxT6NhpJLpNh32IjBQX8Ov0WsxlAeIOAYP5wL6fQ/H99fZsD/q1jHoiXhF5j0jFmAiC0NP98cuyin9dyE/Iy3NQSAguOQLSC2HUToG2WiMtO8wMnRBi6ti5xNTXWfGsG0URqK6JoNfD7h09h/ABjv1hC7YJEmaHZh2w+mXefrKI+g+sGE0KAa8+OZFtjbWSaSFtwonQKPLP3wwi3BWDeMjcIFVHhFh1bxFytKeZQ9SruEfEGDI3yJqHukVTzfAIbe36pJUZNIt2rhXuElhsMgVuCdfQGMVDYgiiynuPFlM2KkJxUZxNH2rhjspGRVD0MGRuCDkq8Omj3eLxjJsbkta0VJKht1L8TNtb9Sz5UwWRjHtzVsT45u8a2NtsQWdUiYcFLG4ZX72BurgTyScgiKB3qujtoC9UMRZDs5B9Ql5vkSf6CsXWF71NvIPeLcjQ0xLcn/xkWqFLDW2U69ooFzoYIbWwqaOUTT9KsZoKKq6aONZiifpPrBhsCsfe1LLfw7qpDVN/G+LEex896CMAVq29CVfZy/t1/b5IxNVWFfC06dm90crWT2y01BuRoiIzvtXBUcf17MR8kQLga8XuhYRcr9K4zYSoA5NNQY4LBDt1RAIivmYDnkYD7fuMuGriOKrjxMMCnduN+JoNTP9GJ4edqj3vL6vjlECRIRISsRZkDzunqiBLIEsCWz+x8dHLTmxOmZpxYda+UcjCi1oJdOp5fWkxok6lZlyYXRs0Q8HRi9uZdqwvmVbzXiP1O03gUogGdLz/z243NlGnYjAr6AwqclxA1KugCkQCImrXKN3s77Yzel6gz4mUqc80UndyzomuB0J/vg1VhWhAJB4RKSjpriMUWdsXsPbPnulr0PPKteUAnPq3hmQ7miDqE9GZFfQHECHky+SLmJCXF8d5sjLQmatJcbwsxLqlzrR9g8eEqXQezapVqxBENVlx6Q0KUsZywpOmBmls0WMUuycKHX1pK4MmhHnjrlIad5kZPT7Etk1aZao3KkgxEUFUqaiJcuL3Wnl9aTF123qPOAFw1AIPUw8P8uffDaJ6cogjL2hj6zt29qyxEfHoiIZEnCVx2hq0fAweHaawWGLj++mB/2umBhH1KqJexahoVrcQAu11RqSIQDwiYLCoWA0KEVnEoKoYTCpbP7FjssgYC2T8LUaKa6KcfkkLzuLeBVeuSVi7Pjfz9/u6LNCC1lAksBdJRPwihaUSJoOCtUAGASIBHeGASNCvQ44LFJZLHHH4TJoK3sA1JI5nvBPI7oaRKpL7K5D7EsK90ZdIzkZvIj1zQt8k81ag270i7gHdeyEa1phBhZbNZqzFEkabgnevgW8trd+f20gK41wip69Jdl+EOE7FmzJ8HfSJSDERRy9l9P+rQM72vnLda3y3nn2fm2naY6LTq2eI42g++GAV9jIJW4mEHBCIxkTkmIAiaWnoDCoGg4KsCgRb9FidMs7KGEOnhxgxK0iRevBjIA90QmdqWYiGBQJezU0u6NMR8ut49bHMKDuaz3ACs00mEhIxWxQiXQaMApfE4Sd62LvFwrQFXqpG5I7+snerGW+7HmuBTElVjEJ390hE4l3EQgIr7iinbXf3KNqZv6nHXZ3bn/3LEMeqCtvetlM6PIqrKs6K28to3GJB1Kvo9Sq2QplBk8OIBpVtb9uT815cVTEGTw6hN6mYh8o4B8exlcjIkraSo9j1SuNhAUGnZhXAL19ThmtYjJk/7LsO7Y3eImkcSvLRKvpBnc5F7ADvIj9Lsyf9fSY93ClOBufgOAarQutWEw2fmikYF6PEEIVVJIUx0EMYj5kRYP0nmnvC2d9oZ19ljHdXFfDmA1qFayvQGuRtm6xceGo7H663Mawqxtuf2jjxWC9Pv+CmfaOFcKcOi0WmwKbQ4dEhSdkbgAIR4l2xWuvWWVl69eCuPSpGq4IsCUTjAudc10BrWM/r95Ry/uJ9nPDdNoJ+HW+95GLrWwXsWWtDEFRUVUAQVGwOOWlxdpbGKXRJGMsVomGBcKGJqAGin8kY7TLRgI5oWEfl8Agnfa+1V2GculIepDdUAEWTQ1x7/24UBQIeHZGgDm+7niFjwxh7sZw4QiKqCsGASEuTgYhXYcMHZj79h4sJJ3g54tud+M2an2y5Pj3WMWgi2W3oSBPImaJ0wKI45NT+t3p6pJEQyb6oq1eB3Jf1OnF+wkVjfWQMpYa25MIhMgrjajyYHTKde4zojCrNG8yUjo9gLFB4+9Zixp3ho2RM37Fj+zuEeTCiTxxsEnnyWhVshQrQu0jrj2UzUyg4ouoXKqoPlvU1kU7jbiNNe0xsWlWAqFNp2GmmYmyYIVPCFJTEqDaEmDW9He9GAy0NJvyteuJRkcKSONZSmYJiiYJiicKyOBWOOK5SCXPqgiBZsnugZSUcEvn1L6sBuOG2vRiM2Z+Jp1OnReYpjdO4z0jtLjNer46gX4etQMZoVLHZZOyFMja7jCwJDBsV5hvntBN2ynz8igNJErDYFCqGRnGWxrHYFAJeHc/eU8YZP2ymwCVz2Hw/XpOAl9zlYPCY7G4cibLjNerBCEfe2MorV5cT6qqHD3TRk/2hr/LctsfIu//QDAyzvtNO4xYLY47yM+f8dgrDKk17TGz/1Mb2T6zMPKcDX7OBT19w0rnPiFFU0dkVGp7VjEAnX9fEits1S7GjOoYiCeiMKpXTIog6FbNLpqBconh0DEGAE+5o7pGfvgxiX8RKf/1hh74Uo85F9SG+ztdeHO8PeTF88EiNlShLIEUECiolon6RopExhh4VxGhXMe+I862l+7D6ZHQGlXf/XkzzDhPRoEg0oMNkk9n6Sbff7r+eTY9WMagihtUp0dmu0tZi4JEXtP2f1xtRVYGX3y1k1smdBEsU2lq0rvKMk7yMPNmHHNcmABaGVe66ogZFFtDpVV75b2FSoI8/zkvrNhMtdWYcLpnRQyMMqojz3AoXG153MPObmjXx7Wfc+FoMDB8Z4fBJIaqsEitfcjFyTJjjTvYAsPtzM5GIyKDqGCGLQplB5f23Clm/1sYJVzcjVKlIMwSiXh3vdq2u1tlsoMaukGobzdX4JURx5v7EdlGEQrdMoVumtLp/Qf8FAewFCvaCKJ3NMS6a0sLrK1xs+sjG528XMHpGgKFHt2EaozCCrnee0eAklnKGbmtsQjCnithcojVNQFs9mkBOiOTU4zLOyRberT/XTLUcZ/NhDr0l8eYTPRuEzrAF70bt5ps3mPnW0n1ZY/PmijjRY9tBFMT24IGlFehlhbbUfCbKmhQX8LbpKaoYWJSJrM8hY9vBFsuHwh3BERKJRgSW/mFQctsRl7czbbxWlnyfGmheb0aNWFm1qoiZJ3cy8mgPpVUxCtxS0srX1zX6Yn/eu0WGWTMCWMwKjqiA2LVcutcnoiLg94vc+bfytHOOn+nn1MMD2MpjFLlljAbtmeYqN6awyPFHaX75mZ15u0PmvF+lx1/uq5PU276WmIH4Zh0tO0w0bjMTj4jYXBJjj/Hj7KN8pqWrg2iW6+xPp683XIPiHH9VM0WDY3Q2aEaavZ9a0MWKcBRJbHi/AMGoIhhh1ZNuor7uCrd5rwm7U8JdFePwxZ04KuJUjAnTuNWCt67bVJzw1U7wraX7ALKWu4O11HWudAaqu75sMf61d6uoO+4lYq7+DQ3kRfGhYfVDTnb9VxO2ol7FYFSw2hUMOpX2Vj1Ol8zI4fPYvvsdSkskpKCIwa5QNCzCqGlBhEpt1nNno4FnfjkoLW1zgUxxWRzLiDhDxwSRXSJVpWEUWUBvVGmXDYg6NTmBLUGmMEmdgOBr1RO0Cii1OjwNBta9VIjRonDmbxvZ+o6d9/5eTNGQKKja0JfNJVM2MorJKlO7zkrHXiPOyhhSTESng9bdRqSYiLVAJtS1ypazJI4cFRBF0OlVdDoVn1ePwyURDIoYzCr2QpmAT8fUY7yMnhbsssplp78CKrMBGgiJa3Q2n5QcrrcFRJ563sWqNdr7veD+WoIFumQIugQJd4v++CMn6JfbRRZxnEaKZTkhkvvjbgE94yNnLjNdd6NK65aeExwrp4WZe007oJWP/gjj3hrWgYrjVLeKavurAzp3f+hNML/xsoO3XnPys1vqsOfwQU1wIGWzR1oDECGHykfX3Klj414TL9xfiiILuKpiHHdbC1JYoO19E+/+vRgElcGjIsycOYuK6c/ntM72yHNKmTjQDk9ffL7TxMp3CmhrN/C9s9u482/llJXEOX2ej/tTjBR/+3k9YtdjD/XxLlPLTOZ7Tx2JyFb2c5WTbO88EhDZt8HCe48W5ZwPMu+CNsYcFci6L6urTMPJOCqzu1UEPDosdjkZvnF/O3Beo56OXQbe/1Mxoxb6GbUwgCBAdKtIy04TvhYDxUOibP2ogM6dBsYf52X4zBDeJgOFZRKuynjOxXLiEYHW3SYCHZqrnKjT3OpKhkYxWtReo070R5D2Z7GYvs7PtdR3fzF2uqheuTDvVnGwyLXGep4DY9gxQXb9107luDDnXNWUVuEZO3U0thjYsUcmWCAzuixOqUsiGBHZscfEE6840RtVpp3VQcXoCN+7v5agTYccE7r8eLuvkyo4vEYdPp9IrbWY1rtlPDsNGC2aIK8aGWHauZ0IYnf4GV+9nqd+mr4cbSpBYM9aKyOOCLLmGRfttSbsBRKTpwcxWxT8TXp2NVgwGFVCHj0hj57Tb2igZFgMb7Oe7e/ZadikDXGFAiKeVs0SYC+SmPZND7vXWHHJcXwdOuwGFbtLIujVIRpVdq6z0bjLjCJrPmPOYomxQ6O4J4YQxIGJp0zXiwNFEKC4SMJRKOH16Xnix1Usvq2eEUUtoKeHi0Umme4WMIDJen0J48QxXQI5YUVOTT/b5L1SQxuqT8Zd24phuA7zR172/EVPA4BOZcKZPsqOCBMaae0hjude24ZraLc1PrOBOtQzwFOFkjH+xbghZIqzVOFzzIleZs7zY7P3XeZyjXTsD1/WpLTEPWz41MpTXf61I2b5Gbk4gKlAoWGNmffv1DqIx3y7nclH+dDpIFIX61MYZ/vOU599MCSyfaeJaFRkxNAIxUXd/rbWrnMVFZ5+w0FDix63Q+bbC7yYjGoPMfveKjttDVo0oJZOPc3teiIxkaeecTO4PIaqkiaMAV790M5Js7OLzFQyO1N93Vcm2UYoQHvn0bDAjnU26j834fdoE0WzYTQpyXj1n61w4Gk0EI+I+Fu1Za31BhVzgYzBohD26pB9IgsvbMVaoGDKElDl0zcL2LXRyp7NFtzlcWrGhrHYZRzFEkGfDluhTPXoCHaH3C+XIUdMwlCgEPWLrFvqZNIsH2a7AsOgdJhWvwTadax53kkspOPTF9ysf8mJqggU1cSYeLyP4TODWe/dYFZ7jSKSa22C/oja/rA/C8p8Ffmfshz3Rl++Nl+Hl/lloMjw/KWVSGGR7/y8njGl2oeXWfm1t5xEtf1VAiGRnfuMrN5iYXedkVafDtBWlDMaVRRR5djLW6nba8FUqFBeFMU4Ukb+RE/tVjM1YyO4p0bY/EYBHz3p1iaeAagCVotMkUuiqc3Ad++pI1igw9+o55NXS4hvkdOGm75xSifFRXFeeNlJU5cbRkFlnCN/1sqKqyqZdXInMyaGcMkCkgw2q7by3a9v1SzbJ1/UwpgZQT580cmql51MmOVnxJQQrrI429faeO95F2rKhDhRVDnuO204iiRCJoG29WY+etkBCAwdEcE9KUzIoyPUqaezxYCvXhPXZ13bQNXI6AFNnBkIjpCYZjlOfY9vv2/n+Zc14fmt39cj1GjP/j3dGOAgTtZLEcVGf/ffsQJP75nPYkWGdPeJUkMbZUIrjWdnb1iyUTImimhQGX2Kn/JelorudSW7PsRcb+8381saNOpDANZ+dDNlrt6XubUOoNz0ZQ3sjd6sy/3hYHboDga53kc0IrD8GTfrVidcwFRtvlnXt24wywyfGGb+Nzuwp4StyzXJq7/vfeMWM8tfc9LS1ekGOHdxG6NHRLFZFYL1Bn7zcCnRmIDJqGIxKXj8en7+vRZKR0SRJPjg7UIa/ToWHR7gl/eVM6oyyuwxIQw67T4EwGRUsRoVRAEeecNFs1cTUGfP9XLMpACKs/fndiBlKBuJcrVnp4lH7y/tMU8FoKQsRsCvo7hEYtpCDxXDIrQ3GFFV+M9d2jLnlcMjjJgcwlGsWVz9qojaKeKXRdY8q9VF7vIYHU1GTjhlGlLBq8iSgE6n8tnbhbQ3GrE7JU6+qAW/R8/G9wuo32FGlgTGHh4g4NFRt10zjnzzqkZqxkX6HSIvldR64pXHipOTv40WmVg43Y/t4kf29DvOdDayWZC/CgbE/liT85bjg8iBvOwDObe3oP+9cbAsUKkfwP6k2dsQjByHD+8uQgqLfPfiFkaXSj0acmtIJBYX8IdE/vR8MTv2GVEUAYtJoaYkxpypQUa7Ygwqknh7k5VnPnTw8h/LmT4lSARY8Vl6we/43ELdEiPDRoYB+MGPm3CpsGOXiZVvF+IN6jjvV/WIJhVHTGLjikLa/qsj1UFWFFWOHR9CANaXSbjtCpt3mfE3GIitNmBzSJQXyNRtsHL3ClfyHBUYNTXIgu+2YbEpfPJ6IWtXOvj5lY0UF8kEbAoNErz7nJsxE0JsTbFq/Pbn9VgsarKy91skPnrZCUBjnZHa3SZ0epV4Rki2lx8vZsjICDMXetJmZh+qiVuZIiWRX1tATApjgA1/dzNsnp+CMTHKa3pO0uuNnJP1coji1G1ZBbI1y7ZerldubqPwPCOB5XGUzp6idezpPuSoQO17VixFMkff0Npr+v1ajrircTzYPscDEb8HmlZvwqc363J/GMjw+qGgv+9gyZ8raGsxMGZGgHGXe9EZVXa/bWX1A25Kh0f4zk8ae10xcX/e9arVNp583k1hgURxURyDXkWng5ded/LPp/T8aHEbg0okFAVAIBoTuPLb7ZQXSfzzJSef/aMUg14h3jUxeWRpnFK3RCgkYDcpVBdLlLt6luE/n9FArWSido+eI0cEIYT2r4t4Yc9ZbuZA7vuL2HPuSiNkVbCGREJWBXtQJBQWePhezfd54XEeaqpjRKwKhQ4ZV1F2v+3KwZr19T9dv8+6phExx6S8ecd4aNhlpqnWyPp3ChFFaNxlRmdQqdtpwtNoZMTsADO+2YndLVMdjTLu8O7OtarC2jcKk+L46b9UcO39u9O+9YGOdLTWG5DjAiOmBNnxmY1YWIcoqiiq5i+s06k8cskQFLlbdNeMCzF9gTcZvcloVilwSzjcUlJEp4r01IVTEnwVjICZefiyfI+/9uK4Wu5kZ8a21IebPhR/YCvoJPxW+7tSTKog7k+hOxRDsgeaZjbf3URhXX+BHikqMnFqkEIZjB4ddE3QsIZEzAEIWuH2x4oZPNKC4pJQ9pooK41zwwUt6LsqK3PXSN2cMWHGOyO4rRKlBTLxQh3X1xpp7DTwjaO9TB0TpsQls7VTx/3/KEEQVP52ZwW33biPY8eHmTIhxG33ldOw1sagah+CALPO6WDk6DBr3i0ksNeAP6hjzqQQ/gY95U6JKZURho6Ksr7RyNKni1BlgZHzAjz372JsVhlQ+da5bQyeHaDTIKDTQwwIxUTefrqI+cd7ksOb9qBIpU1h6hEB1n6U3hIUxgXiFjUpIrakWLETMYpTK7rv3FVHoEPHK3eVsf7dQta/W4jZJvPNK5twlcVpFbVwcKnD+gfLpSJb9BdBgBuvq+fm2zXL+azpQf7+iFYOxtwWp8jVQaPiQuhqMFMXEcnmWgH9F8WppAnkLlHcn0VDCk2djLFuT/62n2zEf0w5gl8Blw714XZ4UyuIW54vTB7n6CX80xdFwKYccr/T/pApngciluHABfOhEMv9FasbPrXy3L+KiEsCU0/zMO0MT7I92POaDYtd5rzrGnOmr0gDF8YBm0Jrs55lrzi55vImqirTy2Jbu44/LymDQplVGy1MGhlhzRatQ37/s24sJoXmDs3SHJdEpo8NEZMEnnu7kEBXWLC/vuJGVQWcNpmZo0KcOctHonmLF+qoRGKIPvtoicE3sAVdDL6+jwEwd9UhCaFttcP9v6jvUd76U55+/ts6vB4driwx7lPRj44weHSEw4/3EakLM766Z4c4GREjwwpcGFFZ89/CtG2ZcxH2fW6idZ+RSUf60aWIdCkuACp6Q3f5kGX4199LqK/TXLrmH+9h0tQgRSUS8ZiAoghIcYF7bq9IxvMH2LPZyp7NVqpHaYajUECXXOHy5ItaGHt4sIclO1v7/lUjm3ZSv4C1hL56T2KAvHFDCV6vB2uxRNmEKOO+4QNHjpni/RCKvc2Y7nXYtEs0px4zOdDIqn+72P6unQVXtlBUHcdsl5N+tJliPVV8Z/6f7ToDXeVmIJ2DXMc6YhLTYtoM40HXmGjdbSK0T88zrzoJh0QuOaUTWRbYvMVMm09He0wkKsCUo/xs3RlmxzobzS0Gfnhb+sQ7d6HEn7/XTFFXCOF4l6X3D+emfxgRYIxL5pwTvPzjRReKoiJ1ZbFIFbjsu23c/WApxxVLjJkRZG+BAdO8OCcdoYWuEZtEti13ctNTJUwaEeHjTVZ4VVu8o2pkmJVPaO4AQ0eGEYC9u0yseNbNiQ6ZoundflyCqGKyyLS3GvjwrQLsVgV3oUz5yAgnzPSnieP50wIY9JDatM0YGiVwoodRwyNUlscJBEXe/qCAk6cFwC0TVBVUp8IbokqiaYoEdTx+a/pzK62OsuA7bVQMjR3cCU8ZETECNoUCGQRBpbI8zoYt3XGkt/4sMdRbx6R/xNBZs/sipwrkbMIYerpO9CqWu/yNc62ql7nQR2KRDwD/ZgH1pm7/SUONSNmFElNnaOWtZZOJVfcW0bLZjGevHufgQ+NL7O/UoQZFnO7stf0XJYrNfbuSJklYAHuzNGcTzvagiM8vIssChq6V+MJhAaNRTRMMucjli9pfDsRC39xoQJIFBAHWLnOwdpmTOee1U3i0n8kLvbx5fwnr37MzYXYAcYDzBDKJRgU+ereA2l0m9uwyc8oCbw9h3NBk4I57yxk7Ksy9D5VRYJc5da6P0UOiPPGKC19Qhy/Y/VAri+NcdHpnckJdp09k5XobW3eY2ddgxBPU8cqnBbjtMiccFkwK33ihroeFeKCiOBOdd6AdzsT1tWeaKFv2oNinQLbaFKz9ENH9MSxk0waOqOZWc+nv99G4x8jSrvpZqTPi7grLufEzK8/9u4hCh8zHLzsZXBNF1EFDnZGONq2dLSiU8Xl7yrFBFTFOO7JrFc5wSpnSq/z+Fw3UNxr44181q7pOp3LFzxoo6lo8pGGfgb/9qRKAFQ+XsuJhmHG8hxFTQgwa3t3hSWgcVQFjmxbCVBS74m5blH5FVBkIByLAZQnqPrDS8ZmDww5xLLevvc/xOx+8SkuDN+nfeeqvGilLCSJ+KCduJD4OVYUdn1l5/YkiQj49Uw8PEAyK7N1tosCq0NJmSDtv3KQg3qAORYHG3WYW/bKR8pFRpJiAqFOJR0QMZqXHMFBmry+Xy0SuwjeQXmLmQgWOkEgwICLqVGJFPStHR0jk009srFzuosCsMGJkBFd1DKtdxj0+DB0nYa5ewfaVhbzwn/SJHgVWmYWHBah0x/F3DR+VFMoMcsex9bIMaK5hup//rQx0KvO+1YHsUCgfHcVn0qOq4PnIQMnQGEaPwOefavGJh5/gx2hVefHWchq3mRkxOsw5F7VgMGi9+A/fLuTV5a7kynWJTkl4m6GHWM2anwtaqOkjlFC7V4fFpGDtut9UcbFrj5HlbzqYNjOAJAmEAiKvr0h3FXC4JEaMCmMslqgYFmXI2HAPwdHXRJHEu071j8ycNd/apmfnHhOvvlmI16cDIT12NcDJf2mksaKIJtXdI4pFR9yddHOItA9JnpMpgvv0L04lhwU5VRyPsW6nTGxF+ZeP5mU6MhcnsLgkXEPj+Bv1DLlRRWeFdecZcdXEqJ4VYsyiHLPdB9A5Ta2Lgj6Rv/1Uu/+xE0Occ2G3paovQZzwOV7/3i0HPVrFQERyb6xtMbG11sTOOiMuh8TeJiMtHfpkPV1YIOPzdxdQk1lh0tQgZRUxyivjVNdED3qjfCB4zAp7t5tY+UQxnc3doz7jZ/kZMjZM3TYLG7r8Q8srY5x3WTMFKdFnUv34e8MWELnxtkr8AR2Hz/FzzIkebPaeIwexuMDLKx28/5GN6ePCrNtu4ZijfMw5PMCLrzrx+kV27TERyvBTvfzEDo7osiwmOP/u7jrsqlPamTqs2whwIEJY540jOwz9EsOyw5B1ezbXjUS9H7IqB+zrno3MdzWQjlgsKrD6GTeffGDHXSxRWhFnw1ob3z69gyOmBdnXYKC+0YgkCVRWxKgsjyMKsLfewLsfFrB+s5XJ40OceKwXr09HWYmE07H/7yBgU+ho0/Pn3w1i7MQQqgpbN1pxl8coKo8Ti4r4PTr8HVrcbb1BQadXk9ZpAXCWxSkfEmXw7BB711moHBdh6LRQn9fORTbd0l/BXLfCwodLi3A6nSxYsCDvc9wbM8/qwFTShMUhYzB3DzPvryjub49/1+cm/n5fOYOrosTCIk3t3R/3WDVOgUth5CgPFQ6JtoCOD3dbmTc8yGf1FmQFikplQgYd9+42s/x3Feh0KrIsoBM1sS2KUOaWsJfEqajSAnfrdCqFDpmyyhioAkafjlhMwGpVsIyTkgs9DO6U+/VBO6LxrPerKODbaaZlj4kWFYbXRDEUS/zhN1q0h5nTAixY3KFNoFNg3WobpeVxDpsRZFhXTMvU5+m1KkS6QsgOmePnhIjI2k+ttO7Tho38IR1Pvp97kYifndHKuOpYlopaqzgzRfLNF7ew8mMbrzxSQjQqUuCQKB4VpWhIlM9edDJoXJjjr2xl3FBNTHmNeowxiWjXs5gwPIIrJkJMq1ymHhFg53Yzz91YicUuc9K9jZpAHh3n6EtbeffvRUhxkUK7hC+gfVILZ/uoLtcaBbNBxeMXicZE3A4JQYDXVtkpccqUFUnog3Dzk5qLwlHjgwwfHuWICd0N2LCaGBf9SLN8e60KNp9INCryzsruZzakMsaaj9JX7jvhvFaq5we1iBdRtYcYzhTLqRNIkttSLMgBm4LFFqMkIBIIiprQyfKZrbiqgvP/VssIR0tayLeEiwV0+QEnfIVDmqtEqkDO6V+cbX+X9TmSErkiEbUiVSC33RAmvj3R2HbfuyCqxOI6BF0cf6OBjd/vvk7nHiP6cTrG0K0a92cEKhOzVWHeGR0MGRtmhLs7vd6E8cH0L94fEZxNKHWGRJ75zEEoJlJTFMNhllm5rYBdbcauKV5A13c+aniE7Tu15eITwnji1CAb1tqIRkQ++aC7/LqL4/z4lw1IEui/Aq2UMyLiHBxn0vWN+Hwia1bZkSWR1mY9Oz+yE0iJQdvUYCTo1/HIX8vxduqYNjPApIk6nKVQ0Mc7lBUosMkEQyIWq5KMApLpWmM0qJw4NcBb7xdw/BEB9CK8+KqThVOCnJexrLfRB3taDTR16jlsWHe9YvDJZJrG3tpoS4rj/RHG8UJd8ryE4E38n00kJ/ZlE8HZyGUQSYtq0fWMD9QPPjO9BL25DHmtCrPPaWfaGR3s2mDFs8vEFZc0M2yI5gNdPShO9aCez2HE0BgjhrYD7clt5aUHPlplD4pQLPGbP9cCmrvHru3aIi4CYDQrFBbKON0SJpOK3qAmO6WqCpGwyG6vjvodZl6/u4TDT/RSUDSwfCkK+Fr0RApFzA4FAWhaZ0KKCYQtcSyFMuYCBb1JMwjmClEHYJkTwGhVsBoVOMTu0V97y7F97qPoHSk+xgMQxQOZIZ5AVeHppUV8/LkFSRY47XAf45xhim0y4bjAsOKB+yj6IyIxWaDAJCN1yNiNCoGYyM46kVadleaAAUWFuEFHa0BPXcCAIEGRTcKkV/HEdDR59ZRXxRhSFsfglBG6ojjsrjdw/vntPQpcPCawd7eJjeusjBkfxlYg89QjJXRmGdpJ5chqPz5VTxSR04/z8s+XnOzrivbwywtbWP5uAW2dWhp2q8KgwVGmTAgRKzoGy+Bua+T6tVb+888SjhgfpMYYx26WsXT5K4fjIjFJICoLRCWBE8f6cVhyV2y+iIhcKGJPsTKHbfDk6w6a2vRs2WNObp94kocx39ViSpo7FR77obYqXtngKNWjInz6ZgFWi8LM8WFKnDJFTgmHXea3D5cl03APjjL2aD9l8yPoTZo/YXyHSP1GC6u7Zj6XDw3TtNuCXqd1IBC0SX2SJECOFaB0OgW9QSUa6W4ofnVRC9Vl8aSVJLURsO808sIrTk5Y3MG6NTZeeq6nK8PsUzoZ/82+fXJTMe1Y2GNmfeq3cvtNg/B3lZMhU4Ls22hBlkSMRoVYTDtu3Ew/c77fzhqrNqyXsCK3xIuT7hW+qEtzr8gVsi1zol1vod1Sj00RyQlKnXsQYjKCpFDcUI/zD13+x/PtMN6MYBWx13vwP5G+aMqsK9upOjycNiu8L3E80I55bzFtswli15T3gYFbjvfXKpwpkjwhkXd22NneYmTtvuyhtAAuWNTB35e7mTfLz+kneVi3ycKmWhNzzm5HlgWMJgWdHpo+sfHR+wV8viV9yfeikjjtXdEZ5h7tZcEpnkNiTU595gGbQigo0tRg1DrVJVLOxrq3jowsw72PlbCnqzMwc+ZMNm1+D7NFoaBQi5NbaFRwOWUqSuMMGxrFWag95121Ru55sIya6ijfPqOjh0hKTFaLt+i57u4KfnlhC797pJSbL2umvEu49PWuU99pKCbwwPtFGPUq5x3eid3UPxGZELS5BOvBGoVIkGoxTpAqShMMJEZ0NsGcK1pPrs5pqgXba1XSjAyOkIg9KCbPHUh86ER6fYnz3vKVK91s6eQ6DjRXn99eP5hBIyIMmxAiHhMw22R87QZ8ER2qooWQKyyNI+rAZJMx2RQC7Xo+W+Eg1NmtKxZc2czrd5fhHKKFDIz6dMT8orY4l0FB1Guh9uIRAYNZpWJMBNegGM6KOAUlEo7yOGKshMB75x9Sy/HXXhyba5ZSOry+195GKn1ZhlMLTCwm0N6pwxoVCUVEvAGRjzZa+bzWxB+/14S1lyV5s5GrJ97fYadclZE5AO1+HbW7ddR1GgnFBBRVYE+HgR2tJqqK4owYG6F6UAyXQ+Kll11s32vCpFeI5lhaOcEPx9RjEhVkVWBmiQ9RgJgssKyumDcanXTGDBxR7OOjNm1CwuGVARZOCSAAnrCO2g4Db35uZ+zkuWza8D4lLokhE0NUFcjsqTPywcd2BMBiVjGIWs91fHGEi2Z1Jp9LquVBdmgdhd3tRjwhHbWdBp77zIFOpzJxcBQTCrMnhikqkPnTC0W0+dLF/pijvSgWAV0Ytryp5Xn4qDBOt0TdDjOdXh3xuMiocUG2b7Ylz5s2JkRdmyHNRWbMMT689Ub0JoWxR/txzIgnRVRhVOKhC2oocUlccnoHu706GgM63nrNmTx/6rEe1r+jrdI3YkyIHVutiDqVcRNDbPys+9pzJgcJR0QuOaMDUeyu9FKtSQGbwna9wD+vGEwmx1/VzJDDwv0PK7R7YY8g+Knfza7PTTyztDjNR04UVc48xst/Vmr3d8rFLVTMDSWHyxIW5FSBnFUc9xF5IhvZ/I2z+SGnbis0dWpWbFVlTN1m2n8fRu0yqg2eHGLq6R6Mo7M3FLnE8YGI4lQS77Q3S7FryvuHXBwn6qs97QY2N5l4/BM3x4zy89/tBTnPmT8twOLjvHiDOtyF6fVdarnNRigk8MFbhTjdEgUFCj6vjndWOvCkNKxWm0yhQ0ZRoLQizpiqKNWTwgy29J52JrlE096wyF//WIG7SKKpazLTj65rYHjKvUSjAtt3mfjwEztzjggwfnTPmLKdHh37GgxEoiKSDHXNJ/Phh6s488om/IKAKQzhgA5vm57OWhM7tlkwGBVmTQtS5JZ4/c1C3IUy9a0Gzjqjg0EVcSrK0tsJr0/kN3+q5KbrGvj1rYOoKo1TWRJnzVYLsixQYJU5bYafCYOjVGREozhQv+EEfQnkVIGWWp73x8c9m7Dsz/seqN9+c+eJaSESE/nOlueIXctXfZefb2ZUmkGtKWW3H+I6QbZVUPdnVKkvMZ7oaKWS65nu3WOkoU6bgwNQPjpC1fgwRqtC1CQS9YuEO3QoMsQCOmJBAbtVxl0d49MXnJgLZFxVMSYv9PHS7eVUTwpx4k80w6Ych0hAh6hTUWQBD3r0ZpWoX6Rls4lAs57QXj3+Nu3fyMOsTB56St6tojdee6KY5nqtEps0z8dxZ7fnDNkyEEtxp0fHLX/UrF6uAgmLWaXAqjCmJsL3TulENKvJZWwThStR0HJVAqlDRwaf3OswUyp9VUAA5WqM8ho4oibcnW5HlOgogY37DGzZ7eTTbVaiiGxv0YY5zyurY3ZNmO0+C7du0PwfF1W18e2hrXRE9bhD/uQEDgC1E2Q0Z4ZvFNbxjcI6JBVMRWYtxIwAuLsXTZBLDcwYEubMKT62Cn6+Zatnr8/Ixi0W1gRMdMqGZHghB3FafQbGDw/z3212bEYFvQjxoErAbCDSqRKWBHwYaPTpUBUBFYh2zUKWJYE1uzTL00d7rNgsSnJCSuKDu+KuPTz4i2oEHegN3RVAfZ0R4gITxob477uaq0JBhQSbtf1DKmJceXwnETsELQpvvW/nhVdcNG6wMGJSiMIiiZX3ljLhBB+jv+Nne7wU23ptanZrp56K4jhDunyOR5TGeehxbfGAAhGuvrcWtd7An26pQm9QOP/X9Yg6sJbF+PhV7aN/f50mlH/wh0E47TK/uLAFR8aiC/agmD5howtBp2Ivyl5x9zv2ZsZ3M2xklJ/eVI+3U8eGz6y8+7qDArvCm2u7BX350NzxgIH0pZot3p4r4w2A1DjGACWeRjrb7Uij7aBCR9RFsbeJIqEDPo9T0dpIrFVA16j5GPtCAqNmhJi80IujPN0Hrq+Jr/vjvtVXBz3R6cklLPaXRP3RH2Fi8GkjYW9ut/H4J90jEqnC+KSpfo47MoDFpE3aURSSfu7uQjl5ncx6K5fFympVOW5heodmxuwAsZjAi0+7mTFbW2zE69EhCNokuS07zLy40oHNrjBnWpCxo8KU5bD2qirc8scKPF4906cEEQQtfvmk8SGGdoX+skcERAHa2/SYzArDh0QROnVs2GdkV62Rt95Pj0oQiYrUNxjZ12ggGhWx22Q6PXp27zVRVhmjucGI2aQw5bCu51IeZ2iWYemgT6S51sS+HWY+2W4mJguYbQpjnRGWPl1EVWWMay5vTjvHbFYpLpL49a2DGD0+xM7tZva1dLcf/pCOx992AnDL2S0MKYmnvI/e3Rj6K54NPi2qUK53nU189ZZO9jykT8iD9El5CQ52VJeBfnNek5DiOyvhiKpZ85T6/UXs2nVSn1PAln3lwP3JV3+OzXxHqRMdU/8eXBOjsiqWFMenXdCMUp7hhpfiO6xF6+pafXR+hI6dRrY9V8BLt5cj6lSKBseSrn46g7YSbeJ8O9q731dUzMiqdP+JcKdI2ydV2spdh5CvveWY+UvZ+HQkaQUEGDklSM34MEMnhAYcGzZRkF9aWcjrb2kN9tQxYaaODjNicgiXJPQQwEDWbZn0t7HLllbig8qstNIEdocmSOSObkuGzt3tUoDbBB1R5I4IzVEjtmAIiyijc5oIyDrsup4VouzpXeQA6Jym9OskrpXClvLTGNu0LJnHVLytca7YOpGoot3zKWe209xoTLqGRERtNTC/T4etQKK9zUDtTkuPdEZWRNnbaiAqiYweGUaKC4ycFMJWICNLAq6xEdoaDKgKFLhkXvpHCd7Wnp2RIcO0yWy7PrdQNSTKcbP9TB4fTluF6o4nipBFOO+yFiIRkTtuqmLwmDAF1XE2vV6IvTjOYfP8HHl4gGJZSJ77x38X8fkuM9Nn+pm5uANzV8Xz7z+VY7ErnHZZC/oWPU8+WEpjswGzWcHj1WO2yEQjmp+vQa/w8++1MqhruDW1XEmS5s/52UYre+uNrEuJtaw3KoyYHWTWOR3oe1utqw/LcYJoROC3P+9pqb7l+nrqxmjlMuFWAemuFQB7AsOB7sgV/QnJljw+w78YNJE8Wr8V9YJaUAUsbolwh1bZWgplRL2KuySOuyyOsVSmsCxOYYlE0eDYgILpH+gk34FEMsjW2FtD4n5ZjqF/wjgcE/jdv4up8xhzHiOgctc1jZhzjJ7lsrLB/lsAcyHLsGenmQ/fLqCu1kSoq1N8ybmtDB8axZRS1t96386yl13odConnNrJzu1mtm2ycsTUAGd9Q+tk1TcYWLfZQiQismW7mVBUpLQ8jsmksG2zlfGTg2xa190RrBkXYvT0ICaLQsivLS88dHwYg0llxcMlNO81suCkGVRMW9bnKnmJDmssLNDwno1dn9gwGFVOW9xOeZZiE4kK/Pw3VZxyZjsN+0z4W/Vce5bmtxqLC1x9ZwWyLHDSbB+nz0+fD5KrLPTaxpBuwOnNVzibMWcg7h6Z9GYkOliLj1hDInWBE9K+q1xtb7xQl7QcJ1zeMg0QCbeKRNqp6WXeSzbXuQSpftQHc+5B4rq9kfptyjK89Jybw+f4MQ/P0pZnCRaQiqpAQUTqEQt8oBEsVE8pvPmdvOW4N+ySxNzzO5h7fgeqAjToqN1iYfcmC68vLaZsSJTv/KwBKS7gsSg94sJmkigIR57m4aRjfTQ2G6jbbuatNTb++bKTc0/yMGN8uMd5/RHGqb2x3sR0IkYwdH+QPazMWURmAp3bnBTIsV2acNAErAncJnRAWUcEOSwT64xgBCxArmop1tktto0uc9ZjEtdLiuSOaJpA1vnlnHmOKSIuU5y4IhJF5NXn3UiyZokutMjYjApOi8xQS5QPd9nxRvRYDDLhuA6bUeZXcxqoKohj1GkV0sZWC+1hHZQY+GSzhU9aDYSjIoYVKm6HjFGv4IuIPYTx2FFhtmy3ULvLwqyJQS74fhOlXZ2r1Lm5ogA/+067VqlIAl67TMWgKDpJIN4hIggqo0dEOG6un8KwmHyfETt8/3utLHmimM/W2Bg2K8DgMdpKSguubUEQVbx6ge3rrPiDIucubicWF1j5fgEGg8qeXdqzPeyIAH9cWswvvtdKiSv9ren14HbJHH6il9FWBfsKJ++/oFUeUkxk61sFbH2rgOrRYead3oFaqiBXZI952RcP31WedfsNfxjEtffvTlaUiQl5mT7HmaRaj1MFb9ZjU46psWuRzkdt30jgzhCOQXGOOdaHuziO16PHOjSKozi37yjx/lvRB8qhWKwlZFVwAZJB7VenfKAEIyJ6ncpZUzsZWRrlN690v+fpY0NcfHonYZuCOcWPMrPhT230MwVRan5zhXwDzdK7LyZgtSmYenFh0+lg+KgIw0dFUBSo22Ni42dWnnnZCbLAdT9qSp4/c1qQ1950cPLxHl56xUEorMPtklBVAVmG3/25gk6P1iwetcDDaee04xgf7hGeTVXb8FgUOgQRvVHt0bkKA2EE5v2wDUdUJVI3pVdhnFn+jBaVyUf6mdwVxksGAilCC2DbDhP/ftZNdU2EQYNjfPJhAQUmJfk+jAaVEqdEU7uBlz8o5OUPCvnLRY04e+mI9CWMB0J/Ryj6S09Lcs+wgvsrkg/0G8oUxv0Vs+nPSKS+REpaoB0xicGdQk5reKJNySTzGfR1b7ms+6miOFWwH3leW/f2HPVmrvUgdhpLGSHmnknXI3RsyiTy1NE8KS7Rj+J1QHztxXEqgghUydRUBZh8pJ/3X3Dy4QoXd14+NO24wqI4Yw8PMuN4L+YMh/e0gihAZXmcEYUyc6cE+XSbhYeWuXlshUKsyx3g5Gl+po8IU1wgU5ilgKUW3sSHkvpBZLpXZLpV6LzxrBbhvsi05MqeKOzIbpmLdUaSojf178TvzGOhWyTrnCZkTxSd09Qzjx2R7nzYUhJJiOau+yop0/GXsvSlXJJB1DOOvaAaNisObn5XCz8UjOlowcwwXfdEqgkl3Z2XoyZ25rR0JN6N16AtDb16nY0tXascNXj02KviEOru9Wc2/IlKxRER+fHFLbz2loONn5s55cwOps8K4N1h4sa/acLixm+3UKPG8bcZKTRpM3YT8/K0CqD73quPDLJ3q4UV7xdgNCsUT4ww+WQv39TLxCICq54qIhTWsbHJyBGDso8t2YMiVr/InGkBZp3sYW+BgUGeOEuuHUw4pKNum4UnUmJNz7uuDaZEBiSQoymLlsw52kv7PiNOk8pR0wLYQyLeFEHTJBezNTQKgBZPTb+vkbm6XVaxrKiMXLcR//1hFpzdxqyJ3e+/sqrr28pwOcm0zhyskI8HSwx/WYt+GHwyFcj85IwOfv2vUv69tvt7OfKwAN85SatDUhvczG/DHMjecGezmKWuhJZJ/TYzd3a5IBn0Cmcu8lBcFKe8VMKWw09SFGHIsChDhkU5Nuzhd78YzEdrbQyv0eoPi1lBVeHtDwoYXBVjV62JsaPCWFR44LESFODXf9ibjPyjZVaLuJNZZvwmAUO2cC0DJJvASC2PAY+O9/5VRDwm4HRLjB8S5ZGlJVjMCnOP9WIwqvz77yXMnRHg5GlBdCnv5ubLWvDs0+EL6TDp1R7CuC9xmi0UW+rvxDyQBLksyalxk1NJjW7RX1LT6inAe5aj3gTzgYri1LSzWYtT6auzYA5oPsoOm8JelyYu97okxgWzdyhDKXNhM7/HVFfP/uY/lcyRnFxxoHO56GUK40S7klgjIds6CqnPrxsBR5dlymvtLnfRGHlx3BdrnnOxd7ueqad6GT6zWyh4TQJzTvXwYUo82HFHBBB1KiOmhNi8ys69P9b8bBec3MncY3xpM6FjcYHPNlh4daWDUFRElkn6x37vGA8PvKZZv1asKWDFGs0P76Fz6rD2sA5oH3/CU0/nTRWPGZbULiGYSxDncnFICFPoKYr7wugyE+uMpIngTEGcjYSIzpkntxl5qB1PpVYZqs3dD7c/sS+VYfb0Y7pcQgDGiV7+MDXC9Wu1YXm33LvrR2oFnrVitWuVyYwpQZa/6iAWE7nkXK133JdlLtVicfICLyOHRfjb30tZ/nR6LOebnyzFpNdE8eGHB7n82kYMNd2CPrWHbClUOO5arXedqESUNvjgqSLWfWKjsjzOr69pwJ1hNQ7YFCIRgWeWFtO8z5i0gM2b5ScySMIzPYbeoXDyFa1sXlFIgUti/buaO9K7txez+Il9Pe6vN7E371gfy57U7vP9NzWr74nHeCkfGe1RcSVcKTLFbia5LMZufTulV65F8akMvVrCOVNh5+165CBY1DjBZh2nX9bKpCGxnDPX+3NfigIfvlPAK8u073vuaR1MnOvHWqDQVm+gwCVTv9OEqggIgopLgM9W21n7kZ3qmgjNjUbKyuNcenVTr/fZF/31nxxI497fSVD/3WfnoZXd1v37f1GfNa1cPsypAjlBqgDK9FHNtPolfleNiXDTdfU89XQRm3eZ+XdKNJaqyhglRRI2q4zdpjCkOoauJI4kCXS266nbY2LDpzYqy2O0tutZ8ZoDs1nBqNP8jMNhkcuubqOhycD6zRYUBQ6rDFE1MZQUxgOJbzuQzlVOa1uaOBCS19e369Mm6H70nva/3SHx9koHo8aFOeMkD5PGhdFlmXfgrJJxIvd4T/0Rpf2xHKdOnM6WZraFRPoicfxAhXO2Tlm2b6S/FuZcLhXQfZ1UIXmgneOE/zFIeK1xvCaB+hIpLepF4n5aSqSkxXpwZ/eodOoxvdEfYZzrG8gWHz+VbGsqpK6d4IjG09JJtbYnyPQnT+z/opaV/9qL472fWfB4otT+186QiijtDQZMFoWSqhgUqPzkvt2se9GJzaYwZI6fFQ+XEgmJhFLWgH99hYvJ04I4XDIffGzjjXcKicYE7GaFDp+emRODnHm0j621Jh5e5uaFjwtw2yU6umLaHjsxwJyqYBZhDOa6LqGZyw0iwzdXtzvQw0KcTfCmitJMgZrt+FQB3R8/4gRRT6zHNpNT80VME9Fdf1uGaiJJHqqJW3OXX5ZiFHqI/x6uGKn5TZlUCGjnpjyroW54cshO9niNVBf0zGO29FItHJki2RwQidjhnmsbe/UNf2mNvUdM5lNn+JgzJky5S6JG6i4DFrNMOCUsW6FD5pxvdlA8puu5ZfTGEwJZkUCWhTS/YF+LgQ/eLqSkKM73v9eKKPbMmzUkUh8T2JriZzxmQoimTj2ff1gAT2vblFJo3GcilDIWvOivDWlpZYt5nEl5RfdzHz4zwM5Vdj7ebmHGCV6aXCpeoz5tpbxcwjibIHYbOhDCMsVb94EK5uebCfu0/O6+S0ei6nK6Jead4GfClBBGYzbLQzqZFW7qNn9A5MY/pC/q8t4yN+8tczNxjj+5yIO9SCLQrl3fZJEpqYpx9OJ2/LJI3TNm6mpNvPCUm3nH+nANMCZoKpn5y0QfF8CUddd+IXTKrKy388h/u9/Tt+f0HG1KCIaEEMl0oVBVeHGNnY+3Wii2SUiKgD8q4rLIfGuql4quxTH6inBgDYlY9XDJha2sWm1j22YL9a0Gql1xjh/up1k2EIyIdLTpeHWrg0BIxKBTsVkVapsMxOMisiRQGpD44cUtxGIiq1bbWLPOxrnf0vxyK8vjVJZ3C8D+CJ1kJIF+CmJHSCTT3NBDLHQJ4h7XCIk4KuLccNte9u4x4u3Uo6ItNe/p1DPtiEByJTahj85UZmdmf6y2qWQaOTLr2ASZHaP++BVnntcbvU1Uz0Uu8WgOgD6evWOXOcJb0CBDpQ5rSMSew1+4tDW7xMp9b1p72f3Na+LXa1JxWNNFcmmrntKuo1q6omUkomZAdn9n6DvEWyqpFuNc/sReY2LyXU93CUdMShPDPfZnKfupeUvkP7UOtAdFWtRD4waXytd+Qt7rr7/OjLm7+eCtQkSdSnGJRCwq0NJs4Mwrm6ga2S0EU1elSnDs2W0c1jVRwRESeeyeMj7f1S3WTl/YyUlTcq8Go3Zos5tNhvTHmPZBZfjfppEq/lJ+Z51U18sxCcGbEMGJc3K5YqQK5MwJdZnnyJ5on9bkqCeGyWnEvmAwkWozETsUNMjodmu1zLaCUxi6bmnaOZl57RcZz6DH9n7QW0QQ6H0iSUyCLftMhKIin+4289H2nnFeJ9eE2dVsZExplE9qrYiCiqIKjBwe4bLzWgkXpldIqRVq4x4jz/+1jKBPj9kmE+maXGQ0K8QiWgVx43UNyZio0NPyJknw4D9LkgsuXHDzPqKFKi/8rpxAmwGdQUWOa5bPGd/vZPIRvvQbSJmQ15cl5KP37Lz4TLeV3O2SOO0kD/rjA6yxVmaNb5wgm8uE29ABssqw/24g/m6EcK12/bETQzTsM2K1ygQDOuYc7eOwwwNYLNp3dzDcEN77yM4zy3MIeJvMzIUeClwS1aMixCIi3jY9g0dH0vxNfR06/nFjVTLecyL4/kDpzyp5BzOUW0dApNWj40/Li4nGRURB5W8/b8h6bDZLcQKjHy64R+tgzK32M9odwWZQaAgY+O+eQjoiev5xRT3RLNHgclmyUoeJb7mtgiKbzKSaKCWFElaTwmd7zIwdFGPa8DCSAz7ZpLm/nba4nWkzA2m+5vsT9zYXPVxzUr6V1H1ekwC7F+Id/Zp2XBa/yszvrD+jH6n0J/xfJtmsopniNZflONeKdgeL/gr3gU4IzCTbN7E7fgJDDdp31ZsPdmZ41dSQbtBzMZJMv/zM556Z98yJftndDrpJxEROFbOOqMqgVn2ffv6Z9GU9zuYHnO13trIN/auvFQXWbbKwdr2VsSMjzJqhRZgJ2BRamypo3JWfkNcri89rpXqojyOPTW/g3369kKfurMBml6kcGWHiXD9P/6UCgMFjwiz+cc9hT69V4bwrmrH6RV58wcWbq+3s3mviTZNKXBLweHV0dOrZst3MnMkhtu00sa/dgN0sc+8lTQhC95AipHxIfQljQE7xB8607CZ9elMtre6uCXYJus5P9f8F0I1w9LhWYlJecnum9TrzN2BMEeQJsZzNqhxb3YL0egQ92qSUhGhWZ2kVQapPczYLdp9COZcFPrOT0QvZfLx792PrxqiH6W6ts3RkRQB5JjR4DTT59Nz1ZjEqAlVFcayCwo+ObGdrswm9U2BDrYn3t1q55Q+VDKmJcvpCD8aq9IYnFhH41+2VjBob5szjW4hGRf741/KufSLVg2KUl8bZW2fE2TUpNJuvWcShcO6PmpEb9dx0+yD+fmNV8phR8/xE/DqiAZHjrmjB6ui/GMhmdT32sBCzx4b57Z8rGFodY9NWC39/ohjzMhfj/i73EMZuQ0d6jOMM3IYOSmjF93gMEBk9PsQ3zm5PWkjSUAQIHjwLwtwjAmzZbmbztvRIKIfN9zLvjE6MZs2yb42qxItVnFUyPoQ0K6DVrjB6RpAN7xfgdEtpcah741D7GPdWpmMS/PiRiuRvUVS55PSO5Hl9hYBLPebdzVpn8fZj6hji0OqHuAz3bSinI6I1N9nMManuS5muTKmN+bfO6OSBx0pwF0gY9Qr3v+amwCrz3hYb973ipqI4TlWp9l2tX2Vn+qzuTPfnGff3fUG3VS1TFKf97ppcZdblcO9JsT6nntvf4fmDUW5yCcwvSxjDwELJpZJ6L/sTSxlASinPEbsuLa1sIhZ6j1WceK/dIRq7DigV+hStifer+d0KaduAjCgZXRbmrqYwUbZS85UZNm5/SYxy9ujopYhiLd/ZXSVSv/XMEbJ/PuVm7Xpb2nkbt1j5zwtuJowJ4w+KhMIlzD/6gG6hT7724rioRMq6/agFPqbPChAM6Ni83poUxgDTju05VJj6EkMFCqee3knNyAh7d5jZvtmCrEJ1oYQxosW2bGrUc+oMP/e94iYQ6e49pn6QqcNOqRVKX35cqeI2lTQxmTrZrZfj0j7lfoj0xDG5lvxMXDNXkKeEhTkhnBMuGHJUSu7rYYXOmASYep0Bk0089yKYs002SX1XL24s4InVLk4/wsfs8iBum4TBCE374CcrB+M2S0woCbPPb2BERYwzjvDx6BtOjigPoPfFmWCJQxTqRR0WnUJZkURnk57f/KmSWTMCFJbHmXO0D59NwWBUGTI2xPbNVm7dbEFVNUtssVuidp8R0aTwyac2PvnUxvGndHL4qDC45bQKVlXB7BWJOBQCw2PceEct7a0G7vtjBYoi4Kk3suhXjb2uNGbqam9yLcOauc1iVpk8NsyHq7tbmEhQx/z2vTS5NVGcK0JFNtQL9yb/PnJqkFIOfgzTXFx0kgffkT7qWww81OV3/OlbDuaPiWCuiTJIErr836DWodIcNhLcrefTVQ6aV5vxtRtwFEh8+/QOZk7vngPxZcZfzSYSwjFtqfraViMPr3SiE1VkReA3KRFaoGed1ts1wjGBddu1b81hklFUWN9i4R8bSygwy/z5zHqclWDyy5gC6aG5Mjt5CRJCOdGYjxoeYcLwCFv2mdlcZ2ZcdYTNdVpdsWC+lzEjI+yuNXHuhBDDa6L7/dz7K5KzidjMYei04zN9Mbuse/vjR9mf1dt6o69325+5IdnI1ub1l/31Ne4P++N+ke3c1GfW/beYdK+AviOypB6T6FxmxjpOJdtS2AnRrZVBJdkR8xpT3BlC3eU4M3Z6X/nsMfpBemcu7e8e5a9n2U+9h9QOcCKWciKPs6YHKS+NU1kRp67eSDwusK/ByPadZjZutXDkLD/Tjm5h3+c9LnFQ+dqL496w2bW16UvLvcw/3oskaaGuPv3Yxh8v645gcdjEICOHRakoj1NT3W0NnTQ+zKSu+LaKCq0degrtMpdGOnlzo43N+0zMGRPi/a1WonEBc0DNHXqti8THn2a9zHShSBFzkWqt4jf4ZIwpvrqp4dpSSUaN6BLIae4W/Yh2ocv4P0lqWLau/430PnkvIYxzkSqgc03wS0zsS7hn5CRlwt6Bklq5K13f//MfFfI8hT2OrXTF6ZT1nDQpwNjRMX70oBbX9wW/C4OoMrc6wMPriqn1mjh+nJ9P9lnZ26k9lw8/0WrccZNCuAWJz7eYiYR0uCtitNWb+OZVjUyu6Wmdr91l4qF7ynkNF85CiV9f2y1233i7gBWvOwEwGBW+eXUTZYNjXH3vHu68fCgtu0xsf8fOmPnZn6cjqi1u05swzsY3TulMiuMFp3RiPSXMW+5BZE7mT1iNC02dFJo601wrCk2dlAitjL8vxsbLjbiL44zrWn0sUYEeaoFstyrYrQqtnvSq8c9LtOXDp08JUlgep6nBwJYNVlRVoKw0ztjqKN88tZNBJXEMiVMPgoUmwf7Oqs8UP9G4wEOvO/l4R/Zln9UMX76+hLE/LLLifRs720zs7TAwyh3htmP28truQp7Z6qasIM7J4/0cMzqgLRKUEmo3dQGJ3kJSZVqSr/i25i+8bY+RVRutDI7HMBtVJlTGGFMiMWxI33MQ+stAlthNJX1BCI1skWAyLWwDLd+9rTrXXzGYzRcZtPeTKZD7I3i/CMtyNgY66W9/6G30RPu/5/vLti13urnnRGQjc84KSF0CWQ9IgNJDIKfS3/opUwj31+2nt7znCh9XPjHMiGFaW566+qQ/IHLDHwbxzocFzJ938L7xXOyXz3EoFGLlypVs2rSJiooKvve97wFQX1/Pc889R2NjI4WFhRx33HFMmzYNgEAgwD/+8Q+8Xi/f/e53GTJkCB0dHdx+++2cdtppzJo1K+0af/jDHzjuuOOYPn161jwkfI5HTHoMqz133LxstG0xs/R5N6JOpa3ZgKJ0Nwh//m1d2rHWkBap4vKUsFdWs0KxU2L8sAhmo8rkURGGmrWKL1McZ6soUqNRgDZ5rXmEloeATaG0Va85+6eQ6RNrDnRN9kuZ2JbpUtHfBTyybs/0c04lw+c5l09yqttF/ezvMeiDfwC9i+ZUC/KBROHIJDLZ0T05cgAk3l/CbaLELrGmzorVpHDn95qwZMRf3brPiNGgEu9U+f1rZVgMCieM9TN5TJRPdlqISwKtPh1Th0UIRUWmHBbCVKaVnVWrbTz5vBtBUKkaEmXuMT7GdYUl83p0dLbrGTw0iihqwdjvvLkKn18rF4tO6MReGWfw0ChvPOtm9Wfpw1Klw6K07NKe5zd/V49rUHeDlzmxSNl9Mq6ylwfcUPsDIo8sLcbr09Hp1XPKzxt5aeKsXmMbJ3CrbYzas5mO32nvSG9QuP6KJorcB9+C1BfZhGhcgs27zLR7dbR59IQiAhXFEsdn+LRmY6BxR/tDfxYBSW3IFQUee8vJmxu7y8WkkWHKiyTeX2dl4vAoZ36zHX2KnssMO5nA4JNp1xu44YkSOkPaCScN97BopId/rC/m4wZNbY0qjXLDic2I4sAmYvVX1GVrXHNFvjhYnRToXRynWo1TxbFj2/Ew9KXu3734j/b3u8sUxgN9bv2Jv9tXqNGDSe4V8g6OD3KCvp5T5iIgqQx0CfZMckXzyCTV5xhyd9L68gce7I/36doA3ddKXRGvN5/61LQCNgVDh44Oj44it4wxZf5VNveJ/pK4fiQk0rHBwqhxYTZ8auOZpcU4nU4WLFjw1fI5jsfjPPjggxgMBr71rW9RUaG5K0iSxGOPPcaYMWNYvHgxO3fu5JlnnqGiooLKyko++OADRo4cSU1NDS+//DLf//73k2m+/vrrTJo0CZvNluuyObGGRRjAcIk9KGIfHOOXVzYlX1bLZjMuh4zT0bPg+gIi93WF5bpgUQcOu2ZZqiqNE7alWDQC6QU/pyhO4DYlHfo7SrV8uFtEKutiyA6tcCcqBJ03niaozV3nJ9LRoQnVTKGbKZQzBWzCUptNICcjSaRuTLlmYl+m9TqbH3IuUi3H2RYXSYufPNTe/Tz6siJnoVdh3MuESZ03zrLtTp7YpJWBvZ1GFs3wMXNUuIcwBhhT1XX/ZXBfTQMWo0qrT8d1j5VjEBXiXasAnjffS1GBzN5WPc+9WcSwQTFmzvPT2qZjzTobvnY9a94uZMMHBZQUx9nVYKR2l5nK8hhzjwgwcVyYm3/WwIuvFfLGOw6Wv+qioiyGooMp04IsqIqxd7eJbZs0C2FhgURLV2iDSEDMOdPeERLpZP/cAArsCudf2cQdN2k+zkK7yDjfdhIG9464O+lznIrb0EHJ9vqkMJ4wJsy5325Pq2S/KHIJBoMeJo8aeOeqtzQTqCq8udrGjjojpx7lo7xo/zsEmY33nhYDyz8pYHXGipI1wyMsPK8Nm11h3rc7erzv3vLcGRK54imt3v/GZC/fPMybrNsG2ePYjTL3Lm7AqO9+f30Jm0yf/8zV9HoTgqnD0Zki+GCI4ly+pL2RsOL13Jbd3SLbsHmud9KX9TKVTCG4P8/ji7IEZ4rh/bEE54qnvD9kC+PZW+zuzDxkknlPieOy5TkRQclq7ztmca6Y7Y5ovKusKTknB2aSKZAhd9QL0OquP99bxt6mdINXavjH1DKXa7GUTMxekd07TNRvtvDqm91zU6oqY7ic0kGILt43AxbHH3zwAcFgkGuvvRajsfuB7N69m3A4zCmnnILBYKC0tJT169ezbt06KisricfjOJ1OHA4HkpReaaiqymuvvcYZZ5yxXzeRbaJQX8el/rZnGYazhkRqGw38/u+ljB0a4ZcXtjC4PN7jGOi9N5na402I4Uz/pco1se4JeW4zsiwgDzGle+1khD6THQaaRwhdhdmIu8XRw5KcinGYA12Hqc/IE71Okssy6S3VBzlXuianEaPDmGYxTkzUy8xj6hLX0DWhsCOKvKYNAwfoi9zbZL0cLhlvNjp5YrsmjL93dCfzxoWQEt9qHxrdZtY+YadN4bChYWKSwMjKGCdMCSAWae4Lj/7HyY5GE59uMXP8yCDvf1TAoul+pg2PUNdm4JH/OvF7ddQ2Ghk/LML0cWGWv1HIU8vcTB0dZtGpnSyY7yceF7BaFNZtsrBhtxlPh57Oju7PW5K0xW8cxXFqSg7dkJReD1f9vIF/3FPG8gc0V4QzHn6fj+yjWc+YnBZkYbyFkSf72b6igPO+3YbhELTHB2MiyqGgtVPHk12uMNPGhg9IHKeyr13Pjf9On8r4s1vqMFuUNAsxZBdjueLi7u3QvsGbFjYxqlQrS4nh95HuCM9tdyEpIBxAqLBsw8yp27X/tfd5oO+0L/9OGGDM42SD331OJOUxZJuEl2khzOZP3FvMXcguCLN1JAbCgYZ7218SLjeZeUns62t7byI52yqO2ejPs+prsmoq/R05SRybuS1XOc+2iFl3mc6+WE5vLmo+i8LeNVY+32lGBcIeHZ5OHUWFMia9yndO9xCyKnh8Ol5/xcHeJiPHz/Szfa+J6WPDWEzZ3SUyv6GEcE/cg3+XkU1bLdTVG9mxy4zdLjNyWJTLzm+lsjyGCjS3GKirNzJ8GDS0Z83+QWPA4njdunXMmTMnTRgDNDc3U1RUhCGlVauoqKC1tRWAmTNn8sADD/Dyyy9zzjnnpJ27YMECli9fzhFHHEFlZeX+3EeSzF5PYtv+sKNOu8ctu83Ujw9TXRYnFhcwZYlnnKvgZ27XfnevyR6ygqPdyGalmFdrnWzYoJnZLp/WTFEJvNToZMaxPup1TkocAkfVhNHVRdB541SuSfTqVfyV0DHNSH2JiCNkY/g6Id19oyu6ha4jmhTJqfQmOrMtDZ2IWgHdFmqjy5y0Bod93R2gsE8iNCI9ukVCGBtdZi2U3AhH+gTGhM/06m6XGZ3T1Gts5D4ZQEQLgL1BE0u2V1JaEOfqUzsoGiKl2YJ6q1xTK0uTQeXqRR1Zjz/qWB/yu4WcdIwXT7nCnCkhnv7QwfFTgrgHS/xlavdqb4nKccKwCE+tdPDJZitr77BwxcJ27GYFuVhhenWMw1JWiGtWteV0VVSeeLgMX4cec44OZKKSNcb3PwKEPShiB37ws0buv6uc+r0mvP+xMXnBDqiColgH9rf8jB8bYu2oMj6TtFXzynVt1L5pRadTewi3/aG3pdlzkRBaB3s55r4odctcdFo7Dy8rYuKI/bNOZ2N3c88exm03VPP977UwaHK4zwldmZ34hFB6ZYsWh22sKQhdffqEdXFquRbJ5d2dNk4wdn8EmXMwsk34zb6IRLoxIeFSlqhHEzP/Uxv7XAaSbG1DNlKHlVOjR/QmkHOG1+qykpn6cWw2cgnj/k5c6y0s5YFMUDsQEpOec8VF7otcVuGBWov744rSW30wEKsx5I4Dnc2anPguEtEyBkpmJytbuc+2TW3W8/TTRTQ265k9JoQggKs8iqFK5cHX3ehElVMCPl593cH766zYLAqioPLaqgLmTgmy4AjtoWQKeXuw2/o8iHRDxcYtZv64VFsJc+okbf7XScd5KSuRerisOQqijBoeJRCyfLXEsSzLNDU1MWrUKO655x4CgQCjRo1i0aJFRKNRzOZ0wWKxWGhq0kKmFRUV8bOf/QxFUdBntH5jxoxh27ZtvPDCC2nuFvtLLjG8q9ZIMCQyYUykTz9BgDmTQ1jMCo++6OYfL7r4x4uab8uPz2klGBb5YL2NHy7W3lBfy2ZmwxoS2bPVyI+fLemx7741Zcm/16zrdjfZMSJAldNKkU1m+uRo2jXqSyT+6xoELpjmasARFRnUWoC7RcS8Lj1CR6YrRWxX9v0JS3M2t4dMYp2RpCi2FGrvOFMkJ/YlLMexzghGNBGcCA+XzSc5dbJeQiQf1IgWGXhjOv64sZo5Y0JcenxnsjPT/9WW+p6tDDBhTIQJXYuCPLK0iA1btNZecqg9JkUmG0oUfnRcJ3smBXh7rYU31tqo7TAQjOk4ea6PE2YGWN9kYOSwKDaXzJgJmli+9oZ97K01UhgUEYTc1rDYQXBnKAiJXHNJCx+vtWkrm70CTreHhg4jUIR+lhH14xCjj/ycuves+HQgxQSu/kV9v77NXByIsP2iRXEqh4+PMGlEA8Y+tML6HSaef6uQ8y76FB29W6wkOfuD1JdKPYZJc61yl0m8UMfIkigNnXpW1ds4olKLPSp3SIgiNAf1XdeGYFTA1uV6lCoKmv06Plhno6xAwqhX2dNuxGxQOMXhzxo9xpBlxC3zd+YiAb2JgWztQ+aoQi6/y1z05vuZmOSaemxvq4Gl5glyC+PE3721MdkE4JcpijP/7m1uzsF0kchFXwI5W50wUL/jbPedINEhzLzH1BjKuZaDTl12PTPSRa5R8sT+TCw+kc/XWfnPaw7GVsW49NutFFqVtGfz4OsgKwI33F/G9LFhvnuShz2NBlZ+rHWWvzXNl8xXuNGAQaei06nsbTLS3KHHGFOxmlQOGxohFBX4aJ+N9zZa2ddgZMr0AMed7MHhlL+QqET9YUDiOBQKoSgKmzdvZtGiRYiiyLPPPsuLL77I/7F33nFunHX+f8+M+qptb17bu+7djp3q9AIhBAgltAMOODiOox53wAHX4I4jRw0HR4ejE0JLKAlJSO+JncRJ3O1de+3tu1pJq64pvz9GI41GI620thOH331eL7+8kqY8057nM9/n8/18AwEbV3d0yYQBURQRq3hIXX311Vx//fU89dRTbN68uZFm1Y0dTzbx8A4/KwYy/O3bp6oud9f9AX53W9j2t/7eHKNTzuI0qEBtHVK1N/xMVuADX9Cj5JKoIQkaX7/yKM5WiVsHQ0zGJe58urThJrdCMitx36HSd2cMpuhZKpPNCTiOqWSf1Ghvj7Fhe4J0QuS3v2ll1RvmOKspz8pjJjJcqF5XLQJshtnPuFoaXS2phkGSHa7K625stx6dsh1Br1bgBE48ge/2TCcTGRf/9sopMgJVNY1mmAffehIQ/EkRWYZ0RiTgV9myMUVXUOGORwM8td/D1jWl47Or2LTCo7DivAyD0y6+93AzgzMSZ61Lc9Ojfu66P8jfvm2SFa7Si8DNvw2z4yk/u9ameNkHJho+J41CEODsrUn6enMcGnLT3ZHjyDE3kaiD+x8u9Be/BdAHjDMunWORjY57PjyfpHYhqDYge0zHbjcAp5rgf25sA2Dp0qWMPQXUINNdzZXuCO/6uzE8y7IkTDpC877mK/0McEV3lLm4wDeeaOd7u9p4+8AoX9q7GIBPrD+CR1T4yY4WfrKjhWVtWS5flWAuK3J03MGBqJepQmXRrqYcQbfKTFpiJu3kpzua6Qzk+adzR+lokqtKoawyATuNcq1KiMZ68+Fklqt1yfUl31WT/tQiivORR+v1nW8ZY7mTVUmvHtRK+mtEjrAQmCPp5gp5jUgmoLyd9Vrg2S1nF2G209zbVUhN+dSyqny1vJMNTE45+NqP23AKGq/dHufcVemKdQCue98YT+7z0t+T46kDXm64PcTZG9K8+zUzRMYc/OrhIF1hmaeOeDgwWphNdqp0h2V6WvS+aHzWwe8eDxBJSCxZkuWKi+KsXpEpVjflNCHGsIDIMcC1115LX18fAC95yUu44YYbuPjii7EzvpCk+m7e9vZ2zj//fG655RbWrl1bd5sic+eRzNY3FXnRRXD++frb/MRs9UF40WKJ7du9KIXIy9qBLAGfwu5BD4mUyERG45ILNXKywMFIGqe/9FA48gKOPIi50vZVl4AolT4fG3fxzLCbc86BlT05DozqtPPG9DnEDhSm8dpUrrxMI54WUVWBC5Yl8fh1g/50VuS+PT78bpWAX6ZZ0ouUTCQcxGZFHh/SyOYFWoCpH0H2rAT7WzSErIrmFlFdAnM5gUCvjNRu6Xg8hetVEMmpXVmUbGmQVTIqkkdvoyOoPwBaViEXKxDcKpcu0bMWrnwHkkc0ZmJRMpUPrrHtapDcDgR3/R2i4LG5xS3HaLsfj5+3vApGkuv1LxIgO7XaUdVCJzpr+VwNybzA3oMeEgmJM7ck6VkEwSYHc4KXaTnP2GyJ2JrvJ2tIedjlpGOFh44VMJ5Js3mLwoYNAl6vykShMYcPeXB4nJxzTmGde7J0LCuPoGfQB/Fssp+J/JW1G98AJDesWq3/vbFFj0xvPkdiatLJyLB+77vdKpu2JIvtbQSOE5CBPG+w3BuOGmOprAhkZQFPVuONr3CSyopMTU0RU5cylH9x1fWc7XDOOSU2tG5jiib3WtQh/d7L5gVmgOHjLo5POzhvc6rYrrL2FHL5jHtQaFU5/zzoXetiz7SXI94c13SBV1JIeNbypkUSOVXgWNJDWhE5KKikHSLSYo2Vi2GzQ6PZI+MXZfKqwMG4lxWm3e3yZ1jcnIeCPF11Cfr7U617YxZkZ/l5lY1ntbBe0rS47T2TKK2TS9R+ScsVuhWXXP63+XeD1quFZ2p24iUVkiVrm2YK7XDki4oV/bxLNitQODeNwHQOZZsXK0e+8jw68lTcA7VgtKli2RYQspY+39wHG/1y4Txq7jqIUrL6T3bnxnzMFc9c4dwUn6vCPVXr2QTTcUqm47PzFsgopWO0QCsUiRGyKjQVPidBzQtlbZZykMiI5CICeUUAp159VZI0JBHysoDPo5LKiKQzIgeGXQhCqfBOX1uero48wSYVQQApD/fu8rNkBWxfnSLgVRnKm86T6T7QAE+Lk0ePOGkKabzzzVmaPCpP7/cwJ4k422FCFTjr7DwbcwKprMiGJVm8LrW4PQ0YPO5C1WBFXw7ZqTGbpOZ1tEM2d2LBr3rQEDluampCEIQyXXFHRweyLONwOEiny9840ul0hdSiFi699FKeeOIJ7rrrrrrXaQk8hN/XmJXbfOhshpVLdPsjTYNAVr9x13bonwVBt3Z672d70RJJfB6VXF5gSXeegUCef/t5B6t6srx8bYzP/amDb7z+OEFPYZouLfIvN+rZ/P/y2knCTSo//LVeBc3rUrn2vDjnrEoVpyON/ZnhTCusO8vG9qYD0k0SU3EHM6MCn7tTT8Z5/FGN97xuhuG4xK13hgi7FCbmnCxqzfPP75rUyzwb01uOwrX1F95q/UCuPMIMhZLTgqesYp4V1azcrNILs/4YansnG8tI4UrdtLltFd81GEm+7qFVtIUV3vzyKC6HRkeLTNqUzGBEk6qVjLWiqg1Oh4vOdXn8TRozQy6++r1OQj6FN75xkrZ0fdGH5UFYqTj5yM3dPPII/NVls2zZmsLp0O+fpw95+MXNelLhf71lnI/+sItHHoHFSzO89mNjxfYZ7XcPvZTOBsoSLwh+WNUFbDyxzfhSYrmg8wUIT4KqEeC8DO/5XjdZWeDKzQnecKE+deld+RH2jNxQLHNbDf/+SMmC8syLR2ju1CUVvpTI+JyDf/1WSb51133wuQ+MEWxSTbpe0+xXWkHTQI7J3HkkyA+f0aPY375qiKA5CccJv93l5+7BTi7siHLXZBinqHHZqjmcErQ2yWSTAoenJDKySFebwqs2xXCauzQTZ8o7638ZrlsyUOWesSvCYOckYQernMPcRySPX1V8pqpGrQttMu6HuqzLGjg3daFwH1YremG+T6u2zz7wWBklrSVva3EXCdOC3TIK7ZjrsT9H1WZHDnivKn+u6jlmAw7TcVqPz3RMZighJygmi9cWd3EcNl/fe3f7+OVDQSQRgj6FgFclLws4HRqjUQfRhMSS7jzTsxLNQaXoIHHdmye44YEgTw15ecS034HeLIMjpQdh+miWf7522tbhJJkS+cZ3OnCpKq/aPsfytfqx3fXHJn77ZJgzBtI8Mejlry6b5aw1qZqnaPHq6r/Vm1ybSHVwfHygrmUXiobIsdPppK2tjeHhYbq6dEIXiURwOBz09PTwpz/9iUwmUyTEY2NjDAzUfwBut5urrrqKX/3qV3VHnE8l7BQgBlEdm9Zv3ocKZQ7P3ZDkwV0+flS42faPuvncqE5O/YWBIx+UyElisRrVL+4NcHDaw3teMs2W/mypeIDN/sywE/Ab33vjCoslhe51Ej9YN0JmUmPHsJfv3NRCKqMf0ESB7B+fcfLp77XTFZD54Pbpsn2UTXP1+6HfrxchMSOSRWrx6P8i+nGnh2IVMol0XEbOqWX6YzuYq+tV80K2FguxI8l29nTzSTCsv7+58xjfOL6U//huB5Kg8YYro2zdnigOfgaZbESTaEeQlxWKfPhSIr/a6cfrUnnrpVG94xPKJTm1pupueTIMQNAl87P7gnz3zmYuWpdkZk7i2eGCu8DrJgkvUvjwNdN87qY2ho942PnrZi69srJi5HNVke5E8FzKKU7U39RANS1vLCXyu8cDCAJcsSnBv9/YTpNHJZMTyMoiXpfC6y+IN7y/f3jFNJ+/WSex6pQTv19FiEjct8PHT+4LA/D2TVMsCeX41/t6CWQ1PJp9IvHEnMTf/Uon22vb0rxu7QyrW9LlxBiIjCn8ZFAn3fdN6vt4zZYom3ozLG6pXZFyIShvq1Q3QbaTG9RzT1lJsFW/bfzWa5rezuaFmtuu5gxyMlGPDVk9v0Fj7bPtt+bL+zD9Xg8TqHUP1ToWu+MQcxrOdPm4Wm3ZYhuthNgsB6qRAG5biTaS1W1SC789Oejh1if8TM9JfODqCMu6chW8ICeDogi29qIZP7zyijibR9Pc/1QTmgbLV2RoalJ53bUR4nGJbrdKyK+QsZzGObfKnsea+O09Qbb0pHnr2bMkenW7xR/cHODpaR8Xrk1w3x4/oqDR5FEb8tw243RzEmo4L/yCCy7gjjvuoLm5Ga/Xyx//+Ee2bt3KwMAAPp+P3/72t1x88cUMDg4yPDzcsD3bli1beOSRRzh69GijTTtpqKeDXNyV57PvH0MSNTI5kbaw/uDs3OuhJaiQmJT46q2tvO+iaUQBDqQ93PKgn8cO+hAEDUlU2Tuhz1XtPOzjrBWNV3czC/mdccVW2O/pEDi/I8O2xaPcf7iJXz4VIpMvHd/wuIvhcRdvPLiYr3x4lGBWK1ZGsqLiu8JnXZOm65j9LR68kUyRJBuE2OESi5Fi0AlzLbJsJsjWUtRlbbK6blQpalILdsT5wuYIWVVkhTJLwufj63f3s+GsZNnAeCKaRDPxFGYlcGu89vIYizry3PhwkFt2+vnQy2cIFSQt1s7Tinf3H+fdW9189cFW7i8Qknt3N/HWjdO8YWAaze+gr1MhAyxbk4Wb9PXuvi1MNivwkldEi8dUbfba+lw8X53Zc0GKTxYZrme7ORne/51Sefvbn9JHliu3z3Hjn8IAvOqcOZ4ddtPfmcPvqU+X7YwrnNGc4sPXTPP5m1v5n+920ORVyOZEVrZn+Y+rx+lvzXFoyoWiOWj3y3zvzjDvecmsrY1WJworO7IcmHTzrxeMMp2S+NaT7XzxMTefvHCU3kAhj0ErPefrQwn6Axl+trMNVYtWkOMThd2LozN+4lrU+VxNqnkQ+1JihW7YlxKZYWHa1Vpo5BjtyIpddLjeJDirDrmhMtPzEOOyZOsaRajMv1dzQ6mFjF+/VxqFXXCquD/zfi1tsOqqi/ULoPKcRLIwk+VBpZMf72jmLy6McebyNO4qsj6XA3DY/+ZJwFKPzNIBmUsG0mXXPeVTWWrpxxMpkT/sbGLsmIvBERfdQZm3rJ5m29pc0Yc8Oa5x19EgPpeK16XxX28ep6tZPxd/DsQYFkCOzzrrLOLxODfccAMA69at46qrrsLhcPCWt7yFm266iS9/+cuEQiGuvfZaOjo65tliJV7+8pfz1a9+teH1FoKxCSeiqNHZLjc86Ib8hUQPX6mT2N6X4eH9Xr5xmy6YW9aW5V0/6yWVF7lsgz6nomkCiqlM66UbGhTcFGCXtWzu3Mp8Hz1w1bo5FoXz/Ncd7Wg2tc+PTThZtihHPe/qdoOSEnIiAVKLG3+LB9dgDIbsex8zUXaHXUUibZVamGF2uACqFg4xoyKCbC4sMk9FwUulEf1UyCl6/Iv45S9a2LglyfikE49HY8nmJM02Fdxs69DbeFEa99tP7w5yybYk3W0yl2xLctEZSb59Uwu/fCjIX22NVGy/OGDYdKgv6pnljCUZzuhKkVcEAoWonhJSyReuqyDAl/9hlMSYxCd+0slD94RY1pFn5bnl96E5emz3bDzXvsGnihSfKiJcLx7aV1nK+X3vmCA1pQ+u/b05Hj7sZXDEzRkDaT5wtc09YQOjf9hCih+/JcVsSiKdF2j2KcQyEvsn3Pz8mTDPHPXQ5FI4rz/FHfsDnLk8zVkr7Gda/G6FrqYco8c1rtvTy0TSid8l05FNQl5Da3bzvUe68DhVJDRUp8RViyI8PhOg3105316LxFgjdrUqqDWSxGXdtjHgzwe76HI1WzTrMo48VaUzC40SW2cNq2E+slLNkqwRByZzBNR6LczfSbF8OeG1IcplErg6nIXM+6+3vaDbpM31SBXyCtUlFOUM5jHVemx2Y61dBNlYx3wO7EixEaRRNXhM6eCmkXaQBP76ilm2DJw8i0fz8ZpLWxv3yfBBF3fdH+Q1W6K8ecMsA5Ju6ZYJeorH3ZLLc/XyKL8/FOa2pwK8+ty5iu2fSKXL0wELchS9/PLLufzyyyu+X7RoEe9973vr3k5LSwvXXXddxfe9vb185jOfWUjTGsZNt4RZtjTDhrUZujvzCxqAPQnduujxcQ+/vyfI8Rkny9qyeJwaH/xVL+v6Mrzvqggup8btu0p3jNup8u4XR1jVe3KKMlSTW5ixsTfD68+I8rMndFs6Aa1IlD/3wzb++kWznLe6imjMgqIdz1CijLBZ9clGhNgaCbbCTJjNy5hJszmKXIskG7CSYfP/1r/tkJvN4Ah72D3thWmYOw6L/RlmBBd/+GMPkkOjtyfPxdvn6N2UYs6vsnhWqLv2fC4vkMnC0wfctIZkXE5dzvPS8+P8zw2tsNVmJTstWwErW1RWFoRtHodWMWgYU14el4bWWbpHbr41zIfPrf6S9lz5/z6XhPv5JsUGmpsUelvyrO9Lc9su3ec8Niex5cwkXzoziaLAyH4P3/pVK1dvKw1C1ulfqE6SBAFamvRl7znYxLcebC3ue2VPFlHR6A7lee+2Cb57RxtSapatm8rvs6ws8Oyoh5wi6ob8Sf3e+sJlx3F69Gf71kMhjsTdfPWdupb9x3eE+NtHVrC8OcuiRY1dV4Og2R3TQgil2TbMrmCEGbWKR1jXq0Wu7QjCfG0/GVKTeohJNau+E0G1dteKLJ+wLec8vvXzHZPVmUNManjGS2TUSm7taxeUL1vXWbQp2DWbd3L9xAoSSFxz3hzbOxK6vNMUYzpV1naeBNz2ZBO/eSTIX26Y5qqlMZSQE5XyFwIplkcJO3nj+XH2RL3kNBFBaNxl6HQlxQZOgt3+CxvvftsUf7o3wMghN8sCjXW4YhQOjbmYS0v89N4gLT6FzrDM8Rknh6fdnLshyWcum6CnRSadFZiOSzS59WXe85JZ2oKnzh6nlk7qZRvnWNWV5d9u6SqLIGvAN29v4Zu3w19cGOVFm5NIUQVRrOywy6IBkWypwl8BZgs4A9VIsd335qQ9M2k2r2OQbStJNif0mfXJ5raZl7f7zQw5muGbvY/R1OxAFEqRDXUZTGacPJML87tfhfDfHeTqq2fxdOar6hINGETzwFEXj+7WR7Kb7wvxhQ+O4fVoEBMRtXnkFHVo2QwY94N50DQXs4nPOTi418OKNeXHP5/2+GSSWfPU9KnA6UKGrdiwNMvm8BjX391a/O6HP2+jTZoinRP5eqF8/SUrEizrytckV/WQxkdH9DyJ5d1ZPnh1hPd/pwtVE7hqSRRESMsS/31vGz/YNEIiLfLLh4MIgsbIhIOcInJOT4LeRQI/6BrEJWmIpkmopyZ8vH5rFF9Kb8dfb4/wlhfHkMRTo6NtBI0QzlqEuVbBpwpZWw0tcbUo68lAVbvABiq5LYSE2Y0TVVHoz+YrQHWitpxmWPXpVfdrifKafYfrlpMUiHs1zbWZGCvRLPsdrXx3pp91S7O8dfOkbc6Tef8nmyTHUyK/fjjIpzcPsqgpBxHKtM9WCcnPdoQZnNbHoKEJV1mQr57iKqc7/r8nxwCXX6RHY4wcy/kGZ02De3Y2ccPt4bLvo2kHzIDfq/CPr5ymr62kqb3zmSa6mmX+56/HT6jIQaOo9gD1BxWue8UYe8fd/ODRgmeSiSj/5L4wB0ddPHbIx5dePUIP+bLISxla3GB5A3Y1e3A1e5gryCrsEvLsSK8V5qixFfX6Iy/kNyscsSSa6AFTlT6AdjJcyhwX9R/jxvEefvrDVmYzEgNdebasT3P+5iSiqDufOGwuhWC61c5alyKU03jqgIfv3R7m2jWR6tOKdVb5g/J7wDxoiiJ88+Mj3PKgn5vvDfHDb3WyfHWal72ifH2DIFt1lNa/T7TDOxXR6dOVEFsJVjwv4rUoib7403bOGCjN4jx8xId4m8bbzplFvuUckF6yoH03O2U2Lc3wwatnuO7GVlRNwCMpfPGxrrLljk46+eLvWkllBF48EGNLW5ZXr5hhTat+T3qsGsdIluNzLp4cVDh/WSlj3ZOwJ5PzRUgXUlhpPtSSZsyHWtPr1SLc8+3rZJHheoh5vdsB+3Nfz7mq+wWkhlzCIMS1kqitqFVMpBqqXQ+hSUVKVibINXKtlJATjIqv1iQ9EzFWolmenPLyR/dGRrIeXnVenIvWpRDnqmy4Cqol+jaCp/e4WBdK0p2NQ1PphUSK5VEOxfSXlBY3mT4Pjx/08OARH1uXpfEGVJa0l85NugkSSZEmn1r24mzghUCM4f/IsS3sBulITGLPsx72j7p47GClRhA0JFHjzRdFuWRDpSzh4nUpfB71OSXG82Fxc57FzXl+sytEPFPZGT52SD/Ouw/4ef1WRZdPoEeKzf9DyfHBkDG4BkIABAD3bIZYgeAm52SaAuXV8+zIbz3E2YpqZLma80U9MFvH2cGISudmM1xDjGsEiLld7B1u4tsjm/nZbWEEdBIRdCq0hRSuumyO9QWP4XUDWT7/yWOkxpwc3Ovh0ze0EUuJ/O3KEc4IV+ntLMR4IQTDjKu2J3jJeQn+5jO9HNrn5eFwkL37enn926ZwOjWW2cxwWDs4IxFpvuXMy9tt488d5gFZVeF/7m/l4aGSKarHqZDJ69fswrVJtq9JIQrwmvPiNLlVPnVDO7vH0mzoWbgGcUNPmq/c284vHw6yf1K/vzOK9T7R+MrvW3CjojkE5rISb1pf0jrHsyLTaQctHoWwR2Ey6eDXR9qZSTt4eCTAB2Mle81q92ej0oHnMvJsR4Ss0+tmVCvrbHy2ev7OR7TqkVacSOTQSqZrSQWM5Wq9WFQjp0biWQVa3LYEuSoxNudZWP6uto9a7Z33JckuQc4G1nYaUW7J7tgKs6vGmHEo6+eG6X5G8dMeUgk4FH58T4iuTJoNK213VxWNEmO7Z2k87mSRaJHWWQuFAc6Qk8FJFwOded57VaSM02T88MxBD//zi1aW92V5/+uniSck2puf31mjheD/yPE8iMZFZhMS132/PLEw2KRw4ZYkL7twjiNjTm572K+/JVVhv37v6fu29LXXjvDPv+9kqGDH1h3MI6swldA7uldcmICkbulmrqxnwM5KzYAUduMNu5Hc9vIIb9BRpkWuRZjrgZ2u2ep2YSXRZp/lapILs5fzfBFrDzm2kOAj3ifoFNK4BQUBiGhu7kwv5b5HQwxsSBcJ4o9uaGXXsz7OWpLiqv4oZ3UncMYady8xw9z51xNVEAR496tnOHDMTRJYviTLN7+kOyhsWp/ira+vXci+WtS3FuE9lWT4ZEeMT3Qq0zwYjccdzCQlvvdwC2NxJ60BmaBP5Zqz5vjVwwGGp/V9PLjXx317dOJ8yYYk3c0yL98Q52v3tXLZqgQb7fToVWCQLSmWJx/Xn/P7d9u/5IecMoubMjwTDfC5S49xaNaNS9QYTzi4c18Tj0eCTKcd5FX9+l3RH2PvtJeplP7Mfuis8Yp9w8L9ak8mKa7nOto5H9QbNawnSa6ebZ2o5tjcHjuiXcsO1G4bxm/WvqRaZbv5SlzXcw5qOlYUvrc7T8bxWpPkjGXtjr0sQU4qb6tdBNsq9bDmsxjBFGM5c6TYGEfuyixhv9pMtyPN2LSLdOEFNZkTGY44cUgarU0KYotYbOuxWSfxjMi67mzNpMlGkQ9KtHYo7In4y47NHPBiIMSd02EevLOJvCJwYNRNKlcqEQ/6vbFheYbLzkxw5+N+ntzn5X9/18LWNSn++pULqPD0POL/yLENcjmBL/6gnWMjOmlqC8tcdEaCRZ15lnTliSdFdh3w8rIL9bmPpd153vUq/cKfrtO4VlgzbZd35Irk2ClpnN87xy/2tgACNz8WYOdBDx98RYSOPglPwd/Y3GlYPYrNsVqjo6gWwTXIZi0JRT2YL0JcixRbMTcUnzeBcD6sksp12D1CCm82Q5RgGTG85Pw5dj3ro90vs32RzQ3UgITCDtZ7slo28aaVGQJNKiOpHD/5ZSmauetZH4/uzLB2VZqA//R9yYOT9/zV0pWeCEaiDj58U0/Zd598/RQBr8rBMRfD0/o998qNUX7zdJhPvGiCT9/eyY/vDXHdmye5dGWCxc05/vWWLmYDTaxptzgB1MJsntGkk1WtWTwOlXhG4qp1ccZiTp48rttKigJ0BmSm8voz8eG79EqoHoeKQ9JY153hbVfGWN6dwzWn8L/3hohmJC5dGudFW1JFnaRCJaFsNNHsVEWKjT7PkELYakhNEcpi4nEDJL+CgDXIYeo5V/WSo1rE2O7zfDAIsrGe3fkxty3j150hzHZxhrORNSJbU19cp3OFuS0NfVclkm2XtG3npQ+mfBbTcubvQR9vpLCbd8we5fwjR5hTXHxV2lb8/b936PImnyDjklQ+ev4EPRtFpFiej96sl2n/5uuP4+k4udPQG5dkueG+ELkBAZepoq8UdiO1ePjSjk7GMy5edEaCnCLwjstny4ixMZ4IwGuviPGay2KIIvR2TJKvXeLgtMT/keMCNA1+f3uIu+4Pln1/2ZlxXnHxHG5LP7Vh+YlF9k4XGANDs7c0QHT4ZTYO5PjFXv3h++3j+jn58A+6+MAbpjm7EEGWKL0Rm6OuVijRLHSBtz+El+qFQgxibI4em3XK8xHnag4X8y3fSKLgieJMxyTXjy3i2V0eupfmyeQE1GaZvt4ccxmx2EEb51Vq8dgPClUI84lEEm69M8DNjwU55xy9a7/m4hjNAYX//V0LN/ymBVHUeP0rI5y5pXYFpOcDJ0qKn6sp+6C7tJ/3XTTNuf0p8l79mgUKz+B3/+IYcxmRvRMeHj1QKKg060SKKQgChAvWkWlZZM+0h7XYP3eTSYkvPdbB6JwHVYOcWhmpv2V3kM9ccozXroDhmJvVA3namvT93HuwiW8WXC3+882TtPiV4sSYM66AAH91cayhxLKFOjHUkjnUi2pRUTNBbiRKbG2fOWmr2mxNI9uuh4ybI8Mnoqe2Ll/dJlQqkF1jHft2m9tc+TIulbz0rbpcE4p9oPGF1cLSnOxWK/nNQKQkDbB+Z96fFpArIr2241vhezOsn40iVWZ5nqHd5VCMviNTyJrAVmmSnUppdvplqT28xHWMB+RFfOKetZwznMKtlMbCA5NuNnYsfHyyexZaAwrtfplDc17WhvU+XmrR+45DBHlkxM8/vGKaDUsqr5VdAp7xotzXWX5NTlaOyqnG/9fkOBqTiEQl9uz3IkCRGLeFZc7bmOKiM5L4G7yAxk1yOkeQ7TrNqzfEuenpIDlFZMcxH++9aJq/u2SKbz3YQjInYiTr5WZFfjLVzJUDcTooVcjLDZaipGZSaXQKglsqRgVcljdxc+TYjPk+Q3UCXW+yH0B4sW9eImx4MZv/tossm5ezq/Q3rXmIZ0W+8geddLzhVTNIElyyOckd9/vRNrgRZisj83aYLyJUDdXu0S0DGZ4a8nDB2hTvPmukuOzirgm62mS+8vNWHnzMX0GO7fTD1upjjTwPjZrIn87PmhUBr8b67jTPjnn5yr1tbOw5RlPheexFwSmqTI9BNAP7Jjzsw8Py9izpvFBMl233K/zkL4f5yv5z+OT9vSwK5Lhw8RyvWBkt29f1j3UxFHXTE8hzzrIUkaTEht4My1pz7Bn3cOHyJJlphSanngsxoM6B4i7aRl20IslFK3QNYj5QO7O/UdK3UNnAicgNzNFMMyGu6j5QxSbMTkpgkFPzvXsiL1x2bgkGrNFus5uAldSaYT5eu75iPkmFAevzVo9kw2p3V3aueiQCo+Uk2Tq1DzYkGcpJdTVbNxv9cLW+VYlm0bqUioivnfuS2U4UqluKGvk35mMxt8mBxl9ln+TVuHGikkHCl0mAy8H52nGelrp4aLAVj6jwutBRnILKXQe62Li+3O+8Vr9Zbx+5qC/PQWeIVRF92w/lO/jqvkW0NckEvApq445t8+LwkJuvfld/MfjCp45Vdel4rvFnQ441rfR/MikwMu6mqzPHHfeEeOgxP/1LsixZlGVqxsH61RnO2Zbk2b1eMlkBf5PCirY8m/4iy8olJx4tfCEN1gYcInznL47zb7d0Mjjt5oO/6uFrrxvlzCUj/Nftbewa1fWJX/+j7mzhVRWuXZop62iMziEbzREd1gmUt0AQtaxSWm4ghBSpLPtsR36Tc/p3RhKfFfNFls3ktBoJr0WMzetbia7detW+M9bdLM2wwjXHwVyQC9cmWdEi0xJUOHLQxVjcyY6DLs5sa2xWotFocTWbp/6OPJ96wxRDebVs2QGvTHxKZM9gQT+ngCTZO1ZU21+j7auFk/0C+lwmeh2cdPHsmLf4eS4rkcppPHbUx2+fDpJXRRb5c0yn9OetM5DnUy+dqNiOIMCZPUkuOW+UWFbiazs7uWpZFKeE7hrh8HN8zsU/nT/KmuWVx3epGkURnHjbJVSkyuIMVPcFtmKhkdZ6UCsJbr7tNloYoqxNpnNRTbZiX3ik9rNYrwTGIMTVjqGardyJRIuroVb/Yia51XyTrcWpzCi+VPdIeIISHqiI5lZojy2za2Ai06bf7Ah2LdjJJ8wRY2swxBr8yNlEks1tKUajjc+m/bnDLjrRyEbzNJEHUy7OX/qH+fB4K5ogcOWiCLdMd+LWyq/bfAGFegMUZ6zO8OM/hHn5Ng+CAKmom5BP4XNvm7AlrQuphmfACKosXpTlyvPmeORpH5/6fDf/9pGximVlGSamnLQ2y7jdGrncqXc2+LMhx//xxW4is6XDafIpbFqX4qHH9as3dNTN0FE3V794lo09OXwpkRdtPDXTwyfDVuX5gEOE/7h6ghufDHJo0o2q6lMjH33RNI8f8fCle0rTPvcP+XnVprg+LUapY6lGDpWsXJG0YE5+czV7YCheJK4GKTZgdrmwfg86ebYjytWiumacqNa5GozOzdyBSoLGX0j7+Jm0gp0HgvRLacYCDr7zkP7SscRf6kCrooqkot6KWVC9KhaAKJWKS8TSImOai73HC7pxl8q+Q27O7Du5ZYAbwQtFQmGH3z5Tkm297ZwIH/q1rj9e3pyhN5Dj6s1z7EwE+eIj7QC4JI0jM06+81ALL1k3x/aBUp/V7pX5zrOtDMfdXNQZLSZxfvdgJ7ePtrJ9IGFLjMHeVQDq12qa11uoFKEeLKQscC3YRU0X2vZa6833/NV7zhrZx3w+zObl5it8Yh7DrATIILR2FTSt27WzuLMjVPo2Rejz4DRJLYoSBAM1or4G6vVGnq8AlAGrpKLaOALlY1pZ9VWz40PBDtROhmgm4MY+MivCMA5ZRSSrSuRUEbe3dI0WSlDt1t+wPENeFpjOOhlOuPnl4Rbefnn0pBNjKN1HTie8+Koo514QJ5e3J70f/rc+ujtzrFuV5tCQB8nRwhnbbBc9afizIcevfUWE+x8O4HRqSJLGOatSbFye4y+vWEDx9BPEC5EYm/HaLZXn7MylGc7pmeOR0QAA/eEs2YCEe05hrjdAoNCBhQsaK7fNNJS5M7BWunM1e3CHXaTjchkxTqX0B8jnEysIsxlmklzLF9m8fYNsG5/rJcgnqkXuk5L8U88+Pj6+kR8801b2W4en8enpeotCNOIb+9Cgjx8/HiadFwl7FSIFJ4JsTmxYavTnioUQt/deNMPbfqxHhXePeXj91lkuWp4kZHKzycoCb1w3w093t3Is6uLjv9NdQwLuGKmcQE4W8LlU4jmR4YLzRJND5ldH23gkEip+NzlXPeroOZYpa3NdBNEybW0+3oa2cwJoRJJRTbNbdxGHOpaxktwT3Z7tcmZZgOn815JHNFooxogAp3xqsXiR36czouLnQmGjmE8llBLpmHJUjHVmgm6VhNi11SDV5mS9iufKYoEGlFU9Nchm0UZtnohxvaR4PsyXAF5sW5WEPjOKieyWyHMmmuO/9y/izRdHeXK3ix+OLuIa71H+8dBmXp6aA79aRnAbIcvWZWfjIjm/Siio8OEnluF2abzl0mhF6epGSXG92uJgoPpy//mJ47jdGmMTTv50X4hw+NRT1xc8OfZkBHyIbOnJs+XVkflX+D8sCCNRR5EYS4LGI0eaeOQrTYTcMrGsg5++dVj/bXlI93gc1NcrTkHFZaLDqQoCahDW8Dz7N5Nk8992sJNnmGGNQJt9l4321er4arlYWN/8q20nN5vh9aFhHAEHXz+2hKjs4vpVz6JEckUdty3qdK6w+unGMhKZuIAoaKAJuBwqOUXPSm7J5REFyCswm3HwlODh7h1h3rk9wpLmHO/9xSL62vLMJCQ6W/K40hJQ2r5VX3y64WRGimsRm3pIjw+Kz0q1bTgUgZECsX3xQIzbBkP4nQrX3VFuJ3nOOQECTpnVwRT3TjazfVGC16+NsH6lfj3tDPgNWEldNUss47d8UII+/Z40iLUdUT2VUeR6SPGp3L8daiUeGqiLzNsl4hrrmyOhhYIU8yX/1fIwtsJMjEfa9b4zlCqv8GnAnxSJ+XSCTLuM3yeWEVyzC0j1BMHyap3GemYU3SwKS1tJrtVP3w5265hRFtm1EFnz37UivFAprzAT3GpEXAq7qUWtjW08KzcjOgUu25BEnlQ4PiPR7cyw2h3n4F43W7ZW1lSYL9+jWh992yMB7t7hx+lQ2L4pxcsvjNOqVLpR2OFUJ9d5vXo7VoQUvvnxEeKZHI8dO6W7fOGT49MBL7RI8XyRBTt0B2WanArJvISiCfidMmlZIpbVb6HP/KGNfTMefnj+Pr3EcuHhN6akHC6xSDynR/TOpq231InYEc1USiVeIMLBAhE2iHE1VJNfmGFN4jN/b21PPQVE7JaptZ7R8W0J6x3b19Y8i6KBJFB7OrBK8Q9rhMwgLamcwLfuaeaxUT8OUcPnUpEVgbwKiirgdmioGsgFkpyWRSRR41Uvhs9dM0YgneMHT7TS35plfM5JOiuiadAaksnLoEVEgjUS8RaCk1EatdF7+1SW8a22v2pI5UXe9vt+QLdPu/togCv6Y2xflGA86URVNdySxtm+Wfa1rWLbsnF+ebSdNcEUf7V5Wt/IXLmnqzkap4ScOrk1opFVkqnMy5irg5l1sEZk0Pj+VONk7avi/NslclWJ1tYLM0mvqLpWw5mhSM6sProGQa7SFusUuTX5rVbZamdcIeOv/rwZEWODLIdSJYJsyCrCezMoh2Io0SxOw5EBil7EZrJsbaNdJL/CBtDwC65ioWYHq8ewOdpsB/P3ZpeKWk5MBqyRXyu5tvPNh9JYYN2+0dbIpAdR0xAEXWKlZRWkDjdnzs1w745uNp+RPimJeDMxibt36Bv6iytjnLuxQLpN0ppqMptTgdOhINT/keMCGol+vdDIsBULiaQ55/J8/Q0jPDvq4ev3tzJXIMVXDkT542CYZ6b0qeI33LeW684YpL+FYhKCN+xG8ohV3SeSc3KR1Cbn5DICHPSJRYJsjRRXiyCb5RdWomyVZpgJca1ogB3M2rNqnZwZ1bKZpRZPozao+no2PrIAOUXgmX1O7hgKQl7ln7aPAqA2SRwadnDjPt0pI50X6ArkuXB5gu0DKUJeFaekcdixnkAkx44xH7ccDvORa6b57E26/OPImJuPfqUbAY2AR+WDL59hWdfJJ5JWH+5Tjecy2miGNdLpdah87LxRHKLG+vYMqqZHgKdTEo8c9vDH0VZ6fVlmu+BQvplH9i3i2jURXtQfs92+2aKsiCqVxQyJTrFgiKWdBoxrUs1Bwfx9tfNatwzDhqTavQzWQj32XjU9dBvw1wXsLcUyyrw+uma9ajWZgBlGlNaOtJj/ruYuYSbJngR4EiK+VKnPMyQWRjQ55hYIZUuRxN4pR5HERNd48PR5Ss+qRWpitpurZu1Wy3PY6jdsWyDEeq/Ued2qkeV6SLGBWjOE5v9dzR7SQ/bPql27BHy4nfo5FwUNwe1gNu/EJajsnfFy+LiL5X25ujhJNV4z51b5rx92cu3lUc7dkKLJax8trra+UQDqRKPHpwMhNuPPlhyfCIF9oZNfM04msXDP5dm8CP7ntSN884FmHhwK8MfBMACLm9IMJ/Us/I890c+P1j+Js9VTJMjZuMzURK5MFgF6p2v9bEU9UWODWM+HpoCD8OLyymDz+RzPJ7Ewki+kFg8u9Gk+ayTIDLvIR71JJIAtYTAGucf3uPjSY11liw/u8NLmk1FFgaEZNyuaM6xuzfDoaBPjc05ufLKZbUvSuBylTvGuyTDffEQn0euDKb77nhGeHXbzpd/pJDnkUYhmHPzvHWE+8uqZigjyycKpIMa2ZMk6kJ5g4ZWFtkUQYHNnGk2DIzEXkxMwNOfB51B4MqKPTktbcowqPoJuhY9tOMrmliQk0f8Z27Xbmckf1m7fVhmFOepsJlJ20f2MHyIdAi2TYtl3nnlebuaVQbS4UQ7FKv2+5/O0nQ+NEl4T5q2YZrft9urrQnnRodysHlCogOVZh+q6Y3PFSp3UVF4HO2lGYFRfZq6ntM1QYTsxt1ZBkM1jpTOuIA2VvlD6K9mUub12pN3Wz9rsSmGJSpfB7GZh42hh3kZx2zaRaMFdOvZ6gh5WxwoALBZvVpJsB3PU2cCjdLOxJ40zrrBn2ssKMcp/DK2g1auPdZ/7UTtfe9couBfmsTauwGMPBAgGFTadlaRp/lUqcDJI7elGjOHPgBy70+B5AVZfWQhOZrnIRlCRcBJy8p6LZnnnuil+ua+F3x8KM5oyHmgNDYEHoy1c3JoqvvGnvApNlBNc89/xgoQi6BOLkWBDVhFL6w9+yKuLKM3R5NK2SsTbWN+anDefFnkhJNnV7NF1by1uMn2e4iDjOVaKXFhLipoxX3TIFoWoiBLJcCzh5qM7lwEwEM4wGPUU/z57eZqV7VkmJ0TCHlknU3f3cXDWw39cPMKbNsyQUwQ0DdyCBjF9gJrLiMXiDx/fcBT30STuFjfnODL87JoYmgZ7hyT+/emlHJt18Z83tvGFV5Xsd07mPVqr+MNCkrMqUI0kPU9k2djvz4fa+c1wO0GXwtm9CW4d9qNo+v1/dk+Ss3uTHGhKsJJk2YtV1ZcsayRuHncK87k1SEuR+JjItTkaGBjVMGvRnXH9XphYLuBLiUVtaaOkdt7jOUnXxtYabAHrVvwWzaK6suQGYxVT7FayVKZXteqPbYpYmKvRmSOy5peUWlKlkvdxZUS+ZVKkBZFIR0lSEXM5KAYx2mUSTSL+pEjPzlyFk4RkIvPmqoR1oaVG/znf9TatW3EdTb8Z3vzWRD8tW/4SYa3+2iis19huPKlwt4hmOZz1M5gP8JcDE0ixPPGsxE65GcEp8omLJ0j6ZvnnW7vI5ctLOFeDXbKeGJOITDj5+JunyfhUOAGSeiLRY+t6dmTZ/DKVfQ4m+l7w5PjPFdU6NOv3z4ctldGJep3w5g0zHJx1s3/Gy+Jgtpgp/5OxXi5edhDQO4ecU0OxENqgRQ6hk2B9mfZOFyBzbEYp+z3kFcqIsXkdg1ybYU6y8xa8I+uJBhioRojNlY7KI2r6dTGmpSVK8hI7mAfAmtFja3Uo9OTH3oCTa/Ixbno6xGDUg1NU+fj2MVYt00nIT59t4eaDzWWbes+qY8WpdZek8ehIE26HyubONFIsz3hWP4al/jT7cgGic17caZU7jwTxOlSWhHIcnix1HWNxJ7G0SMir1hz8rB3zfDM01QoyNOoOUIGFRA0XMLVe1/o21xVKriVyQVbxhnURLuibw+fUCpExQCp5hxv3jpVIlU0vm9pULWHMmshlLppBn6dcr0y5HrkaWkxV1TJ+iXCjLyoW1HpOqpL+AnGz0/JW20e9sHMlsHMpMEcPzSTY/FJu/s3sxAA2OmRLBLn0t307rQ4lYNGkm8hteKjUH/UU7pvFfR4iHSoj7SUi5k+K80b9rC9bVsmHtaBJxYxGgTRWO/66+gDjObBElM1yB6BYzdVALQu3hRDlWuua76MHJwP8MLeat2+Zpl/U7Rs3dKS4cW8rb1o/jRZ24gyKXPfmybJtzPcCYn6BeuRZL2euSfPWq6OAPbl9riO61v09n7P4/0eOF4hGPGVPZNvVyO9zRYrr6Xj+9oxJPnDHEt61bIRPPDkAwFpXtPh7NpqDDioiwLVg1h6HvIJttNjYHpSIdjUHi7K2WGCtflcNdtNeQFlEzUwm8jam9mXrWYzh54VN9C+dFdh9TG9zqzfP9VccwyXpUWCAN66P8Lq1Ef74bBM/PNyFW1QJOvRKR2Iky24lxBcf6+IVK2dp9coE3SptXpmNHSkkQSOiuhie9pBIwr4ZXY7y6Ghl045HnYS82bqiQ9U6PFuLrXoqYNWDGsSrIUnLSdhfPctd2h1lIJBmIu1if8zH9wbb+d6udvrDWa7bdLhieesLWDGSbEgSzH+bzqEx21EaFE3PWIcAqMUoZEWfU4V4l7Wr2H9IRDr0qf6MQbJrHL8SybBDaSchS1zWHa10HjAI4/IQo3NOdh1x8uhUkL2xJt48MM7VmypLEtdLjM3QNBhK+9ifamKZN0WTpKDOZelx1nZCqPicLZ+1slZcqygmYdpOtQS0oja8kRfESFZ3E0KXPRgvLPRIgAdPIqQn6AUlIEQe8OyKoUQyOA/F6Gnx0NnvJx+UihHlRJOKJ1HwJzaixYdiVe8Nq+Qj45eKcg4oEORCn1rrGjV63AaMiLGVGDeCeohxtUiz9TtzsCU3GGPnlI8f5lfz0QsmWN2aKfZ7g7MelgXS/O5gmO3rMviCFZsuotZswZ8O+WgPywR8aoV/sZUgm69VPTCXhq4nkmwsczqRYgP/R44bQKNE9fmQQDSCeq2c5kOXX+/4P/HkAC8NjPBYqpX3bJ0CID0UY3okQ24ADkYFVoTLp38MKUTQJxL0leQVBvpaJXw+kaaAA99cSaMcT6llJNuIGhuyilqOFfVMj9Uiy2UDnylRxEWIRrpZa8dfk6BZBvq7x0N8497e4ufLumZ5xzkR3b7LHCVrcSOJcGlXVCfHkspndi9lqT/N65dOEcnBKxdP0Sdl+Yc7F3PR4jiXX6jwie1jxYFXCTl5esTDf9/rJpWT6ArmuWZjjG88oOuPW3wya7v09tUTtbCD8QzZkuIa56GIKhFYM2pOfTdyLZ4jLPVnWerPcnb7HK9ZOsXbHlxNNgtZRcARSxd1rKAP+FVlOwUSK7V4UArEpkSI1bJiDgYMGQRQFlksg/XFBSqIt7G+M67QGYd8jQEd9OsQkx38drKXW2c6AYjHNV7eXro+CUXi/tEg+7MBDu4NklIksppJ/5xU5305mY8YD0+J3J9sZ3cmxLTiZqMnyk0TXWQ0ibwm8oHW/Wzz1WcdmpvNQH/l9/OVmq8Kc+TeJnGt+JvFrcH8vRLJIAHhWLmOOR+UmOuRmCwk4fmToh45Nm9j5zSeAkn29ThI+VSG+2X8SZGWST0pT1oeKkVpLRFuYybBHMW0Sm+Ks0TWxLoqL2H1viRYI8bG33YE2SynqBd2FfXqhRLJEFWcfCu3jvefPVUixgCRLDMZBzkFYlkH7/75Ir7/pmNleSJW+zy7vnj3sJsf3dLM214e4Zz1lTZwUB69TfnUhgmyeRvVotHm78zbPh1IsYH/r8nxqY6+LkQj/HxW74ITz9p/NNXKl3qeRIq5yUWzjOyOMz6td7QGMTZ0xamUaus4YedK0RQo/W2QZ2skOp5S6Worv6WrlZSeLxPZrmOzdqJWLZwSyZQPIibYGdjbLldlCtXAZNrJ+x5bUfz82qWTvGzDHC7jMK2lVwt/ex3wve37ePuDqwE4kvBy3bOLAdgemuY3w4sAuHc4yNJZNytNhy/F8mxuyvPZV+RoKWgPNQ3WdI7w7JiHlR1ZhAVU85w3UtwILMdd3G4NSUst1IxYzSeBWSBqbdPnUPnimYe4fs8i3vHgKr6//im0TCkaWc2mqkiQTNE+X4qiRjgflKBgn2wepLzHFfKKgNep2RPjajA5YZS1w1SwJh+UbMmMPJMhKjv52rElIJcGzxvGe7lvupmXBkbZ6ovwNyNnAPBaZS/btUH6iPMhx+UA/MvAflY3JaGh11RTG2az/Crex83xRXgEhbc2D3JGdgRJ0FC9oCLw5cxGIgnIZWtHHu36lxMpIFSrb7C6ORS/r9LvGPdweueEnjexzeyhLbFsVLCN3pa5aAwlCA9BuJBvAaVIsCco4TFe0qFCfmaWfjjjeuKmLYzZIrOriA1BLt6jNVwqzMdh9P/zRY3NQZJ6XmDmk+pZxx3z/jOzOb4VX8uZi1Js6UpVHMdQVD/uF/XHuH0oRCwt0h6oXewlH5R44rCHL/+hlb+4cpYLtqT4wtKxugs5naxkOyvBni9SbBf5No8XDuXUF6L6/5YcP1cktJ5kiJNtV2UXET4hu6M68Pqts9z2jJ93SruL04K52QxNAQddwIgDcuhEVtcTl5LozE4T5qivWVpRy4nCqjM2ljeXlPaaatVDfXpju87Q2qFWJSU2mkPzsubt2BnZW8lZQpb4672bip+vX/Us3V2F43bUJtv7kk0cF4PcNxG2XebBWHmVvqDLEsVFd1Fol7NFuQZAewguCSTL1q1VlrbaPb5QYlxPlGwhxLje/Z5otNm2eleVIhAAXWTY7p/mZ8lF/GGqg1VLKvdtXi83qPvOulp04mPIGjwJcN49gRLN4hkIFTWlAPFxmXc+vAoJFQWRtwyMsyaUZyDYgPykUKjCgNn5AiwaU9M5uD/awjeOL+V8xyhvch9krjnA46lWbowvYVT2MeNswh2egxE4Wx0hITi5x9VPm5iBPGzviNHfrSE5PPYRxlqzCtEsX5paxROZFryCzIucw1zrOoyYAwQ4pjRxSA1xQA6xR2nhVS69ylEtYlwrt8GuBLG5v6lF3Gy101XkF2Z3h+K5LhDVue0hAqNtSLE80TUeUj5dQuM5pnsW5wr9lHGc1Vx2iGQQd+ja17ApOVnp9yOZSHE1fTOAx6ZMdBlqyansZjBsYH1RrkWMzdHf+Qo6NQLjulqvbyTv5PqZlfiDAu/YPFF2HJoG94yH2NadYFk4y8vOTLJ2Sa4qMZYVeHzQx4PHmtgykOZ7d7YAcMbqDIIAbarKye8Va2O+BDsD1jHi+QwWvuDJsSOhQiHfyKoDfr6jsGY0Ws6zXpxq0lsvsnNwXjBC3/g0WcDbH9J1c8MpmgIOmjwiK9d6iwU4zGTXSnyTJvlEe6erGP0N9AfJzWY4+HgUgK42sRhJNtu9GZHnagVBrIOWNTnG6ASraZHNHRw2g1gxgaSaVjDsxmsiz2ZCZBfpVKJZ3nP87OLnL3XtpMMlUBEdsySb/PJoO7+O99m2AeDN3cfYl/TzeFx/gF60Os7rl80w0rSmzBbMvO1qSV3VYPbOrUAdU8K226xBKp9PzGvxVWWZWstbsdafgAn4yfgiPplVbMvnVrSr4BbQMqn3NcqdI6SN5LCd5WRhT8Gv/C+VZ/i+tIEfDurZSe/cNMEljNR/jIVItYGKWYICOTLaf+PRdm6b62a9O8rbnPtxNXtwzsY4N6BxZ7KLGcWN5nEQ8QY4KzjLo/GCpEjR/630JXjv6pGaVQHtoorGM/iE0METmRYudw7zcucRmkSFKdXDZ5Obudx5nBvzK9jmmGRO04leStP7lVruE7Uqq1mJca2ZKju3G+tLlPEyXpXAouuzM30eJgpa4ZgvR6/PQcukh/CDMcImFxzFRIyLbanYsv2119XLpmWMqG7hnpgr6JsDo/4y67eGZ41O4GW62DabaDrUF+G3XndrTkqtMtXmdY+6mvns4AouWRLn9esiFffwM1EfNwx1EM072TvtZWmvwpaNOfI2ho3xMXjfL0pyu3NWpvjE2ydZ3JXXyWjhdD+XBT0M1COrO53wgifHYH9iT8eTfbLxfBUusMN43sVKp373u8MulGiWuaF4GUGdmsjhm5OLMgsDRiS5uEwhetwUKMki0nGZAHqn4vOJjE/LtHe66F3nIzqcKlvPgLm4iIFq2jLzdJddx2hNnrEbBO2iK7Vs3FwDoarToeZ1AT7ef5CeTggkkoBQ4atqbOc/JtaxP1cSdnZ6ckxk9PO72h1jS/McPxvXJRT3pTpY35Hmn8+bYGVHFtdcdRP+mt/b6X2tzhKWSNF8qBVBrVi2TreAeitrnUycTEmHEs2yL69Hgf+t4xlgSfEFzdtfOftQ7XhlTeD3vtXM5p2c3Ztic0sCeUi3GRPQvcrXa1O8X97BH8RlHBRb+fauTta/WqZLNbWt1svNUKJqYRvrPZ9TBW4qvMRdJQ7p3xWeqTZHjut7nuCG6GJ+M9zL7WMtrHfM8tfu3YQCIqFUnPvz3dyZWsR/7FzEJavTbO9LIBrts/PCjZTfJ9nZHF86tow+cY43uPVkx2w0hwsFRRK4Ma/LmFaJUS71jBTXtfYl9RaMyEZzZTNaxnd2iVpl581udsriAWy3nGE1GV3jKWqJYz6V3ilHMVKc2DlR0U5rEMHOw5ehWFk7zX7N3v5QedQ6ksEJeJab7tVqfYepv5DMyxmwufdsXw4thN+AcSxes28y+rlTmvVzVM81MmBOqrPr0602bcZ3j8XCfPPIEt62aYaLlszZ9q8bm1N8fOMwn3iynw+uPs719yzi4rEkF21N0R5SeHCvj8iUwK93hYvrvOtFEc5dlS7J3WyIqR0xrrfU9InguS7wtFD8WZDjenEivqi11judSOrzASXkJBITaRXLnyx32AUjGaYmcuRljbBJ/rBjDGbzAs1Olat9ctGTuB2d1B4eyhQ1xX2tEotXlAp39K4L0jSc4vBQhq45md51wbIKe8YyYSA6nKrabnPnb51Gs4N5iq3iOCnvAK1VkGxN/THJAaJZ28pJxgCztsWhd/I228gNxvj22BLuk3sAcKCyKRjn3YuOEGh38c6HVxIQ8izxZ/n1VA9Xr49zaVeU3oDpvp0DIlk+9Pgytp3tZSUNEDu7iK/FSswuYazWi0NxmTo03PV8bnS/zxVmvAEenQ7Q5snTmk2SS4g4RR897gw+SSWnCgiAGNfPQzIFL26dZGVnnoOm7SjRbFnk8PiUgO/4FN6wC1ehpG9ifQe3HgiwY2Y9QjLHWm2aH0R7+bbYyX+dfYTggMKG6Sy9h9J8VTyTl+UP8HfqDj7LWRwRm/nc71v4x83HaVlTePGhXLZlTZ4yXAEMVCNvx6YlmkSZ6733V0TNjOfySu0wvS1pFmdm6BZTuNoK95kXljDCKyJDPJDv5qanF3H3Pi9vfUeS4BpPwXM8U9I4m0iX0Q6HAO9tPcBXZ1bym1gfV2mHCxaQ8EZ1Nzc7V9EqZLmsI4JTqB4drDVNL7kdxRfwan7rtgR5oFRUaL573CBn1tkng5iGI1kCkQzpoRg9hfa6tnWQ6fMQ/8fljLTLDAechHIya49LdB7S9eYus56/36+Po4VS0cVZsuUhlJCT1lgHRLIkdk5w6PfHyoouFe3SdpTbj1Wcq0jpOIqwmbmyk1BVPPeD1fdT7P9N7hXZaA6lW53XE79iW6brBLX7FuO3PUIr3xrr44OXTbM5O63bM9qhxc3+UR8rAmk2tiT5r62D/HRfK/+6rx1NEMjIpXF1bWeaD106jbO90mHGKu2sJ/nNbhmrFV+9FYUbkUzMZxbwXOAFT47FhIxE/eR0oUT2BUOAT8TuaoGIpUX2R7y8rav8zTw6nCor9GFEjGNpjRVheGxKYDYvsP9Ynr5WFSZ00hlPqewYAxDY1q3rkNNxGXdhoIwOp0jOyQQLkeJsNFchnzj4eLSsGIjZ39iMRjOSzYkZ5nXstm0e7IyBxDxwWS2FzKjYniXyah7g75oMc5/cg1/M8+nOp2lx5Mo65w+dPcGnH+xmd07ikxeO0B/OFbdpxduXj5H3zKEcLycQUBmNtYuY2xUusJOJ1Ppshu00cY3la6GR/dbTjhPBZM7Fz+aW8uion9WdGTIxkVwWnJJGVhaYTDrxORQSsgOnoOIUVHrcWYazXj62YRjU8kpexuzHyOEMP8iu4lmlVe/dE3BV9ihtcyo37e5gpRjl3OxxtgcjCGhcrE3ynexa/visl2tCIziA/1y+j3tnW/nu6Cberuzi79XHedrTwz35Rbz30RXwqMbr1kR4xdlW7Q1lkVnzzIidBEkKuzme8XDd6EpWCLM1JRE+QeGCpin0El6Vmk23oPKSzhkuioxyY24Zn/qfbq64IMmGbUkcFzoADXDQMeUh8LvKks1bGAFWslds5SrlcDFauEGbZrEnx8dS53LP1BRXdMwC1e8dO6lWbjaDkpUr7NvmQ242Q25nphSVtKnsZm6HEs2WHGpM2ylWqiu8wHi3tpEPSqT8oBoulJMiLZMuQv0yMZ/GwwMCDAiEchKhrJfeKQedh/RgRXSNB9boxxfeq+uTDZtApd9Pvs+DY1OI5XdPEH1yimw0R6A/WGyvXeET2+fLLrmuWrJdjWfZTr9t7NN6HxiwS+SGyhcYc78ntXh0lw5TO/MzGb483M++pJ/Pr9xDwKEwk3fy+3gv906GefcZk2zOVnc9eXLGz527wwwlvbx2sf5S0d4j8oGeWeLZGE8POvnKPn0m8IKBOVrbVKQ2seZs+om6aNkRZrNMw0yYrT7WUOJS9ZSZN36zEmQxIZ9y9vqCJ8cvWJwsEmvXWVTL2j1FpPmJY16WeFK0J2bJUk40DVeKnKyVddgHowJXLNaIpTUemxJ5bKpkSdNceA5WhDWCPn0tb9BBdDiFN6hHIpK79XRnO+lEU8BBKpUr6o+TczLeQpvKiFw0W9O1wlxAxNwh1pu5bP3drgM3Szpsp8ZN05LmiKfxdzaS4X+za/jr3qNcIFYaECuRDGta4MevsBxjlSnxNWQ4JCll7bVqH22PAXtN5HzHPx/sjvn5wMmMNkstHnbMdPDoqJ9/OHuMM3tKsxvF5CUFZlMSIa9KIisizypMJJ0sDk3R6lWAELR48V+xuLThSJabxpppEvJ8u/dRJmc1jigB9irNjM85eW1+D+eGZsEDg1EXX3SchaBpyIjsjrfQHNC4pHkGYhlevCKBok1w2+RyVmQfZ0tqhHPCU/xT4kzG8PPzva3cuLeFT79snKWteTJ9npK23KxLLxA1l6mNoN8fqgbfG+1jk3uWtwh7ap4zO3IDlRpfX4uHN84e4lxlglvvW8wt93byxkvibLgkQaJJZbJdJvW69mJiotewEBxK8KJnJzk87SQZl2kykaNQPM5rfYf4RXYZNx1X+evWQTZ5oxVtrJZEZ3xnllPYoVp/YpbOmBMvlX6/bREWJeRkYquLPYsMUqISymqEUvox+ZO6f3WiSWV4Zbm1plEaOpQrtXM44GQ4AK/emSNxxzAThb63KeAoztYbM3ZtvR7cYZfe1uUhwpTPoklhd5l2uThrN5sp65tzg7FiBLl4XLUcZKrMStgVXinTGdfRr9SaTSxux0bS8ehUgF8PLuVIRo+cv2vvJvq9SUZzXi5cPMcXLhumLVN9ZnMk5eLzuxchayLLmzMkZYlYk48QCn84FOLne1oQBAi5Zc5emuJ1W2N4XZo+C2gDQ8ZwskiyASsxNn9XqyJmIwHH5yM4+edDjmsRQRs9U8Uyp7o98y1j5+e4UFuravs3E/I6vCPrRbtfJiMLpBM5svHKRDufTyTpEMokASvCGrGCzeJZ7SoHo0LxezDs2fTvUil9mmtqIgcTOXw+vcNr73QV92G4USR36zpnc9S4Gqwdo3ng8gYdFQ4XZti6WFCdPBUjSqbPZoJtHvjA3gmhmMBnIq37lDAAN0700N0SZ1VnvmK9IqoQYms0WOtSKojxfKiWsHOy8HwS45MJ43pcvjTOj59t45lj7jJyzM5plIIMorsQBfQBBKAnnyC3O0aicE2Es/Nlz7TS7yf3mIuRvBcxlqJLhC4xzTlOPeKUzebIRvXdfNpxif6HAO9UnmQKH98ZWcWTkQDv6j+Gq8XNZcsSPBYP8wX5LK5UB3HFFD6gPY4/KHLTXB+q28nHf9fH5S1T/OX5c0iSZj/9bfhlF6biQb9fUsEA+5IBXuOaxOkqt2Yy33OzipPHkyFySRmPIDOrujmrdY42R66qLrZfmuODPYd5YmqU7967jstyY/S06W0yCp9EOlToEHSf5+UBXrpN5stfa+JW1yZend6NEch2h128mGNsd47x5fRGfhNfxOPRIGsds2xz6H7u1lkkax6D3VR9rf7F7lwYCXWGpzBQKOvsIOZTibn1FoeyCr1TDi6/WdGLcWxtY2K5wMMDAoccHTwlryxue7PjAMvlSc4d1Ezbg1AKOqYcBEYVpFi2+NLm39pJL5U+8N4C8S8e41BM/wcVL/3mCLKxnXS8FMAwtmk9M3a2dNY+0xpdt3MWsoO5HXJOLbajUSs3Ilk0DX57rJWfDnXS5c3y7fP24xZVDs15SckSq5boxZZqje/RnMT3D3Uha/p1PjTrISA0ceNt7SxvyXI05uY/Xz7OIkxexenCP+ylCHayhpNZi8FKkiuI8cngXcY2HDloWfhm6sELnxzHcyDPQwSt39v9DY1dtJNJZO3acqLbq3c/dt8Zx1Pn+RhYLpO4y8WQv5Oe+EiZ2wTo5NhpIsdGxTujNHRfqx4h0yEQS2sFWQVcukz/3+xoYWw3lcoVt5+Oy7jDLtp6PRUkFyhO65lRjL4WOkDDSQNKUeP5KuoZn6tt15ydbk3kM9pUixSbP6uFUsI5VWRmVmFWDfL5zBYAorKTT05u4KedTwA2kYwqCXF205zGFLCBar6cVjS6nJnYLKRK1fOBWn7VtaqZlaHFjRedjCzxZ8qiqcUo22wGVzRbNq1uJhTZaI5M9xyJPYVEqpCPPym97M6EOMcxXrFL6yDfKaSY0HysU6fYounk+WJ5mJ/mN/LPe1fwluQoq9d7+OfLJ/njvSJ3xJdxWA2xVIvykfijvIqDkIGXBY/yqdltxO9x8PeXRYqa1NyOSV1WUXAoKFaLLBBkV7MHb1jkQ+FjfH//IpYrMZaJMURBvy80DZ7JhPntXC9HZD8rO7N4AxrpvMCzox5+PyHTRprXtR6jNzWDT6iUMuRmM4QFiaTiYKS/hVAhkupJ6HKAMDqJ6ERgYjkcW5nlwy+f5T9uauVHnk38lXtvmdTDL8hE8BLNuVjljPDDzCpWNUUJCPniuTX2bc5NSMdl5OUlS0rjhd3cT1UrUw+l+yxTKN9swJcqlW72+1RCTSqxgn/tnkUKDw+4gQ4OOfQkzuXyJJfOjrA8oF/vcwe1oq7YPNtodhEBSkGNgiTM1eypONfusKvsGMxtt0oNiiTW0u/Y9rk2fYq1zzRQUSWyWlGcAqwaZWP/DldJw1vNscicgKefm1LQ6b6JELeNtvBvA/tZvUgpJsWtCxdegpNUugFZ4BY1+v1pnp7V9Qkd7izvO3ea5JTMPRNhLlkyV06MTyPoL1Q1vNHtAnP1BjefQ7zwyXEtLNQaptHo6vN08U4ZrBHmai8Zhe99KYVWn0zW6atcDp3M5mWdDBhJdrG0Ula8w4ChNV4R1gh5hTKbNjv4fCJtvaUOL7ylnUDBKcNArbd9O62ZlSBbYSW9ZlgjItV0ydWmAWt5I39+bAXPKG0IaGiFuJaAhk+QSWpOXuIfoRpquWI0gnoz8utZrp4qVaczFnw+C8/Oub0JojlHhXWWMf3uNd1naYsrAIDkEdnt6eLn0SWMJzwsE+P8TX4ny/JRsml7KRDAg0IP/VKMadnDoBBGBUTAhcpfZp7iAWER3xlexuxRD1sCUf5m0VGuCIxz+2QLv8wO8G/S+VykDrNdO05QzHOeY5ycw6f7G0dK8hN9ajxDxt/E0xE/RxJhPHkvA2qMDZ4o0RmFZzJuIoqb69JnFNsaSOUJSzkSkptrtsT5yNYxHFJJx+iKw9BhBw/s8fK14yvJqSJLxTgD2TgRzc2s6qZXSnJcCHJMaeINF8YILVJKVd98Ip3xQoniAlnv3Ommc2sbc8sE/vlts3zqO7qO/2JnSarkavZwrXOYb88uZ5UU5XDWzweT5/Np+R7DTbTs+plf6PM5tcKyst7IsZmotkyKdMY1pFhOJ7L9/oI1mh7ppV0m5lML8giZmMvBcnmymGTXMunizF0lmYMSyehueNFsRfKa8aJWfDmzJJuZ+7pitNeujzukb8fqCmGWmxT3aepz7UipNW/DnMth3YdxfLbn1PTMuZo9pees0PfvF1o4GguyrCnNMibKjs0841h0yTDGwxY3Bw942eiLsbIpiSDM36fZzRJ6HSrXLJ7h8ekAo2kPk1k3jx1ycUlXmtcu1WcriHDCs771Vvedbzk7q0YrbIvYNOKK9Bzjz4Icz1fNym7ZRkr1Vv1uAe06HcrS1o06o8extMhw3M2iLj1Rzki862pzFKO8OVkjkS7Xt+0Ys8oo9M+GxMKM3nW6PZlh2wblpDq8Ra+la3R4gf7yOrWNaEWtBNn4zgxzh25HhK0wO1oYzgIG7JLcDBjDQnwmxzOKXqzDIMaXOI/zts5jxchENc3vfMkqpwsaJci12v58Ee26o8cFuPxisU8wCueYo3DWe8rbHyqYrcGQ08MXp9ewTJvls8pDeC1eJtUsCcc9YR6Ru3mrey8/yqxir9DGOm0a0IVMF2jHuSB/nMOE+MLcObxrb5h3LTrCS1ZGOD/yJB8d3cQvhDX8gjV8KPoou1xtrPPk9OISh0rE69Ojq1FGBA6qYb3tyCwSE/xa7S7ew3b411dMMam56FgSJ9Ck4h01ioeUllm2ApatmOMd8ShjcQdD0y6ORppZ1qLS4lc4Nh1kW1+e1501wqJY5dTxxHKBlmAIzzE3UsFdgaEYgYK86T0bMnziyVX8KLuKT3ofY6BNb8Milx79+0F2FRHRQ6uWwodcPLdGf2H0UQaM4IDd9TCTP0OCkC5Y65nvY88x/X5P3DFcXM8byRCOhZi80MGeRQoxlwMsJnqL5/KEUmLRts3sImI322Oe2TFrgutJJjSva012c9nogavpsK0J1OZnyuqCUq1/sxJmJZKp6uFs7FMFjgpBfiNuIkCOmzJ+/jXwOIulUhZa2eyRaTxPTOT58WAnO6f9vKN3uLjPqv7jNYg7wMFEgPG0m4BDZk52sC/m45Iui6NRrYqB5m2egONDvTUa7Gw7zS8yC61W+nzhBU+O1VjtB7bWDXiqiGqtG6Cem6NaedBqv50SzPcyYH4gwyKt7jwPjfhYm1KL0eEuKHoS52zyUMzEOOgTy8juwajACoxy0zCyO87+Y3n9+0JUuavNUYwaK9EsroEQY/fqeozu/lBZJ2gQDzPM055mGHpj47dqmctW2Mk5rLBqeasVIjEwrbr5aOo83qU8ARKcLY3zNs8+nIJ+bvJRk0WShZjZSSYaQT2DoXlgt4v0NAq781HPb6cT6iHIeQWenvRy9fJo8TvzbAfA9EimWOXRsMMynv/pjIPH0628WvkDl2pHEYAxmvhfaSMOTeGoEOZadR8Xa8MV+74wM8Sdjj4uaI/xm5E8bsU+SWwZMb4s3879Qh/fOb6KR6LNLPakCatpVmgzPC728HvXKs6SJvj16DJWHstx/nKK2uJ9SnPZ9i51HuMP+X6EwnPd15RBkaHNmcXhEnnF+jirhRjgpicsIw1bknCMafl+f1lSUVtQw786ywZKfdaAL6VriZN66VoD/qRIz85coTCK7rzgSXhwDepkNPqkHpWTgkFggC5HmhQOcrNJXM0e0qpOPENqhk8q9xHuD5CblchGlVJeBKUX91qzXmYYZNe4vv4WTxkJVKLZogWaq9lTTHgb3ao/a8PNOjFePJcn5hbKrNh0qCiHZsqq3hnbgvJoqDVJuRGHDWvQwM4lyLzt+foKO39gc3tr/V78vsqYaz2uR4VubpGWs0Hw82HfU/w6N4CsSgTDEg9n+hga97BLbuWq0BiXL4oW14vnJX6wp40d8TAb3bP8R8cu2oL2170Rcngw4kZF4DvbDxBr8vH3dyxiaG6W/oBlG3XMcNs5RNSDen2IqyXM2Tos1UGW5yPSqvv/NMenFI0SzloXcr5lFtKuhezjpJPnOqLHoazM+y+b4fo/9bPNt5dQSuWOYYGDUZmrNzhY1u/hgAN2FyLC27rtt6MPJCor0KPHB6MlK7fxaZmQV+DS1nISnY7LdGxqwbVN19UVSa0pQzobzRWdLgxYp9HMsH5XjSBbo3u1loFKcm61QksPxUqEHYm/d1wOBZnaDeJaAF7kPFYkxnaoZk1UD4x9Kxm17gGxWnTyZJRaBfvjqPfY9maCjOS9XB6YOCltqQf1ao5/vacZj0OjP5xFGdSjWe6wq+jLbXVgMc6zkcF/47Mh2tcnuVQ7ShoHSZx8TTqDGcEHArxW2cMSLW67e1/QASm4J9FJTHPTbtxkNnCicak2zNnyGJ9NnktSkRgVvFyr7GOzNklAztIfyvLr/DK+tqOD9tkDDBRC21c5j3JLfgkDaoR3qU+R8AbZp7QwoflIaE78cpZ/O2ekWM44H5TIECom8pij0GZtp2HtVF5EQLJk4et6XIMYm/W5mT4Pnl0xvShFwdbMcFEwiOFvMgNs9MzyAcdTYPp+uZYB1jEkhDlMmJVDurWbVUZhYD5ZmHFtjeclsXOipGe9rJd8UCrqjI1kOQMxn8pPw2sYV9qKiXW9Uw7O3JlDORTRvdPneVbMhT3sCh3NNxtmdpiQWjx4CxpzZ1zBUWj7SHvpvIRSIv6kfi0Cv5sgPRQrzvJZSbvZxcLObcJa3a5alNLq+GPez3H87M8GEYDf+dbwt95n8TtXsFtp4WmljQ4tySfGNtInJuj1ZmlC4YfRAR6Nz/JPvftQoll+NbuEBxKtfL77CTodBlGdn5ybYRf97nBkEAsvkiG3wuJQnuMpVyU5NmOepLd6az3U60Fs3e9CCj01+ttzhT9bctyI9dKJRpGN9U/FBW2UeJ/0iHi1ykSW77uXqMTzDvbQgpSeKNqxjU/LdAFNHrEYKY6lS9FiKJdHmLEirNHV5iysoxWixiWtMuiE1CDGRLIV8grztJ2dns0soaiGWp6XBhqJMJthHlyM7Y3TxKcc55ctFxM8fEx+iKWO8iRCa5LfQtBIZGgh21wIUT7RY8rNZgj7PXQ6StvJqCK7s0HumetkpXuOkJTnQv/UCe3HjEbkFL/er4c9jh4TGCg4kVivQ3JOLiaYmv2yAXoDeQaFAFN4+Zx0Dj7yfEJ5iGeFdjq1JH3V/JyAaFzDISn8KNrPdvU4Qea//k3k6RDT7E7r7R4XmjhXK+hxY/BJ7uMn4lr+5fBqrmqb4A3eQW7JLwFgUGzh244tOHLwIc9TvCd1EQB7syGUyGEwPGKhIt/DiA4aRWQMwYBVKOE5lilO5zoBT4uHzKYQnoRYvgyFaHGLWy9es3Nar9i2PFTUS8+k2nn08U7eLu0trlt0w4jlcIl5mrQ894mL6YtOlc00gVyUktUbNbZ9Pgqk3RlXaEEi46doPzfcLzPcrPEH7wZAd5wAeEBazaGVES5tH+GMr5ZvrpFn3M66zOjf3GEX/isW63Zxy4UiYe+YKvV9KZ/hAiIXEwQNJ42YW4NmveZ36L3NnPFwW7GEtIsQuUIE3wzjszVPo+bzZlxfYxs2nvJzcZWvhLfS6svg02TepD7LescsRwV4TNbHlEmhic93PUmns7Tel6dXsiPdSiaSwdPiYbUnzl3JTn4T6+MdLYdx1AheNILdmRCX98xCi5snDzrZP+Pmb9tmUSIlclp1rK8RTa5WYMOOONciwtbtW+1GrZgvR6PaOs9XsaYXPDlW5nIoqUpD9OLvDZ7chRLcU/Wm0+h2T6nsoobUwu3UeM9VEf77Dxfg7ssTzKVYMzEI04OkUipOl0hfq0S8ILuIpTWChfw9XYZRTpBXhDX6WqWyqncGutrKb1tzhMmIGFfTslUjsWYZRb3rNLKs0SagLHnFLjHvu2yoWNetyfQxV7TiqrbNenCixLVRnMxIciNoTUTLpnSnFTffmFlBRnPQ5cigFHSvGVXELailUqvPAVa1pNkf8fLgZIhFUxMVhMCIGk+PZGjr9ZQ5VkwNJvlDuoPz+rP4kLlG3Y8LBQ8K27TxeffdS4L/Uu5GQyjqZc3YI7TylNBJm5amjRTj+LlN6iev6JT0dcoezjGIcQHtpDkg6smit0x3cgudZb8fUpt5ieMIOQutjcsSQYdSJC8u7G2/7FA2yJts4kB/npxG5NDilgAmLasRVQw5oUAM4lMuehwptjvLz6Xx3Lx77lGGHSEGvR1lz3p4sY+OQpRzZHec8Wm5JkE2uzPYFZbw7IqVkX1a3GT69PYunhV4Kc8WNMY6ljPJLzmfpwIr+eTb72XZ92ZwUZLr1OPXaxyn1ZkhHHaTv6STSIfKI4sUQGXtcalIis2FIQKjGvmghM8v4vepRQu55bIuCzE8lIcDTjg3T2hTE4uHCtsBGLTPP6jpFGMdJy2VGu1ett1BB6ogskibo0VLs1KLYNiHvqH1GHlNZKMnWuZYMpT1sSPdiguFqOYmHMmwtWWWr6x+ls8eHuC2uS5eGhxbsC96THHy/tGt/F3rPh5LtfLFC4/zzGEHn9vdxxu7jtPsXIDfb41ospX8GgS5LlJs+n6+Qk/1oBpXe74qmr7gybGBEy0X+1yE8as5EZzUfdQh/agbtexULHILZ1xh67IMyzpzHJ5wMeUJEeroYf30IPGUSgDdl1ivgqcTZMPKLeQVijplIyrc1yoVC3gYkZht3RD0ScXiHqvesqI4JctOPaHI2x8iPRSrOs1p53tsRI8bjfxaCbUdWTYPNNaMbuP6m+UUAB/mMT7IFWXb+SvlqYbaZodqEog/Z5ilK4ucab7Z+zgyAq5CdEfV4J0jZwPwlZ4dhKVTazavRDJIwKcuGuUf7+hhTSgJBfWD3f1jdmIB2O9o5RupPlqEDGco47SEBS5huvCrfRKfHYzkvTQSnuJrgo5vi5vJCg6WuebwCAoBSSafkvCT4zLlCBdqx2zT6dapE+wWdVJ8pX+UPyZ6ir+JqNwqL+WAEmaFEGFU85PExT2Hvby0c6a4XG4wVqysZ0V6KIYXytwBzC/GYNHyF669kcCYv0RvmydBua2YqR/LByXaXCozqps7Yp30anMMEC0ebzou0zo3wxF/E5K39EJvTapb3h+i6d6xYh+Uc5TPdllhfclV7hxhbFekbNnWy/qgz6O7UaTEIjE2F+x4jesBQjm9yp20PASHdNmCOems1oyXtY9yDYR4/I1eYm6BnT59Vm5rapRQVmOkXS7KJMwRejCm5XV5C1RGUo22DwecEICYL0coJbKYEJ4WN7k7Slp56wvSfBaK9Y51gbCDT6hP8NBcG/eJfcQ1N5vlKI/nejk03c+FzlHiajv9zgR9Lt02bULW76YcEh9OnQfA17mXUNjNO5cc498HV9LvSrLWEyejisxOCSxqn18GNy272J8NomqgIvCVmRX8Rc8ozakUfxxr5oLwDFe2TlXqrhsZ7+twnoLqbhPFNp+kAk+Nouzl13fqx60/G3JcD54Lclovnst9nzAxnm/7hbdNZ1zh318yzhu/r1ftunr4MR4q+BVvicssCTpYHHTw7K5SmMEgxkWHiiiAQMirFqPMoCfobfOWNHzFRLxDMaTlIXKmF6BsNFfs8M2ey0ZhkGoOFAbMpLpWEZF6EvCsEVNzNMTqs2wsPySHwdT/vCv+COt9lizlGvs4HVErenwyEvlqwXzORQFcpsFaFODz3U+wKx0mrUoLJseNPs/qTJahhJcfHOxkmeriiXwb2RYHb1B2c4F2XG93yIWwpBlBgIQisjMS4oaRbrY6pjjPOY7kWVOx3ZTmIBr0cWxOP5d9WhwBeFToISk4ieJhgzZJHomHxF6OCGEAXqQOMqBFWa3N8DZ1F48LPezOt7PBE2OdO8YjqTY+HrmLcLB6eP1v1F08q7Vxk7iSh9LtrBJn2a82c6VzmGfyYeZwMqu5CQtZLnKMcIvczw25lRwYneQdnUcJt+pR5WhOYizZRN8aF36XWrRbA8ipAulJGfdcisN7/KzpBC1Wbt1onTUydNzdUCyvbJRYTuyc0Am3EV3eOU3r8hDvO3OSu3e0cYu6nHOU41wUPUBzUMATdJAJNnFcaecJsRvUp4sV4cAUiGnxsOiV/eQGY0zuiujdWgHz9T/Wl2WDwCqRDM64XyfGPpVQTiPmchSJZtHPmElC2TyjW110htpwxfK4zNE9Uz6GtbhFcTZraxuHN2kMN5eeFSPyCyWZxHBAIpSTWdykFouGGNCrscGGdo3FvpEikU40CcR8euLg4lmh8J3+m16YxUVnaBnSUKLCPs4O80WRDT25XUJ2ZxheGTpGNuvmUbmbezJLeKOYYKv7EL/MDTAa0f0Df7joYQQBzmmaYXP2bkbUJsa8zXwzsoI/5RdxTXSSxWF4Z+8w3xlbzhc6n2BM9pJSJbqj9tp/A6oGfze2FYCzPNN4kfnghhE2L5chAv6AyFRERI1V2n5WOy929nDAgq3TahFic5S/mtd9vR74p1Oxp/+vyLEVjZLlEwnv1xPZbqQt9aKa88W8hPkEfAa/9KpRPnZzJzMbV7Ji6hkORgWG4wLNB1MsXqFrKR6bKkUamp0wmy8fdHeMaUVXilhaK0vii6dUhg+mCPTr02X+Fk/RbN4cSTJHneMpnWx3mfZhkGCrKb8dIbYS4XqT9gxUeNSaijpYl9tIhr+feoovZDYDsMxXPWEK5ieXp3N02C67/WSiHmeLTkeWNkeOpNp4d3giz+mbBsbxZrNMTSl8wPc0X0xv4mfSOrb6otwuDvBQup3EHr1NAho+ZN7u2ctmhx5pPaLBs3ILO+V2opqLuOZiVG3CI8h0CklENI5IIQQNlmgxgi6ZuOrmEfoIiHnOzx7nnYF9fDq1ldvFAS5RjxLByxItxl9pTzOp+nhc7ua76eV4yeMP2kcBi+cCjbNCMbL543w/u5p4wf33pel97HOeRS8phrQgEc1LQild5yeUDv52tIMr4mO8ev0cn9q1hFhSQDkicW5fkrcv1sUYo21dfOmZXuZkCRkRDYF/EZ5hSUp/5s3PpPnZL+LeMbxBB4H+IK6BkF6MpeBqI6FLKwySfMaaPGf2pBgRZH58Tzv/Ii6lTUuRxkkWiaxDvy7hLe1IBWcJs3zBiHC7BkKEZzNMusSyfsXscWwmFubnwXAoKdpAtnjIBCX8Sb3Yxx+863lp+llCWY27mnsZ11o4X9mnk+TAJMMBCoVPJMDH2uMBWiZF/IVjzBXcLwznC0NHPNIuMxwQy4qHmGURO309xe9BjwIPN8vEfDlW4CpWRzOwbJdAPugq+lQXq/BlNTqmnHgS4PPr40Fg1KjaphRfYIDiLIE5ua5eVHPtMfc1r3EdZr0UISDkGBFW8t+ZjfjI0ysmONsxST5aWt/T4mEZCl89vggAZ0ESqESzbNEmuD4/QFJ10O/SK30YfYQ8m2Uo18SAu7wCyFMZ/TkR0FjapfLmziE6evTzkVMFbjrayrubD1Y9NvM+qp6Dk2Q+YN5ntUJR5u+szicvJPx/TY6tqPetpaoGrhFt8wlso54H4pTbwdXIhu0MwUVLEjxwpJuW+F6kwhTusRkFny9D0CfS7FQrCLGBZqdWTN6D8uQ9KJWNzs1m9IhNIflCORQrao7NA6WdrZKdlhkqozl2frPVEviqTVFCeblowwN0bihesS13oSO5J7+s+N2/SefzeeVu232aUas4yfOFenWOVpwIUV6I1dsZ3tmG11koMTaew5f1RfQkFlF/nr8QeYgPJC/gH1LbaRYy/K3nGVaIUVQEBEAqSEHmNCc75Xam5A6ezK7gPOc4q4VZPKkUiwM5fHF9ZibQHyTpb0KKpxFjKVsS5hZdXOMa4tZsHy9TD+IxeSV3kMJHnlcq+7hIO4ajBjGG0rU+i6NsYIRfiyvZKXSRQ+S9+Z0EQhK/zy5hq3OSqObBnUjxP9JmEoKXv409xK+0jdz9SCcdYop/UR4nEVX5jbyB9xxfTl4TQVF5pbqfjdokN4mr2O9sY5krgVx4d/QGHUX7O52IllfVnJrI4ZvTSxx3UHL+kFo8xRkoY4o5H5SQYnm6Do7z0YvSzHldTIwIeBwqnRPTOFv1c5nYWSqTXFbhrSABMWCuumb0Edb71LBoK2KrnqxmJCNGt4fYsTJfiBQ7i8QYdKkDjPIH73qeTq9m3LOP85V97PT1FEnswwMQWpRncb8Df1KkpW9xMbkv5nIUyTDoxNdYz/jeTJbNn/UDhOWuSYbX6+3ZMKEVIsJqQQaiFUteG//HfGrBTURPNMz4KSYhGkl65uqz9VTFg/Lx0TzO1oxuzmZY74hwXWoLrUoLq6VZ/s6zyza5zlj/za1H+NL0GrY6pgCB3GyGjFaY/VCd+CW5rD1jsoeb44v4QNt+sprEmOxB0QS+NrOCf954hPXN+o2sRDIohyA7m2M47yOhOFjpjte0smyUJC8EC3FCms9t6HS25fw/cnwSsdApgUYIcd3Lm6LDdqT4hF0t7HRKpr/ffH6ML6VaGX3RpVzw+F3kRF06EfLK7BgrjxTbkWRDe2wQ2mMzCqv69IQZb9BRjKoY/p9GlaRoQeeXKkSKrSh+Py3T1eYokmRrtNhKzMyk00qqjXVraQkD/cFilMpKiq3aZWfYw85EaeBJCQtLtrO2+2SjHuJrVyyl1np2yxvr1IPnsrM9UZmW8Vwag0UT0OdMcizfhFdSSWsSD8ld5BHJahJbHVPsUZr5ZW45K91x1ghZ/in/AFJe08+plmN6b6ZozGYQxY5NLbi2dhbLU5vL/+ZmM2xIjfAn5yLuVJfyUu1wWRtdKORx4sLeUcaMMn098BfRPVzBkG5JCKxMR1kizXFbfgnnOsb5L8e5bFHHuFJ5kr6mOdYpD6IiIBVIeCAA71Sf4pbcMqKCm0fFRfxUWk84/jB9TXFmHYGyZKl0vPQc25WbL/0tMzcUL4vwAsXEIgnwmAhZ4o5hctEcngL51fqDJI7EKuQbybmSo4O3UEwEdM9iT3uAjk0tZdG03GwG/9bOYqJdxk+ZJ3PLpEh+Uwg2hUj54eCSHKEshLJ5eqf0Yh+G7nfFUYe+7qJneSnPslPo4SOzfw/Ay9v+QJcQ0TfqgJ0+GNdaYAXF781k2EC1yLGhcV6OXnnPLOswlvl6z7ridrqECDTr++wSIkXNcswtECpEkVOF5OyUD3x+iQD+UnJY8YRUVq41V8qD6kVIqvU3ZqJ2W6yLQSnIeVKU8z1Pld1btusmyseBrCYyrerX3COUW6GN5L18K7KMybyHj05uYTrvQtb0se2MQJTFh4dICHoUPxf0cXBU5NPHVxbXH5O99NawXDRwsmahrds5VdHf05ko/9mQ44WUn32+K2zVK+NYKCreqOf5XGyXTVXBRgT+SsiJM5bngy+a4R9/00Vs0xp6PNBTkEZUixibEUvrkgpDJxxKqcXsb7O1lTGVGd7STm4wVjadai0sYvxt6Jgp2MxByVfWiOzW0xlYCbWV8BbLANt4L9dyzNgzUzqnHvKsUWcqlq0Hp1qLvFDCfbpEtU8WTkRaYX7GD6kh5mQH32i6h3sDy/ljfACfmieiuBjJe/mjvIROR4YPeHaxXIozKK/DFy5ZLxnk0PyylZyTGXpgkrYCGcxGcxX3d0tIwJNWOCYE0DTKku22a9VLkteDJmT6tBjHhBAH1DAHCtXyHKh8zLuTxWICl6BiTSY02voTzyYek0vOF+eoI6z3xdmNi0xeKOpJzefAVlIBFf7oxvPqKmg3zf2h2forOpwqbXMiBwd1kuKzcaIwtpktFBMxiLIWkMvOuUGelYIvsCE5MLyYDZJsEOaWSZEzHpZKkdVC8aseINPn4pl1OWJuoUhUt6ZGOav5UfalVnLP3Hms9h0oeiHr5Ha0jNSaia0dyvTGNomAoZzMckokukuI6ATchPOVfYRycjHaXQ0pnwo9Eh6zj3UVxwWzZadxvq0RYmsFU2u/mI3mmMLLjY41fMjzFH6pb15irGgCP82uAOBrmfUMHTMqsmpc0jROi6TvS9XgvmQHP4su4ZWd46xsGmF4xsFwvokWR5ZeZ5puR4Z3jl+ChMobDxzkR9lVZfv6a/duepOmpNUGiyHVeomvh2M837KI54tAv+DJcX4uB9LCTtrz/aZiFw2udbM+V0l8J+rcYUSRXXN5Xtwf43tzq9jevYyppdvoP3aYsHO2OADbEWX9O70DNQa5Zf2eYjTIbH9kDKJGZSsrzF7KMUv56lhaI3YsX6y2l5yTYTiFt0Ynao5O1bJ/M3+fjstgqX5mFz11hVzcmevlpzk9YuBA4VPex/DPJVgIGiWhC5VAnEqYI9+na+Jhree4pkOOaT1ZE/hZdjkvD43ilDUuOL6bCyhck4TpmhT+zAJyTi0mmxkw6+HNMyjj04nis9BuemETlzTz6bE1OGWZXm2uRlHn+mC9f5rI8zHlEUD37xbRaCKPV84jZsBlM0OTjeaK27ksc5i4KOFE5dzEEMvlGQhKHBSa0ShPai3PGbAnyKD3BT6jLynsx19wyDBKuxtFeaD0nKdS+rI+n1h122YE+oP6y/tQDLm55GNdHHcK8g2DGINODH0pXY5AYfKoZ2euFIjYOa230bQfTyTEGXF9I3M9AgeX6ET5r3L3EPM9UFzuEB08IK2myxcpI7t2pNgsmTAIr1VWYSbJxt922zIixlaEshrDzRDylVwvrDDkLUV5hSmx0Jy4aA1qWKvwzTeDFSDHuepxvpLewLXZAF3z5HFIgka6IKEYUnVifI40RpA8tycX8yoOMukL8+9Tui1ns5Dl/okgN2g9KAiIaKiIXNI8zVBOd8JQEHlM7mCjNA0aLJdinOucoEW05wQLCQhCY0G3apUS6+mLG020tiP9dp7XzxVve8GTY6hdH74e2F2MaqV9TxUWYpD9QsD2vgS3DgbRRAHPmnb+1NqHf22aC555gFB6jsem7Ifjx6ZEmp26//GKsEZ7p25rZa6mZFyj+bK/S1WqLJFjEwwSEUyptBe2ZVTVK7NpMhFiq8bYTEzmS9izkoghQvwgsYFJoYk+Lc7HlIf1H6rXcqiAXSnnRvDnQIztnl/j83OBRmd7UqrEw/lu7lT7CHoVLsgfAaH6gGJX6tyM6ZHSMdsVozDsEY11Z2cVRtUm3ILMKm3mhO8hA3YvWl0kK5abbx99zPF+dWeh8QASaSQeFBbxD+5dZVFD68uqHYkN+kTdQtIyC2UQY/PLjfmecYddxZcNo6+Ip9QyaZbRhmICXdhNeEs70SenUDJqsQ/IzWb0aHfBV9nQ24IupXDGFcJ7ZaJrTHKPfj9zPRKBkBOpQJCL9/dgIe7e4iYAbEi4+PL5uu3apbN65D/mFtia03XJBsweyVYYEWbrMgZJNpL/qm3HiBqbZRtGpNlwughlNRbPChhzFUa5bzPsKrUZjiP+Fg9eSyDHuHvNz3+1gibm7z0ovFndzSWJA9wrnMtGXATJ2fY9xnof9T/BA3IPlzqO01UoNnRADjKJh19n+3k4qc9LCqjMam5mC1XzvMikcRAgS2ZO5o9yF2917+Ncx3iZxjkbzeF21+737HjOQriLHemsVUK82nN7MmR883G33GyGvHIaWrnFYjFuvvlmBgcH8Xg8nHPOOVx88cUAjIyM8Jvf/IaxsTGCwSCXX345W7fqFiWJRILvf//7xGIx3vSmN7FkyRIikQif/exnecUrXsG5555btp/rrruOyy+/nG3btjXUvoVMAdQSjZvJ9nMdabZGoU7UDHuh69fUJs9TrtLvUvni5cd5MJ/AEZthNtGO5IG7tl7KRU/eC1PVbW6MqPJjUwKhoQzrN/krljE0wIYkoikuFyM8BqxG/OYKewaMKdd4SiU+lKGrzVGsTpaOy4QX+8oG/GqkuN5iIVDqSEZVH59Lnc3LlQNcph3BaUl8qhbRtSNQjVbCanSd5wPPR4W9eiFrAjlNxCdWL7cK9qT5qXSYr8+sYFlTilc2T3BWKIoWK3deiZqm583XyRt0lCV52fl614pwGst2hDP8ffs+Pju1hlul5Shxke3qcZxBFyoizWQajiZP4yUS9dCNRltYX3u+e6wRUn63sJTVvgSLpURF5NyANeHW/Hwbz78RQU7ujtM0nCK82IcLvX80rgEABVeH3nV626ZHMkVJRS2rR4Nst17Wx5Sr/B7OzWbwFqrz6VXJdAmBcucIhN1kzJ7Mhb418LuJqk4BuZ2ZouuEFMvzypExvt6zjruadYlFzOVgw4R+j8aKuubqlUHNFnFQrideLk8W9cZW5woDhqzCINFmOYVhRVcNxTLffgCpvNiLoTEuJCkWv7c5L3b3US1PeoBWZFqVFLeL/bxG3V/cjl0f9MvUALvFdgJinhdFdTeJuABPSe2AwCIxQbeY5HGTLMipKaQFB6ulWV7lPMyvU0t5k/cA5zgrI+7HCJCPiqwJp23bWg12LhL1cJdqfeZCxofTcRayUTREjlVV5Yc//CHNzc285z3vYXZ2lhtuuIFwOMz69ev54Q9/yOrVq3nta1/L4cOH+dWvfkV3dzc9PT089NBDrFixgqVLl3LrrbfyN3/zN8Xt3nHHHWzcuJGmpqaTfoAnioXeaCcKa6btiZZRXHCy4ElwthAFaA5q9ATT+LQ8F/TNcXTGyb4VG2gfeaAuDTIUorUFY3xz2WRDZ+lq9uCN5minpCG2apDNsEvYMy9j7kDrJad2MMizmUC7wy4ez7fzvewackicqx7nSm3Idv1a+26kA1KBcfz0sDCZxnOJhSTgPZfPp4FvzCzn0XRb0QfVimrP3a4CMf7g0iHW+/XpAWMa3wVl3r5VZy4Sclmk2AwrKfb5xLJkNePvbDTHhuYYP+p7hKfTIR5Pt/KfyfNwpDTyDhGPILNdOc4qbYaVWm1Hj0eFbm51LGdW89DlTDOR97AyPsMHArsbul9rDawKAvc7+vi7wAFcbv15N14M7IhqtRcEI6dhfFp/JoMptbCNEtleVCCuhsTCmEnqXRcs6pANGH2QmXx1VLkPixrYJ6cIF74zlONGYrF0LFNMCqTFXZAWlFxujPNkRnooVtRPLwu1cX6nbutmOFYYrhShnE5SYy4Hi+fyFWQ15hYqosZ2comYy2HrXGF8ZyelAIpFTKyfjahxqow4i3rVPEsVNmvgyI4UW2frqhFiK7qVOb4n9LGNMZYWKvQYBNl8X75O3UtCPcxiWX9unxba+aa4GR95Urg4rvr5B+9TPC53skiLc1wI8jp1L1u0CURZw51VeD8zkNRlUla0oJE+CZP7RiTZrl882UGEagnhL0Q0dOZnZmYYGRnhrW99K4FA5xDD4wABAABJREFUgPb2djZu3MjevXtpamoinU5z9dVX43Q66ejo4Omnn2bXrl309PSQz+cJh8OEQiFkufwm1TSN22+/nVe+8pUn9eBONp7LJL2TZYZ9Mkpq14Vq1XcKcOVlWtwynzrrGEokw/H+EHfO9HPGsmZm90WRPR4SXd2EjgyVRap0aUUlaTV3fMb/udlMUYfsNT2kTQFsbduM6I/d4KlPn+Yqkm6MfVVzs7DCLtIMkNIkvpFdD8BH5IeLnXC9WMhb+WGauU0c4L3GNHUBp8tb/smKED+XszxvbxnkRfnxhtYRQm5um+3lFZ3jrPfPlSUVtV67HCXk1COIBRjRSqhuQWguclPtfraLFBovma5mDxu9MTZ6Y7xZ2IOmgbPZw2DOzwOpdr6RWMLfyY/RV0Xn84CwiNscA7xjyQhrm+YQ4xmiEZmPp87mA8nzWeuYpYk8LwkOE4zPf69XuycPCc24UVjm1l/wrG4R1SK51uRcKBFk0F+Uj80ohLxycfmOHZO4tnVAiwcv5bpma99jJ7MqyicAI/PXcBAp4smpskIchsuCYTNnaG0N2YfhdGOVBRgobiea5aXBxUQ6Jnh4QD9Gq/yhKHPwqUWpw+JZgVDKiC6Xzq153cVzpUiusd5yysmzWaNsXh4oI8bWz76UWCTHvpSoFxUxHb913DI+2xX5MFAvKQbY4+xgt6ubvHCUzzrO5aPywywxEWQz2kjTRimqu0Kb5X3qTpq1FJ90XATAiNJEs5bmI8ojyIhlVonzoYk8TeQpiUVKaET/a6fDrhcnOi4sxHHodEFD5Li1tZVPfvKTuN2lG1QURXK5HBMTE7S2tuJ0lrKnu7u7mZrSpwXPOeccvvWtb3Hrrbfyxje+sWy7V1xxBb/73e84++yz6enpOZHjOaWo5+Y60UG5HlJs1cYZsCuracaCLF0asXwzFw4x2+60uMFTNOTRq0dFYpzZnuD3XMjGwzcz3NrG1JYtxDesI5yI0r5rF9mIPhBv6y7PDDds3OxgnP9qpBQqibJddMmItFVLurNWlTJgvUesFaiMdn0xvVn/X/5TWYc5n6b0RLCCWVaYiLE5YeL5Jsjm/b+QOlGfqLDS3YAwHHgg2sJs3sn2uUGix/XB1R126eWBAXZOM7krUmFHZtYLm78He+eEemC+5matorvwzC9OTfNGppEdK/mdupxLtGHCWoZOkpj3eMTdwkXiKCunj5E8VEhcC7v4vO8hZgNh9mSCHM4F+PfUNpxOlQ4hTY+YZEN2jHXBJEY+wKAS4NNpXUonSSqKIPIZ+W6cqMRx80txNS8OTZQ9Z8bs0HznoBpBtvsc9MHQA5P0ml60wlvacRciyOHFPgL9QSZ36dHRjk26xnbogclSW4ZThClQm65Sv2MuPmQm1dlorhidDvQHK/pdKezGHXaVRaer+bLnZjN4756gZ3mIxe2l86JHiTWGA049OmzS/8bcQjGCbHyvR5rlMoJrRJpjbsHkPFF+X4ayGiH3aNm2dH2xDsMDudpn0CUlUixfdA4B+zHNOE/ewmyrq9lTjK43QoyL2zTZFv5WHeB94lN1redFZrUWYbfQRrOWZlbw8rnMFs7WRnCg4UCZt1iUHRqxyzwZfefJHAee73HlRNDQVRJFsYwYj4yMsGvXLl71qlcxPj6Ox1P+MHu9XsbH9ahKa2srH/3oR1FVFYejfLerV69m//79/Pa3vy2TW7zQcKqjVVaZRa3f59tOvUR5wbIKa4W9TPkbs9Ti4R2BMf5hbhm9mxcRmk2Tzqd4TWY3D/mW8OxFV/CPe3/DXYc1YmmNZQHd29ic8W2VnpgR6A/aJuuY/y7vOMvLTFeLQJk7N/NDH1VdSIKGD6FYrMG8jJkga+gZzi9WDtckxs8FTgfd8YkQY2sC7ulekUkKuxka9XGme5qWJX6klrbibzN3HitGiIvT/Sa3lVJiqY68rFXElOyixnak0YiyVpMOWQfca92H+Z1nOb9PrSSCB0WFreoYL1aH6AxrLM4leFpp5fLoobJtuMMuep1pep1pLtUm2N8UpDksMJ5zczTt5ecz65lJuugVkzTLKQ6LzTQLGTqENA5BY7fSwsccl+jHQZ6LnaNcmD9CNpYrJsmVH7tcFl23RtQNgmxEjM3k+GBUKBYfMs536vEoG1+7RL92hQiycWzZaK7YJxnR9951QbLRHMMHU0DJ/Ubpl3FY2mMHQ3JlVPAzF8CwugjNR7L0CHSWMx7Wk/nuXK9xqLmDS2dHWDyXL1q/HXJ0FJIdYSu6B3FRH5ytjBLH3BpQIsbG98XlXLpbiFljbIVBhK0SC3Pk2Hn3BDlT/25X/hnK7dvslrPC2v+b5TBr85O0ZY5yVux+vhS6gFY1BQ2+d67TprlSHeRnku71fLV6qGx/CyHIjeJ0IaTVXEJeCFjQFXrmmWf4+c9/jizLbNu2jXXr1jE6Omq7rKaVOh9RFBFF+zvt6quv5vrrr+epp55i8+bNC2nW847nI2lvIXjOnS8iWWiv/LppLslrFk9xU2YD/bv+SGatk35vinU8w8fVCxnzhIEIB6MCq+bkolTC1ayXZbXCnMHt7Q/hokSWrIb91WAlxqm4jM8yheoOu3g438nNmSX4kBHnVIaE5opt/aV7H2dzlCm8Ogk2a+HCOdZp08XPjeK56GCfC5xoxNjO6ud0hPmZ80sy+xIBlOjxYlGO3GymSIzNBNeuuqMVZrJlNwtSLbpsJ88wYHVWEYHXhveDTy9SM+4NcetoK9elzmNDfIInxS7e6dlDeqpcdlD2MivAWk8cMrAo7GZbMMY17qMkVYm9mSDTipu/8Q3jl0qewKoGhH24BBVRoKD/Ld03VpmJVT5iPh/G3+YXDjNWhDXyokTW6eYnW69iID3FlulDtN87RnixDy8lFwvzS5jxIm4U9XANxuBgqtiWep9T8zOtz9i5i39DKfiRnNM11kbCcLXtFxP1IhkCO+HVe0MooXHuvrjkh/yAtBo0XR+8XJ7UE+x8FneJQvTYCjtSHMrJFV7G1uS7Mr2xWwB0lwp/Uixa2YX3ZsqIsQEr6Qv0B20T86xyGwPWF0JjWeMcHku4uNfTzyOhC3BoCt3yHJqDhpNSV2oRlqhRjophWimMSc9Bf90IKbYbd57LMeV0tueEBZLjlStX8oEPfIDx8XFuvvlmHnvsMQRBKCPCBiRJstlCJdrb2zn//PO55ZZbWLt2bd1tGVt9FVG50iLodIHkPn0IjOCu71qUreM5Oe2f9S3jQPs1xc9aRkZod7A4I7OqrYNF69YScgd51HkGS5jjFaqb6TPPYPtLxxGArN/BuEfvVKfcDsSQGy3kRChkMmsZGZYUtp0tRGO7QMnK0A9ZU0dgzvIH3S8WIF/4Pwoc97WSFp1MiU00qTk61QRx0Y2KwPL8DLOuMMuEQHEbizWZvCDRrGVoIc1hoZn9nMMhVBREHKhs0KZo0TLIOZUXOdsZ19bjlGPMB2t7q8E4jnqXr4ZEz1pGznvrCW2jHkiF66lk1OLfJ3X7pmdPycrP67NofvbcXQFWZEUG3ZtQsrJu8xWQSXaoGBzEkHjmZP1vcyqcy6EP1fKyDSRf8U4bgzR75BzVh/ik5Z6xu4eMa2Scx0tWSWxMSUTkLZylJgmIS5ms8pxZ11WyhdK6Xv2eDXlEWtwOJoFJIBfLIXlExhQfe5RmrghMFAlKIqzhyuaL9ztAKqU/806HQFLWcBaONS/rY1JO1sgV2pCXNXKyhssh4JdLY1ZOhsHu5WiCwDmAU1UYESV2zY3SpqUJdfkQlkjI8YJ7UHfpvnVscpf6ynbQWmOkUgpJIBt2kuxYQ9O1f0MuIRfbZz3nxjmTPCLOjiYyw3PIORVflx5wUZplsq0y+cVq1WtW7TmS3A6EvISQcNB3v5Nel0A8pPKqQpPjrqW4FZWtQFYSiYo+PKqenOhBTxhzK+UkNyuJBHMqLhlyjqItMwCqDK68gCMvkPKp5Ar7ccmle9mVFyCqEQMcidK96UkJTORTaKtLs2pKVkbpLt+/5BGZMj3TgltCbs6idKvIy0vLGv260yWStHy23udpnCzq7uLSc7aSwskxtvOENkmX1phjhIzAWUKYa7QEI7y1oXVrQfKIKJn5PbZt25Srvl6tMeNkjSu10Gj/n3acevOGBY0Wbreb9vZ22tvbmZmZ4ZFHHmHLli2k0+U3UDqdrpBa1MKll17KE088wV133VX3Ot37biEUP1b38s8nnu+o8kIixifDrQLgQPs1rJy6iWdnfRxLuXlJ72zRwzc15+ebB7rpyMWZi+b4fUs3q3ITHHa0kZzawzmRQ7R0unSrpYKcQsp5UPx+JEUvMcr+6eK+rGVDjShTrQitObqgBJu4sVkveetXs7w4fYC9jhYOONtIisY5LCVMLVcjfEh9vGz75wYdXC9u44DYysfkB5lKO/m+fwt/oe5mTWyEGWc7N/nW8ZHYvcVyufXAHL02Z2XbLbNQjJz3Vnof+v4JbaMa7Cy7TlX0oFaW9smApmHrUAFwOOvnJ9ElfLxjDw5BKz579862cMt4L59sfxrnsWmmRzIcPmbSc1psBnNpDa+plHo8pRaFOOq1f43yi29V+BhD/fKKavKhaveQ+Vq5mj30G+2s4ilrt76dm4D1HjC0t8NNS3gwu5qlyrNcuXSO/dkAn5tcz3/Jd+GIl8abqQmTdtpyrOPTMhLg8onF6LGSUmnyiTRRiiBPa008vO1Kgqk4L3vidkJegcmObn7Ydx7vO/AH2gdKHsa2HugFKzWA3Mwku2/XJYW9K3yMe97Okj0/rUjqM2/PiAYH+oO4hBBP/mA/AtC+QreRtDpkGDBfQ7vzaY5ym8ta0+JGCTmZ65HYsThfVtTDMB8zR41DWc3WZcL82Ygmd84K+JNi0ZZtslVmuFnDY9qGPynSZOiMwxS/65zRkOYS5AZjdUspoPweNBfCMRdtMlse2p03gOClb6f9mRt4X/ICAF4m300zte9ruz54Sc01Th1qzSbYSUmMz7W2N98yC0U9mmS7MWOueTFHrtx00ttjRkNHe/jwYX7/+9/z/ve/H6EwKgiCgMPhoLOzk5mZGTKZTJEQj42NMTAwUPf23W43V111Fb/61a/qjjjn5/IVCU+nc6genhvLqUYqdT2XEASI5cxThx42CdNcf6XM9U91oyoaXjnLuBREFiUeblvFRnkSb7D0INsRdrMmz5zBDPp59vaHSN87VjWjvSng4Prgdo45wgAsz0/z5sQT+DWduJyXHQYgh8gj7sWkRSdBNctvfWs4MzVMOlc5VfcuHil+bgPeNreDbwTO5jpGWZ2fYlpq4iMtV/GFyB/qOnd2RUSqEeNTOWW20CSLeh0+ThasWdon83nTNHj/+DbeGByi35Xk/lQ7L/KPEZJkZE3gCzOrcaLgDLsQBcgoIv9zfClHsk38XetenMemGT6YKtO/2sH8W9ykmY2lNVwyeKFCiwzVE/SsmnrjnjC7YUD1+8rcv55I9rtdgqsV6bjMZo6BYzU/kdZz5tAd/FJcASJI8XQZUbQer/kZX2bj8GE+X8Y5DWYSnHnoCfb3LC+mZC1LTNCXnObOzo20jzyBoRC3Ox+GlZq5TePTJgvH/hCuaJZ0IdkyOScXt2e0OdAfxFWo1gf6tR0+mMLnqyzuYuxjaiJHe2f1Z8l8ndJDMShIeaSI7uXsCUqsPS4x0p5nOOAsWr8ZEonFs6ZEPQshNiOUEom5db2yPynReUgDFPJBiQ4c+JP6NszFPoxEPOOzTqYVlH4/UiRj60JRixhbUa2vt3vJMPdHmUL1uwszgzQ76u/rNOCgo5WWRJo2NXXKZAq1Aj3mMcBOX21drp79nKrjqGcceb700w0dcXd3N/F4nN/97necd955xGIxHn74Yc477zwGBgbw+Xz89re/5eKLL2ZwcJDh4eGG7dm2bNnCI488wtGjRxtaLxvNcZgw/eQaOpnPJZGu9gDXG92qdzkzET4dSLESyUC7/v+6FlitRlAipd/yIS9v/f0A7141wtedvXwwdj/fbDqLVyt7SWcFvtt1Plen93GBFislIBl6vJCz3CS+SrKhEs3iDZZ7kRodpCfg5JPhy4pR4XfFH2GlPFOxDQAXKhdmjxQ/n1sgzfXAq+VRBZGM4KBJy8+/gnldS+dUi/ye6sS+0yXZo1FYn78TIcuCAFvd0xzLeflaRC/3/WS6mbeEh7gr2cmc4uSf2vdz72wrg2kfj8fDbGhJ8fm+Z1GPRou+pmaya0Y1wmy3rEH0ahFi43erpt5cctkaTTLDmohaqwDNQgZU636Nl1glnuNa5XF+0X4mHxMuIi3q+92dC9GP/TNqwHw81QgRlGuPV4wPcqB7GZO9i2mOHEMAXjq2k+/1X8rT8XaWHJrGqSnF85ick8H0YuGNlpexLji4oWTUohWbN1hqi502VolkSA/FytpVrZiL8b352Oo577nZDMxmcEWzOA+56dzaRuchiTNjWTJ9U2T8Ajv025pnOqWiNzIYCXkQczkJucvP6eJZgcVDTjzHMkULNk+LB2e/H3oki4exDnOEOTBaklIYQRDjTsvNZooa41rJ52ZHj2rnxBo1NhPulObgU6nzAHhR+iBpbf5za3x/k7Cc26VlvD7xFG25VN3R2VpotD+vNzBSb5DDeI7+HHJcGkFDR+vz+Xjb297GH/7wB/77v/8bn8/Htm3bOP/88xFFkbe85S3cdNNNfPnLXyYUCnHttdfS0dEx/4YtePnLX85Xv/rVhtZxh12s0NJkYiKBsL0HpB1O5dRuvabb9Q7SjRLjE4XRORUjsg1ILMzrSC0eXRNs+t68D3UmzbbWOF/f3wvAih6NCw7v5U9tK/mw63H86Sw/929CnnyGy2KjhMNuiGT0KmIhZ5kPphVmNwO7KmIAv/atIym6WZKP8L65hxtOvqgX8QL5fsrVw/bsUd409wQ/DpzBYamZZYp9gYV6SLF5uXo60oV2dAtZ73SfxVko9mcD3Jns5n2t+3iTY4g2R4brp9dwJN/Emd4I5/mmOSyFuWeqjQuaZ/jYxmMMBDLkBjNMFqq6GVP8Btk1uyXUQsgrlBFkI3JsjSiaYS0CYoU1UamWZ7DdLF21iLAdqk3tmvdtxobUKMPxQR4NDuBXMqyZOMzPO8/kvaN30dmk2jpTWNtid8zGeTM7WMTSGmdN7OOOgbPx59IE1QjhfIpLJ5/lro4NZB0urp3ewUBmumyfTQEHOUS0uIxApb2cnFOZG4oXSZhVJtEU0CtyQmm2zy4J05pwaa3U1+jzWfRiLvhqK4C4AwJhN5cvD5Hp8/DMuhyhlMhws+Fgof+/9rhGokmXT4y068dhEGOlUMQmN5sh++QUgf4ggct6CQA9MZlMn36sRtlsT6LUJnOwwzzmGD2JtZ83uxLV6m/s7jXDFtTsfrRfCePSDvOe2EN4NbnqNuzO9VEhyBXKYS70TICn/P6u56XxZAY2qpHaRvpkq5/3ycTpbvPW8BEvWrSId73rXVV/e+9731v3tlpaWrjuuusqvu/t7eUzn/lMXdtwBpy4Nf1iOwUNZ7hSk1kP5qsVvhDUUyMcapPe56VsdRUSXIskW0lvxfcB259RIhlEAT7YfYj/1fq4M9LOj8d6CWpxFiWn+afQRfxz/k7ekHiKG5s2EI+KvGZwisBAECJZJNArSBnbi2bLosfG+ctGc0VdojEoGgPLIx5dHXYqiTHAyvw0bk3mD75VnJEbYWXkGFLTJh7w9rM4EeOoo5kDjlYe8izhQ/EHaFFLmsr5Os1GSInxd6Md3kI6yBea5KlerHTN8T89jxOUSuf0R30PA/oLal4V+Pa+5Xx46WGW+VKQh9xgtuhfnCpYhRkkN+QVWFGn9jzoE4mllbLPZlhdGIz7vNLVofI3KyGuRZKhvlkEuyhetfvV3EYz6T1fOshT/j7+avwBWuUkpHz8sm0rb5l8GNF0PAYhnZrIlRFHOws1O/LZ1yqxKDvK7Mwhbl51EfEjOzg7fYzN0SMMjA5yvKuPn3WdxfLEGOemjhASouz2dvGb8BZygoNWOcnbJx6gKwCGa0ZyTi4mgRlwNXsIU5TaVvTxxnm1mw2Yz8+50WfMNmADKDsmEXfAmbt0mcfKgp5aCTnJByWdBIecSLEcnaFCfYOdEyhQVskv0B8ECqS3EMhwhoo1AYv7dMYVpFi+NBtYmB20CiztgiC1yo9b+y0zaTQvm1Kc3DC5FKHXzT8oj7HInyZdZ22mBE7iuHmv+uS8OSSNEGDzs5JFQhEEfNr869uRWut9US0P57mKEJ/OxBgWmJB3OsNa2MD8XT2oNV1oXuZkDfRWn9Zqv8+HhWqMrf6Z9USLFxJJng+iAH/Ve4zz+5J868AixtJuXuUaZHcO/r35Mr4m30ZoLsO3wmfxRHQJH7/3Xjo2tSBF3LC1DUJO3frokO7+UCzFW0hGCfQHaS9Ejo3oy/i0TNAnclF0H/eGVyME3RA/OZUJbY8ReMvcTr4dPJtbvKt4tbaba9J7GHS08KSrh5/7SwkGnw5fytfk205aJOG5klxYcboS4xN94RQEyoixGbsTfr4/2scyX4qluVmUQveRm82QnJOLHsbmCm3zwUzk4imVvlaJWUfpswFjm9YXQLD32K1Gks1/Wz2RT3Sq2PpiZi71bte2eEqFVILepgl2uHp5lXeQK2ef5budF3CvZ4BLMoPFddo7XWXPuEGUrbBavJm/B7h88hnas3FuXboN38EMfUKSsC/F2vgIS5JT3NOxnl+3bCHWqUceFVGiLRPDq+R50tHNq4LjZccot5XGo2pVHa3jjDUqbH1RsX42J/hZnzdzYnI9z6G5jbmdhb+Hyp11Ejb7MSO8peTfqUSz5HZMFsclKZYvRo8NUgygHIoVkxqLKPg9G2OVdayr5m1u3E92RNE4V/+PvfOOkxut7/9b0vS+s33ttb3uvZzL3dlXOe4ODkInBBIChBYgQAih/JLQQiihl9CS0AOEeoR6XIWrvuJydy7nbu/au942O22nS/r9MaNZjVaasju7tg8+r5dfntVIjx6NHkkffZ/P9/PVfnNVhf8b7eSBQjd/nR+nNT5BLX8KbRzLCLzb9gwAvli4veL7Wqh1X9YvP2cLMCk42JAfrtleNZglxDYa4f5jwNPu6K0I8WyKHZhd/M160JvpIBuNFs+m5LQx4lvrb+N3Zub0s8EqJcKnl0c4mfLwtbOLuUk5ye1CH3uEDtYWBtmaO8ejzl4GUzZCpSlBs9TNai8dxQdKoUwqdsZP8IfgKo7TwnLON+1YzLCyMMba3DDH7G2MiR72OXrolJNsz51lUSxKuzzJ972b2e/sIRpXabZiXB81mSsdmf6hc6kZv88WatDJl55awgs7znOdeK5i/OnvPWbEWC+X0MsmzAh0PKXgsAnIhraCOmcLjSjC1Jg3ftZHXME6Klkrgqyh1niqJqWoJvsAuD7yFN9dfA07zg/QoqZ5VuRJvtF1FSvOjxNOFWVJo8M54imFrjZbzRcBq2MtRppzrEwO8Vvg1yuuAuCm8/u5bOIUXjnHc4b2AjBh9+IuZHigfQ2Ptq5gfbSffS1L8cYUunMxljjT2FDIxgtES3Ia4++hv17icYVhyccKb2ra713t99fkAcaiC9Uiqs2AsUqofjZUC55oFe402YL2vLLHKwtDyUF7cRutqqqGUrS5Goz7N4uYmh2/9t15xc2d+WKFyi45OW29akhSjIK/p/AQEmpDAYhGiOg6EqiA4J6ecGfWrvE7M+lTIwndf2x4WpDjZmY8VruA5gMzyQCvVia6kWp4M0GziLGxn0s9KTb5YzyRb6cvG+G+fBcbA2NsSg/xqLOXTy+8mb88uZtd4Ritp4LIfT7LtrUbZ/e13UhhF+N3DTCZKJSjcRnJgUst0JmINOVYqkEEXpN8jC/5r+RjoetBVXlG+kTRskme5D7XEi7PnEFSZf4p/CxeMvlkQ0l/VjCL9jWbGGsPeT0hvtgixmZolnRJCjl5MNpC2J7nevEc+Whm2gNJI2hWEWOzym2xtEosLU+3eSuoSCZtGSPGMF02of2fSlW/L1ZLZNPDSsZWreCCEXoZBVQW7YinFJzRGJtDZ/idfw1/Ed1LSyEFgsB3O3by7txtZEtex9UKptSDsiQjlWNFfJBjgR7C2QTnRD/LdVH6gEcklJ/ktq7NDHlbAbgqfZIjdLHf28u9gZXEbG6caoHLnb2c8+9gZb6oVR6RfOSROCcEcEfzuJU8GbuTM+Fi1PT18n62BIYbij4aob/uzKbUjVFms2vV6jvtb+26meYQVCrSpH8+aBpkX+k7vba4/HfY/FklhV0VZaSN0D83tf4a3X20sWd2P3oiEQAJWuQUAnBGCvGocyHXZU7Spkx/qdGQjhcIBuDTkV9XlQvVCyNZNl4vgm69Wvfvmcg3/hgT76zwp1+hBub7wd5sffHF4FZRCxoxNhLkX40Vc71fO3Yv3+i6ivuVCa7In+LfI7/hk8Fr+F7HFbjO38fOPcO4+3xTkQcdtLMnhZzF78JO/H0Bxs5lygRiX8siNuWG8Kvzo4GaENz05GMM2ELIgkhfoUjKn3R08UvPWtoLCa7NnGIPvfzEu4GAkmFdfqRp+5+vG+DFSozNSs3OliBLISeqCgeSftbaomVibKx6NzA+FS2rV1JhhVzJyk2DRiK1/Xn9U+TWGHXUzn87U8ljwLTorZFU1yIAesJV78PZWO5Zg15WoUXHd44d4SvLbuL8ZICufJz/1/9rPt57C6Mt7XQxMm1bMxjlJdrf+m00ydWNw09wLNBDzO4haHehUnxZ8XkkhrMSd624gpxo5zXDD/Dtzp0kJBfXx45wfewIAAnJSVp0kEyvwJ2PcE4KAiohJYNXzbGsMI5DlUmIThZkYvjVLI86evmmawMtcobOKo4cmsOIGQnWv6iYyQ7MtjEjv2bf6f/We8lr+wLKCdL662pa4McYIdaWQcXyagEY48yM1bHA9KqNzpCD/qiN76hrOWVvZV3uPC9KPsm4uJpv+HeQFyTsyDw/dRiABA5O2sMAHLO38ZLUAaCyLHSzYJQ8mOmmrdZtFMZr+k9R4yIueXKcT+SZi3Q1fRRsLh/wVnqp2aCWvOJCE2YzMqz1Wf/dW3tP8sWBpfygZyeqKvJjaS2ZThfXDR/mPbE/8K7wc/h619W0RHezvZTIIff5kGL5ov44YvI7RLI4lgZZMJHh3ME4Xr+NU/YwO7LzV0gmITo4awtydeYkz04fKV+EfqXY31Gbn+25s3TFk/zGvYpv+Lfz+vjDeNQ8i+TaFfUuFC72BAsN2gNbf90ZCbPZNLEVpJATRYX/GVrIgbiPf3AeJ3q2GG3SEu+AaRZs+mQ8I6oRZ207X2ngWLkaGKUKZrKFqYdsoSqh1ApUVINV9NFKkmG0VNRg9G3W/265ZJZd40f4cs/1/P25OwgXUlwVP8Z3fFt4VvYAz2gpksmxcxnL4zErlKJ91vTg2v4DhTSvfOI3fHfjLZxqWcDtC7YwFmwn6XBjU2ScaoG/H7obGwpXx47xy/Am/KMP052L4fPb8CIDaUbEDBsyx6v+ftpvcR0xcp0SP5JW8prAAezxFA5kPCaFG6yeTcblZuO52nOnkWeefl0rMjztOtLuzWYE2erebYAZMTbOWhn7Z7xHfZXNDNv9bMwO8qrJfdgCdh6wt/OXrfewT+ggPFBUHkdEN58L7CIkZ3h98hGcNJdAmhFSvfzNKqDRKDGvN7r9p+jx04AczxXmK+pl9YCeKYwekBdTMRAz0l5t2Q6G+W7vMIelVj56umi6+dvcYjYHBulmki8VfscDmU6+5NnKPz94iBU7i9HjMkouFhVTeyWdtG9rJxyMM5kokAg4aXcVqFEEqWlYIsd4e/Kh8t8aOVjmj3BT6gi3e1aREhyElDQx0UmrPMmo6GFtYbRpfWiG/2YtXIxRYw3ViLHxe+3vatfnF/r7eCTewkcLv8c5OmnpSGGlJdZHfWtB206LQccNU/1GtC1wVTxo9dFkvb1hLReEag/wai9GRoKuJy7GiHHc8L8eWvR4e+Q4d3du4LSzjXChnxuih1mYneCX4U04Io9zVThK2wJXOWqvb1//2Rg91qD9hhpJ9+fTvG7f/3Hngs0IwLbYKc67gvgKWa4cP0pOKZADNnCOScnJdzuuREGgOxflBbkjLJDj2BzitGhftd/qGZkTfCT0DD7ANRCGjxfuIR2fPr1fz3NKP3aNY7jaS6BVVFY7d/0EuFVcyT/6n0AUpuzQtDyUup5nBm1xxf8llJPEdc8KKy1/tWUwRZpPE+Szie3kbRJvij/E8tLs3T3ZBThUmat64nznUB8vyBUt7lqUNG+PP0BISSMBq/NjTZFRWKGRRL162qmGPyXgmeNp8WvMlV/efESNobrOuJHp3nImcA2TdA1zrUeute96sUYe5ws9j/Hus5vIiHb6hQDd6iQCsDV3jqwg8dmRlfzT7sMsvGIq01kO2pFKWc7ANLKsPYgygg0pWSs3eeZQgVO2Fp5wdHPGFmJQCiCiEpZTLJRjtLkm2Zo7B7EkuxJPsU04TsAHeVUkpGQ5YW/l574NdMUfIqBksVMfiaoXf5pGq0S9sznataOq0J9y8cLAAM7Tk+WkMJgidEXdsLXFkxUxNrpUWK1j/E6TCrgDxaprfijbyHn9tgrCqpcZWHkGW8HsgWqUVBilGdr3ZlIKPcwi69rvEc4m8MnFcyQCa9LnUSMC3++4nOVnfsHidf5y9bliX6ZHxvV/a0l8cV2kX1te7EeeXUcfLfdle+k3dxkI9g3+fnacPUnE5uNgeDHf8m3l7+P3mx5fNYSVNJ+O/Jp7nUv4P+869gpdeIN5fisu5Y3yPjqZIspWSa+1nhv676uta0yu1fCg2MMRsZUjhRBtUoYFFtvribe++l/5vlxHlNjoUmF2jer7p3GCu4TFPDS5kA95HmVcdTGkeIgIIt+VNgDw3sKDLPIkypZtw5KPXiXDE4kAXcJkuViTALQq058Rc5XAZhYpbpTEVitYUo81aL37ebqS66fF0TSTGM/G1SKu2MnF87SF6lvfGL0yEuGZyi30xLNaZNZM59tMNKNt7TfxxhO80n2MhGrnKvs4ojD1QnRltp+U4OCjg6v57NEjONt0/pgmkQnt+CcTBWQEoi1ufMrchI0V4L/8Ozhqb5/23c5sPwnRwW2e1dzhXonXn8Wj5Pjb5MOg5rGj8ObEbgYlP/vt3Xw1cCW9+QlCaoZdmTOssKji1yiMEa1CTpm1Ltksscds+cUAsyixEdXIw/5EgKRi4xb/IEZbVKOLRC37tmrJZMbvJm0CXguP4662qYe2cyJTUXACKqUMetsz/TI9qjklWE1nW8GKGJuRf+NvpUVzrx49zA97tnNt7ChXx48hAKvTQwA86V3IYirlR5oDhRX0RVSMLxtmSZL6Ut76NiYTBUSgrZDk2ekjJAUHnwtcxauoHTU2wzXZ00io/Mi7vrwsVKqvOICfo0KYa9QBfKHK82NGHo1jWB9EqaYN1s7pIaGVTnWSMBkeE7rYLxRzQj6V3QLAfwj3EjbMVFa4V4ScdduATnNNsogaG2G8vxwRwgyqPl4/eT1QrFLaIya5STnJ85RjZOOFCsu2bjlBvy3Ej/qX8UZ5b9U+1gMzIlovoZ5J1Liae0UjaJQYa5+ttNCXIi7dns8RjDf2eknyj8VV3CMuYYNznDdGH+O8v5XFYgKxzpwbq+lcsxry+gzhWkRYg7aulbNFNceL2UAjyGpWrr0y1h6gAFfYp/wd9UkWZ5N2rnEMcr+6mMMnFTaImbIlUMXNWCPKJVtUbXpZFQR+515ZTrBoFCqQEJw41QJZwUZAnfotP+fdyTl7C4Kq0iEn6JETJBQbx92d/Kz0wHv+5EHGJA8nbK2ctwX4UOiZvDmxm6WFoj1Vj5ygXZ7kbvdydmb7OW5v5YC9kzvcy3lW6ghL5WhD/a12w2rWzcxIhs2SfS51yNEsSZ+X/xjo47UtxxFjqbLHrhFT1dima2rB2ncXrCOruYKKbPguoNPTah6/Rk2vWTGQavspYrqnbjpeKDsWaOdbb1dWbb/G/ZkR495WyfK7NYlzdJ6M8pPFO3ErebYnTyMCy9PDCKimY0x7PdUnIBpR1hpbyFz0LzdGf+lYWqaXqfMnAM9OH+Fh1yLkWZQX8pYShZ9hP8uf2U8TECVA4mPJYonjYcXLa6jUMxsDL2aziVrCnNk22nba8jQ2/kPaVrWfD9sWclN0eMrCzdC2W5vZ1PyKNds2qifd6T3rtf9zExkGZC8/mVzCS3kKBYFOUmSjOXJBLz/KLuNBWxcgIKoKPnK8Qd7PUmK6ohrTcWX2DL9Ui8+qlbGhqsfbaOGlRrarF80iw9o2syn5bmznUibG8Cdy3DS0qhleK++jpzDJXqGLb6Q3sVkapVXI8DzHaXxicy4IPVluRJpQS+9rlRhn/FxrO6ubMF21+1hPpNzsZeWEvwsJlSsY4tv5NXyKp6b6p/kw6yLIWp9Dizy4S+095FrcEDlOCTZ2OxdxVgpy1hYkJrooCMWH+TXpEzw/XezDamWczmyK60YP8pmFNzFsC/ChiTvwpnMkBQfH7a08JC3kjCOMKgi8QjnI96X1fCmwkxX5Ua7JnGZZfhwnMp+a+A1HbG0M2IJckz5FSrSTEh1T4tMZwqg/NuojZwqrggSXIjG2kjcdT3nxCAUu90TIZa2tz/RkSyNgZiWeNeiJqNdvTiwdJpFjs7a09a00tlaYTg6LSXlaJLFjUxgp5CyTF7Py09Wq8pnBKCPRl3fWfx9PKdhIsG38OL9vXcnWEjkOyBlG7X5gDHdfkPSpWHm86TXIZkl5+mM2i+Ab5TH6qHbQLUx7MSHg4gfiZq5UzuKn2/KYa2FTboiJ1GHudS/l6vQpAiHIqlP9u1/snUaOjWPVSITN1jFDbiLDvbFWvietm/bdzswZLs+eYaGc4KMt1/NYIsRNgeEpCYTByaLclzrtP7XnjkaGcxMZVBUSqo0vZzZzTGkBEQ6IHQAsVSYYEzzEJ520q5PYUNiqDPEq9WBd+4OiRGdb7izbY/fWXHcuNcfG/ehRj4yhHn17s/qlz2fQls80p+Vikmhc+B48TfAM9Uz584JAgcczIxRUgbvkXu4q9PIq51NcY6/+JqqH1Y2r2Ql8GmqRZysibkqE68BMbtRWuEYdIB0v8LvQWgYLHtOqf/LxWDmiof3vC7twnIzx9wfu43PBq4kLzoqobzV827cVuyqzPj/MVdnTLClMkBVsfM23g0EpUF7vlnTR0gkPfHDiDlQEfKVIkF/NsSU3xBaGmBwp8CvfWu7y9/LM3DFEVLLY+Lp/O5Iq8+eTT7IhN8R/Bi4HYEtuiCtzM3PYsLpZNvuGVE1KYdQHXqp4dMzPuOzkoclWtlJM3jErslGNdFlJFvQPH80pQpMkeDwiOdsUOTMjnMYCH40QYiMp1bZPxwv4+wK4+6a0/XqNsZV0w9hHY1/MtNX1SEzWxs5ye9dmEpILeyLFcs8It7es5ebEKVrDLtxM3ZO8/tQ0jbXWr5TJPvVEXIOxUIueFHs8YvmFIBZq4VviBuwovEw5zCjbLY+lFgTg+sxJbKrCpzyXsykzzuP5VlbnRxix+7lOOTNNEmF0ZLG6v1bLe9GWPS508qz00YpKhEY8P3mQH/o2cXjMwRqmginGa1u7N5eDKVWKSOkDLdqx/CK3mF/kl4Kq8rb4fazypjgihPmCtJ2TYgvrlRFeJR/AS960zXpgVxU65AQR0cVpqYXL8tbP7bkmodVIp5kncjMwEzlEreqZzYhKzzdh/hM5ngMosSyvZR8KcLvQx5O2Tu7Pd+EX8myWxhCqzLCZ3cTqIZL1WE5dLKh2I64GK4mLAHgCNu4XFrLSkywn3oG5U0UZYSeOcAcbJvq5euwU3/Rv5U3x3TjqSHg7KwX5x/i9tCjFksBp4EhrD0O2AMvy5lpgMT696pUGr9/GLYlD7FeT/LxtCy4lzxsSj2BD4ayzhR/4NvNjdQP/MnEnZ6QQR+xtbMyfJy0U2/Oo1W+M1WyA5hLViPGlRor1hEO7zl4ROo1XLPDlyEq+4h0itMhDNpqb5gAxk+Q2oyTFWNggyvQSw1BdNqDBKnJqhEaQtSju6HAOHo+U++Hvm3oRrFUsxCqqbuyLGSkOGMisBjcyoqKQSxWwA+tSgxxxd/E5305e/vAprlxRLEiR3DNcYVunRz0vDRqMbiOatlv/++eCPj4dvAaAbiGJQ6lsv9GIo9bvq+OnWVKIcNzextsdp1goJvi6uhGHe6pGqJUbRb3Qk+W8KvJE3MfjYgeb5LOWfUvHC6zLjxCWU3w0vZWus5N8MnjEtN2yzE2bcSwRZSsZn0aQcxMZooqDvXIHy3OjvGTyAO1qCgEbi9UpfXmQLJ5ZEGM9zkt+Djs6TMmxsQLfXNtY6gs3mRXWmYt9NbL+bOUjxheAi6FS35/IcQ1YDfx6ynKKwPOD52i1S3xzYhnfynm5XjrHC5ynqu7TKjmiHuJ8MZPkRhMMjd6VtXC1OsCZbAvZo3GcK0sPbe1mvHwq0pXpLf42roEMctCO+yXLeN2d5/hkv5d73Mu4OX2s6n6OZH2ogFsp3oS1B+MPvRtZlR/lL5P7TbfTP0CTgh2bquLSPaj9fhvrxQg/BzKinR96N6IoMCr6CSgZ1uZGeMS5iNs9RVu7kJwmKrmRVIVFxNiWGmBb9hw2E3I/kySOZqPZ0eL50C6bReT0/9uBXvskAFLIBbGi5lYjglqU0qqIxmSiMFWRjakywGYwVv6yO8RyIQijVZsx+Uyvc9b3xxjhhunk1CwyWrZlm8hUJNjVCz0hruVHrK1nlJ2Uj1vJkZEceOUcAvCC8X0c8nTz39ImUvsPcdMzwLe1E8fJGJwqpkwaX16qEWR9BN3MOUN/bgEeEbrZIo1ydfYUC0nW/6OUYEUM3AEbK0nRGz8JGSBg46QQ4gapvhnJRjz1v5pZy6OFTpBgQ26IDfnzNbd5a/xB3h2+hfOqlw8cWsY/e2ontFnNPBrJsqPFxRfOraenEOMvxYPY/SrpOJxOuvhE6Fqcap6sYOcBsZerlQEWkajrOK2gAsvyEZblzaum6glcvQVvqgUorM55tQIgFyOhbAb0x2s8tvnEn8ixBaqRMrNqQdUIdIeteMEnFTuizdrOSUMti7dmFw2ZK+gjbXK2/gtW/1tmoznyiNhQaqa0PEc5zpfzW3nv4RX8WWyMq0MRsqdjcCqGb2tnOUphDy4gH5CQg3byAQl7XMa1vYPnjp/l4+JlrM6Nstgiye2k1MIP2zezI3cWl07smxTsFAQJv5LFWYcI+Bu+7UgovCWxu7xsMlGgxZ/hk5FfYxZnnEwU6He04HP04iPPC1KHWOFJ4l4U5J6xFn4sbOTH3o28Jf5gOZkPqkeq5qqEtBFzEVmZLx/yWi+ag8mit7YSzSAZBml751QftaINRiKrJbs1ei702vBa25pFY80kBtWkDEUiO0XkR4dzxUiyCawi2npox25Vsc6sPWOb7oCNcGGSpD9Ab7r4YiKhsiE1iFMp8JP2zVx14nE8ywJIoWJ1TOdEBkqJg1Yez9X6DeZJgul4gXggwEFHJ26hwGompq2j77dRq6ktN65ntq2GCcHNJ9KX8V0emraeGawIsrsvWCamBzJBHi10siV7jpdMHqh4gTfCSO6+VPgdb7HdzEklSCrgxxNPlPfpriPJW9Il7UExspybyDCquDgre/kH5SHsqOV9fk26nBuU07zcf4rfxrt5VOyusLmbKe5wLeewvYPnpp9iWcGcIOthRlj10Ap5mG1n9Z1+W+N+9EjFC6QFO8TzFYlwM5mdqLb/2cw+mslPZmtJN9f4oyXHtU6Qlbejflm9SUZLUqM8WznBVuU8fb7qJKGW57EZqtm/NartrVfTXC9Bb4TIWxGoh4QFpLFxs2odcdfO51sDj/FEvoNfDiznUMTDa7d4kPonGL9roLzOwqXBst+xS9O8LQ+yQipOz30huIt2OcmLJg8gCyL9thAnbWGO29vK+3tufCrpzxGw8wHbTQA8z1Z9VkDDmxMPIVL5oiQGXPyfewV5JFqVFFuz58oa6BO2MN/o3YZfyZC0uUniRvA6sKkqZxwtLF+gsvzoGMftbXwpsJNXJx7Dq+Z42NlLUnGyNhhhlTrGfcIiLkv2s8o7yQQu3DT3RaseEnwpyimq4QWOU+yV2zgmB1mWnHJU0RNeM2lEJUFu7u9iLIesX67t2x2wVSSp6b/Xw6wdfeW/ajpqqwe1nnxWI8L6dfWV9vSyjp2nh7m17TJIHWTN+JnyNisTI7iVPPec8fKslilJlQNwl6J9RqmHvgqgFhE3O/6AR6x48ZlMFDjf0sE3pW2sZYIrbcPTtjGi1ouNMeHJDEvVCRKiq8KWU/+/EbXG8skxiS9nlvPqxGNsyFc/BrM+ZeIFOlomGRG8/Ci6iFeLB6ft06x/1ZY5WlwUxkVEVGK4aNOZrymiSHdQ5XepXm4Vl/JGZX/V4EQ9ZG5Y8vG4oxu/kuEO13LCqSdoUaaOoVYinJ7sVpND1NufWutPCg4O29vZXipYMpN269lfIxFcs7FbDym+mOzfLo5ezBP0P3ytE1CP33G90TBnyMFLAv119rJ+1PJEtlpWjzxDv8zMTm42MKu2VA3XqAPUirfrz+dmdYSVcoT/Tm7ibx9cwXJHgh4xwss3jpA+HSN3Mlb+HdKnYsXjO178/9P9vyYl2DkUWMB/+i+nU06wsjDGysQQ1+YOcdjdwzPUM2XpgjtgI1uK89pUuehBWoem0GZyRCOij3tdS8t//8qzhjfFd7O8ME4mVSATsJMRpyoAflXcQkjMkj4pFSNV8ggtUo5zgp8Fcpx7XX085uwF4Ck6CMqTyEjs8A5xhgD/I63nX+QHieIse6c2E1Zkz+iJe6mRZSMEAZaICYYUD2sN0odqM1BWEaFqM0eNyIz0kgFj5NXYTj2aaA1ae0YCrm/DLNpqprev5p+swfgSYbx3P3dJjODBvfxn4HJuztnYlThR3v+N4wf5Zcdmto4domu5s1hW/nisol39cfUfmx51tPpttH6cTjrZ07uYp2ztPMs2wHMdZ5o6U1LtWXWNMsBjtmL5DbPqqEaCrI8cG58FdyY6+WFqES9QjtYkxhrM7nPvl+9nTzrMHfZ1OLqmNPq1fO/1fdUcUJjI4O4LsjQEz91/nPfbruFjhXsIlsqZvjT+OPcpy0ji4BWT+9ngsq4iWg9hVIHTthZ2eUY5p3jZXehiIuOuIMf1oBFCOFv41BxnbUFW5sdwz9C6qNnJfbNxrmiG60Uz8EdFjhv5cZt5c5srAjCTKHOt75qxvhnkjDJNLtEIGnUJ9VDgbcoehmI2TtlbeMC5hA8NtvGqlUMEPZM4KCbqOAgWbd4iWaTlQTqAxKk4V6tn2SWfLdJeGxAoXrRr5SOli3ZqPNlLN6Q2UrO6qSyWo7wn+nvuci9jQAqxpDBBoHRTXueI89HIbeQFCbeaR0DlmK2NH3s38I7EQ7QppYe67tl+XfokXXICFYH2QhIXeT4bvIa2yAQ2FDY4B3lUbmG/r5cXK0cIN5kgWyXe1fviOZNrxthmPbkBAKP+FlrFDG6h8YdLNpojIUo43PK0fer/Nu7f6n5U7Xqr97rRR1vNJA71bF9NR6y3ZavltgG1H7a17s1WyYrab3X1Ohd9+f18UF7LZZP95XyAzfYIQ5mzvH9wHf/+yBO0LPdNK0Ch9V9vmad/odBHk419sIdc/GBwPT1iipukAa6oI2LcTKxUI/yPvJ6CegabYO5nb2XDqSEm2/nB4W6ezId5l7ybbibBIvJnJQfRoC3fFIjy7byLaKSARyiepx9ml7Fz7DzLpibg6sqR0QIYL1yc5daz8L/iWl6v7EcE1uVHWDcxUuyjy7wQRSNICg6ygo0/6xhBEuDw2Raygs30eK2inEbNrNl3zdYFPzf1VKly6uzs5RrJTWnU57me8zLX7h+N4I+KHP8Jc4+5ztptFN3+AqH4EBtz57k/t4TPHV2CQ4JrlH425XIstk/iopSsF8niWBokpPNuhamyp1rEWYsMahevp9cHk3Dd5InyNgUE0+hwLXQok7x88gnT75zIONUp8rbZE2Wzeh/4wOymGFIzXJGttHv7dOTX5c9+Ncs+Rw9J2c5YSiLsbbi7TYWeENdDjOsZa2YlZY1IYeMD6R1caxugOxvnKvUsvxKXc5Vylu6QYknUtbYiuDgmhHmNdLRmf+ol69W2r7ad8eGrkdxpZLd/KnFQD22dWqWdq0WaNW21RnhGHjfXbJoRDv1sQj0vSHpStXSVg77+KIc83WxN9peP47S/hTQ2fjC8gDeGh5GWBwltaSd9KmYZ2fP6p5ZpfXC0uFBUOJIN8HgmxH3JNpJJO34hz9syj+Ilj8ysbcdNYUXEQmQJqhnOjEos6yju2UpSYSWz+GJ8JR7y/LPyIH6mjy0z+zCoLgfpx4uHAi4KyKrA7flebs8v4q78Qj47fj8tVM+pMWqjtWWf797DxwZX8b3Cal6UOlDOzagWoW2EkAqle7ZzaZDU3mE2S2Psdi5iTXwqIl3NTq0WtPNYLQ+kUbgDNtxAOq7MuXNFM4h3vcd/oeUVfyLHcwyzqNGlPo1shmrJixeaMGsX4rXZU1ydPcURezuPORZw+1ArnUKKW8bOsmO7HbtYvDEOjcE7k8Vyo1d7RnhT6Cy5kzGkkBN3yEl232i5XYBf5pbQpSbZljuHArwr/ByW5CM8M32chXIMvzr3x2+W2V5r/StzA9hRuMexjC8EdvHJid82vV9m538mUWEzKY6xyEitcWb1vYcCry/sY0AO8L/SOjYWRhnBwyBewtHxqtsCHBDaWKOO4YpPQpXjmk1perN2qkFPCLTIp9WY0I8dfcQZpmuXjaRY779sbN+K/Jhlo2uY6VjRoqNrpAnOOMJsEM4R8IlIqBQQyQk2Hsh384qTxwiFXUhhF45olpChHbMxpn2eiCp8TtpOTrSxVh7lDco+2kjjplCK2s0dql3PnWqKs3kPyywcGqzkDHflFnBvtotRwcM/yg+bEmP9+WmErOwTO0ni4PPpjay0xfhZbimtaopxwcPnM5v4p4m95SItMF3iYSXzC7fA/+s5wqeGV/IJ+7W8Ob57mjd9o1Zn2jGmBBtf9V/BMhTUiQwRX4iDk2Fuzj1VM2JeTZZQLZLcDMTjCudsAZYQbWq7zY7iWr2kNPNFoZl42pHji0nQDdWneZ9OsHrwXyhibDUORGBNfpQ1+VHyiDzi7OV7yjK+eJ+HTa4J8qrI0Yy/rOO4L9WB/akcr+w8C6WHjL8vUL55R6Iqv8kvBgHe1XILHUwSVtOctof5b/uO8n6fkT7Oc9JHpvWnXtS6gehv3vW2B7Atfo4HPEvwqnnSgg23WqCQUyxv7o3C6vzXO07qfema7Tjbwghb1BGeVzgOQA6Js0KA9aq5Z7UeY4KHVtJ1X9vVXpityGAtBxvjNvry6lbr6Jdr61cbY3orN7M29Z+1/iZOxcuRZK1fVrITPWolDlt9v747x23nl7LXv5ir0yd5Qfowb008RAqJD7bcRB4Rwk7k47GK7cwkQFpilWav9+uRLnDDB/L3zaIYdPOxTR3iWxNr2dZ1AK9kHrfWR43PyD4+l9lEXHXwSuVJNqij+Kp4A8/k+s+rRe/lA0orB3Kt/K28l43qKN8R15GTzMdhPc4wuYkM7X1OPrz0HB+/r42fetfzt+LjZGvc+6pFj7VlWcHGkC3ANjVD6mSMD6WvYqM8zMbcEOmcarm9FSE23o/NJBbNIIUSCm7l4iOXRlT77YzrXQwc7sL3oMmoJ8P3QqLWFC2YT73OllTXIumz3c+Fjg4bYXUj0t+Q7Cjsyp5hV/YMk4Kdxx3d2FSFV+SGyjZtScHBd3yX8enR1bz7pgi02nDE8kilh2tXNMvH8/s5lfPSnZ5A6Q6iqAI92RhOUSY9keP9qR3c7V7O3e7lljZtjR4LNB4ttmr7eepx/kPahs8vIsdn3pYRtaK52vdGklstwjqXCXzn8NEvBLhbXMwGZZRnVXFH0TCIjweEhbzHs69iuZVlln652TVo/M2M2lo9JJdYlfA2gmrnSiPFxsQ4fZ/10gOYiuTqi4TkJjLlF0u9dVijRYHMCJTW1ppb2nB+RyWtFF0HVIrvuU/ZO5AFERUBOWhHWh5EimThZHFfehmItg+vCqfHJe7OdnBYbmHM7eKlyuGLihgD7FAH+Y66gV8MtPLyJSOAtbQC4Ne5xVwun2OzMkwfMdN1ZouXqEe4unCGs0KA9kSMRb5iXzarI9yuLqurAqwRjhZXeVw54inetf4s7zu4jDtSC7k5cLbuZ77V/bNFydChTrLQnuJwzEPOJtCejuEPSKbBAjN5xYWIgBYQ+Il3HX+T3IO7RjGoetBMKUUtVNMkX2j+dvGxxybhQv+wtVCNBDQSbZ5pgZJ6l89WI9lszPaisbrQvWqendn+UmKHQLpEEn1qjjckHua76mX8++1h3rntPF6bUi57KoWcLEJhEQnkqIzkLRaEkAul/Qgyn/Q+xA+yy7kz34uMiNjAFKzVm7X2ndXnRrBaHcep5nmvdD2fCdyFzdEofTfHTDW12rZ6wmZG3pot2Xlc6GASOyns9KrxmuuP4ubfbLtYI0XolSZN16lVeKGazlpPOI0Wi7mJDJLTZlkiuFYSba1ItNFL2Oyzsc9mhEc/nV/2uZ3IWBK3WjDuY5ovLvDRl4zwk1vhcTXM5wO7eGVyLxvy5/ErGZ4shLkhli8SZECKOHHo2tLa+c0TLm6NLEBGYKt6nmeqJ1mrjiHHc+VktYsF7pCDv449yW+Sy3mhPIZLKt5frAhyAZEuNTlnxFhDJxk61UwpJ6K0TJ3kpBIkUnAQbnAM5CYy6K8Q39Igz3ef4ZfyQm7mbFXpQz2Jeu6AjTXqOPsKbeyXtpJD4v+861gbHaGVAh4T9wRtdmEmz6Rmkc6cYCMsp3GaEONGo9NWmu35IP16PfbFgIujF39kqOahXG39esnAXDhtzLZNyVUf4aqly5zNhVOPhspMO2ZD5fXiE3w3voZ33r+Yq4Pj+MQCl7vThG3FfkohJ46lwYpy1XrstJ3nD7mecsJHI32ttszseBolySKwXh1FmkEC4WzQyItbo/Z/M8Et6kkAXirXJ39pI81fy0/yHTZwUvazVEpYklU99MTU7MXXuMwqAm32udqyeqatjVGxWrKMamRVfy3oizroS3DP1glHr6eV+3xIsTzeXhev6epnMnKCX+SW8G228rrEI3TKSZ5K+7mBPPlAcdpfiyDLkQwTeTs/eTBATHFyLO/njco+uuLj5UixzMUZdMlGc1zOIA8Ii9ibCLIzZF18BMAlFIhTuyDHXEAr0PH7yQ5eFDxb8V09L3f6MSxHMlzWmucrmRDRwnQ7ynqInn68p+MFrg+c5jYgI0xt+7FQMf/kmfJJlJDIi5Uj5TExn6TYLGKtAofsnTwnfaRiRnKms4hGNEqsGz22atsYX3Tm+9q7+K70P8ESFypy24z9ypn6oqXGadpGXwzqRS0rImNEthDP8zeBQzyeCPJUpoMB0cnPHJtYbovRk4+xajTJqUkHNy6cIswwFeV7uNDJNWo//tJDuRlv4s18my8g4SWDAiRLNaguxPSx1fnWuxfMNUmuFwJwbXCM7yThI+ltALzHfYiVZOoqvlMremu13Uxh1Qe9zMOoVa523dXTN71tmlahsp5tzeQzVpFpbR9SrKibtcdlCDmxT2R4oeMkGVbwSek6wmqKQ0IbRE7jimQpjGe4a6KVUzEPI3GRp4RWNmcH6ZPPc0v+CXp8hQrP8ouRGGsQgG35c+we66wgx2Y2bg8XukDq4uZCfQWL6oUKfEvcwOXqIGtraPVvjffywsBZxqMqPxLXELO5ebl8jMVSsuZ+yi+fgBZzOZr2scNdPL6Z3Be1c/v7bBuiWnxWCaqCKkxRzjulogf9c5TjDfkJm2mPm3Xv/kJgJyvzY/hz1lH4mdjHzaR/s9mm2ovMhZLKXrxXewO4GPQplyKsdMeNbN/INjOJfM83ETKLHOsJ80rG2eQpTkcOxp7ihK2VH/g2c3sGkOCJwRjvlXcT2tIOFB9IjhYX5wa9LNU9MGplPzcCd8BGjmIFqXx8KrGm3utCBQJkeaZyhjR2HhZ78OJnoUX2+3ygmtToQpNiPbLRXPkuukEaJzwZxdFaSXrrSS5rtJLlbFBNj9qIhrlaJFqOZCr9hMNOsJhVgelRQ6OkpJZkQ+7zkeiRCB2u3Ec+luM1fQMQsXH3ZBcA9x6wERvJsVtcQlxxsDV7juVqjj/LH8ARS5aTDtOK+bWjAkeFMC61QCeTFWXkLyRW+ya5LeNDUcHe6rKcxXqB4yT357ubtl/t/jUo+ng01EOHnGIt1uT4FvkYt4tLOZ33MubzciDVhiyLPFDoxhUYp9eRKltmQm39vAOZCdE9jUhZwSxZTlv/mYGz3CqUAjkCtKqTZBSJSak4/tYpI9hQUKDu3JFmcROzY+opJNiYOz8tkt0sTjQTYj0TmMlV9N9dCLnF04JR1jvd/CcCXR1mGeW1bLNqkVejZrlRsjtXkWMwn0K2SnIzGzstSoZtuXNsixTLdv7WvZI73St4IhVke8miSHu4H5BbGRcdPEuujNZY3cRq3dxS2PhxaD3jgpu/kg/ym8JifEqWaMjPVYnjTIpOtrrN/WWNOI+Xm5RTtJZKSV+vnLmgxLgWLpbIsRHv6j6KIEzF2xshuM2QGBhRlhvUqExmJgXR96URKzU9KghyxJyM14pmG38TI1GWwi7kvqKoNXQ4gxy0kw9I2A3H8goOM2krRk2/N9ZLm5imLzPOM9LHcZRyAIwez9r/KuDTabDP4ufz0nYAFqpx/kl+yPqHaRLquf91JCI47TJPDLvY2mq93qDiZYd8znqFGaLf1gLAGnWs6nrHhBYKgsSRrB+PmmIZUdoyCfbSzR/OdxMU83yo+wBui3tQxfkPOckNSETcfsjWLymzSv66VVzBamWc3wMqIuOCl2XCOCdwgapyUOzg7eJNrFAj/L386AVPznxp6knT5fMVLZ4L1Eo0T8cL0DL3/Xjas8U/EeLZQX9TbjS5z+y7mRKaZmmfzWDlBlHvdtrF/Oz0URYU4nzHdxkLlH20xqPFwgFBDyThRcpR0+3r6ZP+Bn6KID9WV3JaDANwOBPgEd8iAFxKjphnFa1ymrXxkbqO5dvSBm5UTtGqFolIc9Lx5gZz6VYxU7xO3s9/S5v567NXAvDVBY/gFaeiiWZlfWF6UYZmR4s1UmxGXI19Mu67EbJubL9CB1wiyFZRTLN25Gi27GZhJMNGNwzCTqRYnkyvi0SPhCsJGR/Yde06WlyMR2ROyUXnjA/ID+BEJp2ufAgbC6JkkHhcbuXnbVtolVP8Mw/hDjlYTpa/iz1GEgcb1MpyxXNxj6pnvGvr3Bw7wTfGlrN85Ci+0uHoz3P/uMCBQph/VQ40tY8ATzmKs2W1Eo7TFKVlTyb8tAgO2kjxvPRhnpc+zIlAJ18WtvG2wa18c+FuRKFoBwiVv4Mmp0mPFY/tGmVgxs96bTsFuEPsIyl28Er5Fwzi4y5xCX7N5k4QcCs5ZFHkmBAmj1h+sZrpPpsxa6hBLwfUvqu2n3pnLa3WqaVRttJ312rXrE1jW9nJuZ+t+RNzvIRhrCLV6E25WvS3lh/pxUZQmoXZvkxtzJ8nmXbwb+JW3s5jrJpIM+kvPpQXq9bZ4dVuIils7A4u4k6xj5CUY0R282fO05zOhWmXkxy3T4WJbAIkbB5eMnmwYvpQBWI4kRH4g7iIK5Rz9FB0WHivvJsUNj4nbuOFytGL/qbQTPLRDDeWK4MT/CE+wjGxAyiW49XIsV4TC1NEZaZuDY2iWtS4mrzCCmZRZqtqbHqCbLWeVb/0+7PqrxR2IQenaLDLRK6q9fe43MaIWvQrPjPppFeudCMZnRTY7V9KQZA47WpFQWTM4aODFNeqA9wr9eLyTY2VapraZhPkam0dJ0Q7KTpK610XGuNopp1bz7bz6sui5d9e++36Xa2kUnY8NC9KqN1nWuQUC9Q4vVTXDbtREFUFn1Phwcku3q/cX77nrWecdxce4hO2K/ni+EreFD5unqA6kSGVs/PFY30E1CzdJC1lavXe078hbiSgZrhCGWSFOsgQXu4S+tgvFOU4K5Uxjopt5fX3Cl1coQ7W1XazYZYbo//fav1GUcubuNr+E9iRA06ccXMnn0b6dCGi2hf7c7AmspMy/gvdiTlAvTfYuZxirhWla2SfmierlfXcXLlwzEeFPuPN4gbnIHvUXn7kWMuHWw7iV/OEhQzHvJ1cYR+p2R/9TT4hOPhy4HJGpOLU8eufl6T3kaO0SHl+1r+UUcnHlQzyAs/D9IgpVBW+klnHrd51rMyNEfG4EUQbK5RxHhc76VKT3Cn2sVtYQFIonts2NcWY4OFy5Rw9JBmZu5/KFM0cw8063/V6NGtYZk9hy49yWGznPee38J2FD6EpLCpcG6rIG/Swii43sn497hTVCLLRi1hray710LVgtG4DyAckMj7KUeOURyG0PIj82NRIvrw9yZaJe7htuJVv+rbx/NQhJiQ39zuXIAsi2ZDEBsboDCq82DGCPJJk9QIISnnuOyhyR6CPAiJ2lIuqsFOYDC5kstGpSNqz7f18JrmZl48NI8SzZTIJ0CbHcDTBC9eMrByxt3O9MlDT9aaNNMcJk1clFhKnlUzFzFhnPMLa0CiPpdt5/7Cbtzv20yoWx+GI4uIrmfX0iXHuP9LNLuUsr1P2ktX5DJtpVqGG/BIbe8VuFitRbKVocFwoXauqCoLAUbENh1ogJ9joVBME1ebJoBohijOR4dVqy+yFYrY5MXkkZARCdcyQXiySDj0ueXL8dIWVjKCWzKHaQ90qSlaNBOv3ZRVJruZJOxvP5Xr6Xm19Y4GJ+YB2sbeqafYo3bz27A6+tOYAr0yf4dtjS1kuxU3LtBqh3Zi+Im1lRPDx+cLtSEEX4RM+pBUecoqA80yBvCBxmS/KklaF3AQ4wy5emh/kx6M9TIbC9PoKjCYlvpdcz0o1wm+l5QAscaU4kC3+pmNCMaLWSnrOy+Ca4UJph6vZqNWjqddv/1L3KXBDWpX4u8lr+LeRdbwufILuaKZq5FYPPUltJKKrb6deUq23VjPbxti3ZhDiWpHqavvXtq2IxJckFVIsD73FZf5BGZdm06YVjNAdw7MYxxODP7j68Kk5Xq0ewKfm8AUklmyfsmJMZxNED6QYAe5uKcplzituOhKVNmnG2bv5RphKgpaN5mgnh2KDn4108fK1E1B0KOTseNFVpYskWSScVRIJZ0K2rhHOsVytbiMHcINymodsC3k03cpfKIeKVQsNeIVyiG+znmOFVt5d2MkrnUd4shBmv1xKdJZV3ik/whLNq9lgyWZEtWPJI/JO2w0AZAWpvNyvZlmrjHBcCJMrUSUHMjlsDAt+fi8uYqUyMSsLzEZ/41pSBTNUI57V+EIj+zIbL8axWQ1WbV9I0vy0IMd/TG4V9RDfWttakdxmRlmN+6jXyq0ajA+hizUxC4pJbXvEbnKqxKdPLOFvnpXimnvG+XB8Kzd7+rnFMUA2miODxK/E5ZwPeVlMnGXJYfoKxQfMRFxluMXLLtsQ9oIKsTTRfWlCW9o5dTxPUnSySznLnZPdLGs7W470LbCn+fueE1OdCcPN6ilOutp4ke08azrzyNEsT43ZyxZkAHb14si6bxT6cVBvAmet68XsGqnnhdItyHzeex/vnbySd5/fwrcWPlQuO66HmcVWMyGFpopbaO3XilrryetsItbV9lWvlMOMKJeJcdhZTPCLZMvJeK6BqWS8jA9ckey045aWB3EANx+PcTOnS1poG3Ip6po7OSV7crS4CJU+O9PwBtvBYqEXwz3H+P/FIDcTKBb6+PloNy9niqx+M7OGXjXGgBDkHbZn8m+FP1gSmHrJij7yd53aT7oOSnFcKGZTOSkwIPjZI2zhHYE9FeuEyfAWZS+H1DZ+LS5nUPGWifEmZZg3Kvur9tvMjcLsmHKIfEbaAcBfy09yhTrIOVYC0MMkq9UIh0pyKVSVpOBkmTLOCbGVJ4UORvDQjblsoBFov+t+RzcKApflzOUaVh7N1c6XVaS5lkOEfh9WeDrzrkv+yJxeCXeuOYcxH1PwjWAmEVLNB7bWtsYItFWVrnr7ZGW1VW90rh7SX6uP9UTVax1Hs7CUGJ8p3MkoHvYqnfzT/y3hXTuHiRxP8dOR5Txc6GK9O8Lv8z1kSpfhYdoIBBeyUR7m5thhfhzcTKeU5kb7ANozzBlykD4Voy2RY5U4zgPiQl575TjSqgXId52zTKJqE6HHW8wil6OlPkoJvu67h7hi5/2pHfxWXMYz5P45+T3m6tqymqWotl+z9WZiY2gFn1DgY97dvHNyJ7+IL+SFhmIHRlgltFXodi0IZb0yDX0bUsjZsA7YbL/VIr2zacv4Hej6rumMg/Zy1FgO2sn0usiUKrG5kkWi69jWUaFJpuSDrCUI6smw1XVzVvZyXA7wKudTMz6++YBxnP+t6yCfy2wiNZYFVeATQys5qoQqzMv/W9rEO+VHypHPakSqFtwBGzEc7BG6eYZ6puq6XvIEhSw3BYb5cWwRa5RKdwutDw4UNqsj3M1iniyEcat51qhjvESpfS7qIW1J7Lzb9gwAblZOmOqHd6nn2K92clJoYZU6zmIlzp6SBhlB4FTGQ7dr9uRYI6d+JUtAaUyu0Uhit9Xymeq05wpG+zbj2Cx4JatNm4aLOTF93nExEeOZolGLNKhNFqwIp0bEG4mWWCX/VeunmeOFldykWhKhWV/q7ftMIkIuZHpJ8HzlOAUkPvZgD/lEgbA9h2y3kXY42VUirBuVYVQEYqqT+8RF/EvLzfRLQd7mfnJaaWJtivudvSf49LbjXLM9UybG1X5XvT2WngzcnV9IAgc5wcbbbTdyWGhlspTvP9tpLf1vrB8vZsu1z2bb1xprVp7dM01StVquf/ms1rZfyCMj8rN4L1AfCTWLvjYrqqw/53K0GFVNn5qeIOpYGpzmcGHleFELUthV8c8K9RJjKMlAdKS4gvxStHNzDWTKxFiK5ZFOJWHPWDnabGwfKmUj2nm1h1z8TF3BLY7+sub1YoOMwNlSfWb9NbBeirBZGuXLJxdx50iYkYKLzxdu532F+wD4vPc+ZEniDmFJeZtq+lMjzJwQguRYq45ymgByFZOzcdxkVBu7PKO8ufUoh8U2UlXidEP4GFE9vE9+gGcppzgstFmu2wi+LG0B4C/lAzxfOW66josC1yn9SKrCEbGN26WlKDaJt7QeZakY47DH2i/a7N6pLbOSfqz3xGlXUnX1v5ZcotYzyyqhT5/sd6FkDTN1kGomLvnI8R8TrPSQ2ud6CUG99mpW08iNOGQ0y8rNqh96VCNYVn2o51iaMVX6wcJ9fEPayOOpEF5yDAke+uQJ/lDo5oXyERaqCZI4OCm2cJ13mN9PdrLOHccrFCr6kI3m8N24CD8g3dBFMJYn+Y2jli8G2nIray5Hi4sXqOdZnUnTkZogrUo8KF7Gx6QreV3sYZYEZidbqncWQf+50d97tuen0RfFel+q3uQ6wFcy67k/2cZVvrGafsNg7fxgum62YLqOXoagwcH0lyLTNqskD9YruzASYaONWyPSCo2YV0TTgUxJY2yPT0mBXEmQg/aypZtrIFOUXmhRcl0/jImLGhwtrjJRvmOkhWHZxRuc1SP/Rsxn4nAGG+cFHwuj5yvaycVyvC54mG9k1nC3soBXuo9hT6rcJi4DIBy28aLRk3xHXskz5DPoLcmsoor1WHE9kOvkCWcPf5ncx0qvOcmbxE4WiX8a2sgne/YDsE/oZJdq7r38cfn3CKiIQIgsC9XGPdiNfY/hYAQfL5UPWe4X4OPSFQwIQWwlffZlrgh/t6wfezxNKinze7mxYioa8Txpa6EnHi8XkGkkMa2ee3E9Y8nMdcL43cWYKDdf+KMmxzPRrF5o6UUtsjmX/as32tsMVHO2MPusR60EmXraqPVdo+ggxXvl3QD8SFzNw/TwYKF4Yx0RvNyonqZTmeSj4k52eMa5RT1BuEXCt7wTOZIpR/nS8UKRGPQuglv7SRtK/1Z7MTBWG9OTpY0tKrgFchMpdtiHCTrP8vXgDv5p4p6qD8tGiHO1BNJqLzD1tlnv+ao3gWomUWd9u5ul4qzA1yZWkEXieu8w9U4GVoumTrMzM5BPMxKu1yDDdBmBmQsEUNb3Gi3B6iG41byN65GNmCUaSqGS3rhEjvMBqUyQ7XGZfEAi5VFwJcVi5DhSjJA7WlzT2jPbZ3l2RRW5XV7My0NncGWma/HNJFqzuffWu51xH17ybFPPW+rf3+Ke8jPOAlcrAwzaitaSG20RvJLMAaWdy9Rhy33W60+bjhd4NseIiy4O2TtYyWnT9a5X+1kkx/iBuI63D24F4HvSenYWzpnGm2eT8GYFF3kWq1E6q+iFh/EwIAQBKCBhQ8EmKKhnJvh+to+U3UU/AdN7YK1lXwrs5A3yPjarjXsD1asHNttvGomfCqv4K/VQxTpWbdkDdlTUWRc7SWLngNDODnWwLFf4g9DLYjXKYhIXvJiKGf6oyLFxwGo3mUayWy9WYjzTaeS5wmyT5WZCema6vnE7K4eOZiUA/plyjJ2cY0TwkEfkMrUY9QmT4dXyE3x+dBMf8jyKU1SKCUd9PtxAdN8o7oAN10CG6BoX/pCzbNFkRfiMZXn1/+uh966VnDYWiUnGBQ9fD+3gZbF9tPunHlDGqEI9Gc1WJLqRcWulGZ/JeWl0xqMemPXvC/yOA94F/C62iL3xAK+Rz9DZNv1RMBP5hOS0IZ+fOpeqCilV4p5kJwvTKTZ1pbEJdThX6BPdNOgr2oWdSDqSbBaZNm23RvGPetwrpmmOdfvUR421z5EOpfx/z0C+vP/cRAZ3jf7qr6Hf5Xtpt2XZ4ppAcE+vGqjHTO8L2jXRCKk2jnurZ4KV1j4YzTKhOBGCTtwhJ32H44zhaajfZtDuAQKQE2zc7lnFzZHjptd8iCxb1RHWyuO80/ZM2qUMo7KLt9lu5HnyMW5UT8+6P7XwWely+oUgnfIkazGvJHqEYpGllxcOsIZxfuVaTWchRVRw8Nv8EsjDQi2Bw4BaCY3/HL2bBb68Zf9mY6Fmtr8pv3uB56tHLdc19vtr4hZ2qufYUuXlqR64KeBALhNjFbhbXIzAIt4vP/AncnwhUU3rU239Cy1MvxRhJfeolXhXb0RvrjHXfXAjs5CE6fTgenUMuyqzu9DJixku2lQBuWgWZ8iBu68YyUh5FEJhF9l9ozWn+TUSYowca9/pI2saCcmUYpxtjjwfaLmJF8pH6FOjRAUXMgL2VIbFwgQeg2eq8boxJlbMBrVmEqr1Yzb7aGRbY39swObJc2wOjvO/2RV8cHgDL8v2c92C+IwIsZEkZhWB/z3TycFsiOF88dwopUdNIJLjra1HWe2qjPRWeBYvDULYWdbnTivzrP0dnoosVyW8Oou4elCN/GraaDOUJRMltwrpVBK5z4cnJRYlFY/Hyn0wK1ai7cesTLaiwgP5bl7fdhLB4qk92xdkPRGZ6YueWXCklnRpQVDGkyrwfwOtDOXd7Cu08C71xLRtqqHWlPsrJh/nWemjuAM2MkhIKNhNor9uZOxqngnZwae795JSJD45soYh2cdfKQfqTohqNGJ/r7CQfiHITs8ov08toUXO8kz1dJmgKcD3xbUE1eJ4WUaUNjJ0Z2Pst3fhF6ZI7XOEkzOyUwsradLx6tKGetqsZ9/6daoVfzGbCXyZcphgA5ZsVpBQuUwdJoqTOE4WEedD8v2zbncu8bRnfrN9+2rUU/CPCWY3cqubs+QSLb+vtu3FiLl2vXAiM67oqqoF7eQmMvi2dkLYyeBWB56USHJP7bd5Y4U2LdKsn0b3mXje7mif5OsT96CqcG+hh1ulVeUmNkujnLH5mVBdbFKG2TI5wIZ8ZV+MtkCzqVylR7Vo2WzbrhdGIlKP3MgpKPy18wgH5RG+NbGa0cIoLwyctSRfAEN5F05BoYBAe1jAJqrI0WyRvE2EOelv40MHN7PGGeMVtqfocaTwkS+3+VihnY+MrmeXZ5S/ZSrhSP9yVCzv7CySy1KpZ1MYSbMF6iXFRjSa8CcfjxVf+EqkXe7zkQ9I+AcrJRDpU7HyC6U+AbHaneaBQjcum8JKZ2UVPe3lshnXvBnBdAdsM267Gqk2LnuFcIDfJ/tYIiX4kOcYnri5NrgRrbEeNhQ6lEnAxjfFDSRw8ArlMGHSOJErZBL/Kt/Pf4mbOZgJ8syFMT4qPc6nRtfwrexGXqkcqMt3vZHf7H5hIT+R1vC+jgN02jI8mGrnVmkVB5R2blFPsFiNUUBkv9DJJkb4cuF35W1vUE8j5FW+rxTvhf9Y2M1SrKueaqhHzztTnlIPQW5E+mZcr4101fULCGSx4cU6Cq7HecHL3cJi3qzsq2v9C4mnNdv7YxaTzxWq2WVdKuS2mTAmKer/nyna1DRt4nSSIff5SPRIhEeKkbEk1r99hZyiZFklhZzmUbQSIdKigoJTKkfUnGEX/yDvZ3k4j1OUUVQBl6hwOufla5HlrPOm+bmwnntYwVsmHrDUB1bL3IaZk9kLLSOySgo1gyDAetsE70w+zBcT2zlfcPHnwX7abMU2RgpO7pts5+clh4sKDMFre/o5GXPyaDpMWMrxnBVJbvE+UEGI9dhmGwXggVQ7f9t6nJwiIgkKE7KDM3kvO7pTFWTYKupbK1pcT6LhTKCPdmuf9QmBuYkMjhJp17Tc2kyLBn2VPzOtvTFRddwX4scjy3nXkhNIpaaM5bKbmdehf2mcSeLrTCQdq9UIq7PmUgJj3zTMdCYmJTnJOVx8JruDrCqhCgLPVE7xQuUoAkWHi4Tg4GTOC8QIt0r8P+EQnzm/gt/l+7gheqQpL7p5RH4rLuU2cRnb3eOs6cwhR/P8l/cevpddye8LC/ia6icj2HmO0MYn5G9Ma8OGyp8FB7k6OsB5fPTVQYzNUOs8mzk11MNlzM7RXAf0CogcENq4XB2qa/1VaoTVau2xdzHgaUuOqw0m45vbH2NEuBqhnYlrwx8jMdbD6LTQSMKX8WHrtKukVFuZcCR6JORojvhygfAIFL5xlKTJvs30xUadZb3T1vp2trTrFggquYkMS1rgY12PA7DVHeGdQ5fx7vAtvDf6exb5ptqspyxpI9ffhSbDZmhk7GejObpDDv5Z3cMvckv4p6FNbHRHyagSRzN+LrON8kbnQdxCgV4xiVOQOSS3MKx4uP98mF5Plve49tItpOiXV1ZM8ZrhXa59fDGzgX8fWcOBbKjiu65omk9274eT0xPk9JhPYqwn6xX65pIdnBzJlPfpoPjiJ4WcSJQIsqaLDk+/FrTrQyPMxuWyCt8cXcpNviGW5SPTNMZGCUYzxmK9AZxp9widnWAOkSx2/HVG7+ppfyZ9NMNNhZN8X1jPB1ccZcGVPqLnVd53Rw+9apztpVyLFytHuCO/oryNR5T589ZzfG24j+cGzCUfzpCD0Si4yZtKNvR4XGjnW+JGvGKBG71DvGzR1CzX7/M9/L6wAEFVeZf8MBOCC5/aY9lWNprDDWVi/H1xLduUIVaWiq2YFR8xQm+bVo0oG0lutURI49/NeLGpBy7kuokxcFFqi63wtGSFVoNorknwKYI8IC7kr5SDc7qf2aAep4Y/dqI7X7BK9ntCbuMJGf4mnCTT6yJ0OEMy5EBKidjvGTZVjVmV99Wmkt19wTJh0AiFBj1RACi0TJFbs3aNyzpsWT7Q8SQfGtnAx0PX8cnIr/GaPABqTdNeii+pM00W9QoFXu48zrPs/Txc6ERC5fXeJ3EL050RttqKrhe3OEoFWhpwp19ti/Ia11N8JbMegLVShGfb+zkqB/llvo9XDlzJx7r2sxjFlOTWiho3A1b70JZLy4NTy5h6qctNZMqSCTlSUsmHnRWOIEbLNv3Y1X9WVfj2xFJkVeC5AetiOs1GvZZZ1aREI3iRUCzJcT1Evtr3+j7WS5S1bTaoozwnf4wPH13Ba0MTbLmiwMse6+dbkXV45AKXBeMsV9J8NeVnaKx4L9EgVZFUJKIF3mu7keuVM7y0SlGQCC5+alvDi+wneXbHOLaWoswsdzLG/SMBvpdbRYgsL1EO080k3eok52qQbRU4SYilRLlf7GUcJyuViWm/ldlvYiWxMP7GjUpZjNDfcy/F++rFgAvyq01OTvKzn/2Mo0ePYrfbueyyy7jlllsQBIEf/ehHHD58mJtuuomdO3c23HYjg6jZgyZMmlATxOtzhUuJ9OqnQBvdxgzGdozrztfDsBq0SP7CRJyMo1jFLOMLIj42UtQbH86QKyXWWcFIADToK4wZXQKMPq+S01bxnVlE2riP5c4k31r4EG8f2MKI5KNPdx2YPQDMigjMJ2oRhvm6VlrEHM9yDMzpPrbZRvm6757y34logU/bNhMmTQQ3D6daWczotO3qJcaziR7r92HpjxzJlhMC5T4fUil6nI3mcOhIcpkg67Y1WtiZQVXhB8M9HFX9vNu5B6Izi+ZWQyGnVH05nN2zSKU1CE7BPPm5mbMt1SKYVsdwlXoWr5Ljfx9ZyeDIJC9Zn8d5up9vn93Ao/FR/kI5zC5xgK8ML+VDa0+ixLJkFGmaRCuCk3NCgA3qKHYUWtUUHtU6Wh7BxX9Jm9lhG+GWzvHycm1sfC27DoDXFPaxogGJhIzAZ6QdvE15jOfLR1ijjlmum8GGi/rNAGZaodCqnWZyHKtk62YgihM/2bqtLucDF4Qc//znPyeRSPCmN72JZDLJD3/4Q1paWujs7Cwv/+Y3v8n27dux2+21G2wQc3WSg+R4rtJY5u+fMAWrKGUt4lqNLNa7Ti0ybrV9M0l1FpGfxPo4KwX48tajSN1BXMnSlHefD/muSrN6KyJs7JP2cHRQJDKOpVMJSmYvCVpxCatjtnqxcLS46FXjnLSF6YoXo5xW1bWq/f0nzC1sKNxiP8NW2wh7Ch1MKub0ca4jxnqUibFmJxfJVpLlEkGWYnkIu5BL7i3a2NPGspXHc0WVPZ1rhz3k4oexRewttPAu9z58Qn1EpJk5FjNNTNXQpoAtNkmWmWmQof6qeNW+t8KXxMuQ7TbenN3DJ05fSZ8vw66tedb7DvOlIwv4n/x6/kp5kn9QbuS+QT+7vFlCUo4JXOwROlmuTpDFxuelbdxSer4KwIfl+zgstHKXsJgbSiWrk9j5mriZiOQhpdq4wjbMyzqLZaG15GRtXL/DuZ/PZjeXyovUjyQOnqccZak6wSqdDdwgXtpJlWUe7oCNQUJ0MomP/Ix5xkxs3Zo1G1crV6RZ3OmH4houV86x2eQl3bQvLbPeZU3M+1Mpl8tx6NAhXv/619PTU9T2XHHFFezfv59nPOMZuN1u/H4/oigiy3JD5LjeKi/z9XB+WOhmm3p+TkzMobEb4VxGwmYS5dVv16z1NMym7G6j+zKLqs4UX3Zsp1/x8bbek7Q6C2R6vdjjcnFa+VSS6RPu9fXH3xeYtp4UdpWjbmD+sDcj37XO9RXuMb4trGFJYYIeufFKVvOFZldDvBhnIqzgDjl4MScB+F1+EesDM7tWjMly2mftOyNJ1S8zSwQsrxe0F+UTx2PTSTNT0WO3zoZQ349axUU0jPtCfH9sMSM5J+927yMkNlYMaqZohLhY5YTor1dXPFnx3Uz7o6FWvxp9XnYyyd3yEvYLnbSpKb75VAeSMMKWdjtviZ3kg0Pr+AflRpYpEb4TWcLJhJOXdgxxmX2M2/N9rJPHCJDjbfJjdFLprLFQTdBJkgH89JLgAWEhJ8QwH2h/kj5HEqkkcjW69kghJ+snJtguD/NzdSXvlh+u+3gcyHgoVGid7xEW8WNpDVuVIV6rPFH+jZYRnfHvpoeVhtgqqXm2pLURMt4Mgvwq5UlTZxIrp6PsZD1Pw9lh3snx2NgYsizT1dVVXtbd3c0DDzzAypUrue+++/jXf/1XrrvuOlyu+ohKNaF6szxWG0UUJ9+WNjKgBHiJcmRGbVSzDLPyEq7WxlyjWoliyWlrmHg2CrOKWlXL3Bp0t/W2bbV+reOrRpgezHdyTAnxbvdeNoeAcJCMD+wlN6nknmFLomoF7XyUp521iFnJvcLM43Umfdevs8mWR2E13/Bt453x+3DX3Gr+UE/VxUZRK7p+MZFkVYVjSpBuMYVfyCOrAqdlP9fY63+JsSK91T6bFQ2xklKUv9eIcan4iJXfsm9rZzlJz6of09pW4efxhdye6OYG2wCvdw/gMtF614N6pRU2R/XoZK0iOWaoVRCkFubr+fgS5Qgv9A8wGlM5JoS5OjfAl59cwRuusHNZOMM/Kkf4wthKTuTDiKrCo7l29gy2s9KVZKAQJIaTTlLTiDEUK919XrqSQcHPKmWcI2Ira8QIy51TLwzlZ4FxzJ2KsUCc5FGhkwmctKCTmiHiMCFrCez8UFzD1erZ8rJTBPmxtAa7WmChGp+2TbNQj8mA1d/N2k+tbWa63x8Kq8kLEq9TnqhYbkxenE/MO2vMZosDUE983W432WwWSZJ4wxveQC6Xw+Go/4F1MU7LhsjywcJ9BJhZVMasDHC9Ru/zhXqSteYCjegb6123WrTZNFGpDvJthFl0Ma8K7C508n+5PnzkuGy7C2Wpj2hPybN1z1hNnXGtfeYmMpb9rGY9V4+kxbi+A/i4upt/mdzBkOQnzMURPW7ERaQeNGPWY76JswqcVbwsFovnZL/cyojqYZO/vge63kZND7OXTOMyq+vQzFtZ0mZJjKWrwy7koJ18QII+XzG63OdDCjuRH6uvDG9GEflKZAUTWRvvdz9Ku4ll4nzBzGUAGi/LDvVFjKvNrM7lM7R4zRU4K4Q5KLajKsUqobc/voBN7VnabfDhric5mAnw1fHlTChuXEqO3ek2AD5ku5ovFX5n6nRwt7CEQcHPp7r3cjQb4EiklRW2Kf1wtXEHsCaY5uej8M+26/inwoOESXNEDfER2410qwmWqxO0qWn85LCnMvzUt4Go4GKjMwqTRUnF/eICAPKCjSfETm6WT8/oN6p1Dq28sRuB2bZWkeiZYKZe9gvUBGcIVl1nvnNU5p1VKop5BqqqTk1RNEKML2Z0mLzpWqEer+ALnVBnVmFtPjEnXqomBTCqrTvNA7aB6LMRjhYXfxhp4Ue55bzUfoIdfTliV4UZaS+w8t4MOd0Dv1GpiJF4advrlzfi0atf32q93EQGWRX419SVoKp0FpKm612qaPa4n08pRkRxMqa62DV5unz+zivFssEpWcJvkxuubqfBSH5N7eCiU1X6ql1nmqSCPh/E8hWlq/XSinxAQtL+j+WrXh/aPSutiHxidC1BOc273E/iFGoXmKgH9UZtG/Wr1ZPWmVrHmTnF1JIfzhVR3hmIcF86yiHaeVXhSX6cWUOutwV3IoUczbLOFecf2o7w/pGNZAQHgirjRiZoEWByhhyoMbjCdp5OW5ZO2yhXe4ua1XIeBNPHY+5kjNxEBkWF/xxbCkBQzSAj8FVpC21SkDZ7lmjBzX2qf2rD0sdN0hjbJvvLi88yJVtLzZBSNXJuq5HaaufO6mXM6u9moN4xFRDytAtp9ArUai9tTu/cp+7NOzkWSm71iqIgilNTTfrPM8GlbAel4UKT33pwoYhxvWg0c16LXBn9VTUYo1rvP76StCzx+oX9rPROVnxX7371D/ERe4BtUoSbWiNIVy5g2KOw6JSN3GODFQ/8asTYzFHCOK2vrw6mR7WIaqNkLana+EByGwnBCSJ8qOUGnjt5mA2uKC3x2AW9Ni+FIjVzSZbvzC/koBzmQ6GpcbCvUDSw/sH5Bbxh4dTD3kiS63kpq3XdGaUV0/yMlwcrSlhX2LGVquFxslTuus/H8HIBz65iwmqm14ULQFvPAEeLi6wi8tmx1bQqk7zOdRixSYarVl7lVqjmgdvMymnatmbEqR5f3ZmgVuVQQYC3uZ/gUMzLz8WVXKYM8Q+7l/KR7WcIUxxDfc5JljsSHM/5UQWJ7XI/90p9RHDSakKSl6sT/KKwkr+IHK/w/Nbr0DXoEzEBhn1h5CT8h/w7RGAYD8eFMCuELNuzZ7GpSrEUezbPgKuVjM3BwkKUF3n7cbunznebOslAKeo5LPj4nriWy5VzfMZ2BV1qknfJuxEBAdVUqjFTNJIgqZ2buYq61orqGiPWxjG5Qx0CtZIQVxun8zEfOe9PK6ezOFjT6TRer7f8uV59sREjm/6MeG6y9oqXCLQyyxrkjDJt2cUAvd1XPYiGV3Fy9V/Mer+C0/yNUc2WNINd1dczbdNlOJZ28/VUoGNzC4oqcKegMOhLggqdzix2oXrSpeCyoWZKN4jFxf4mFYnoZAdXesY4KW0nH/CTSiokJgXYlC4eU1fVZk0hZwvl86M5T1R8nyneoK3GVbxjDSfXvqL8t+QSa24DsDvbwTrBjoKAjzyyIBJTL+deBHxqHicFetX4rIoVmEHfv3rXn/G+Ghz3zYTxXCa613Hmsr+qa9utqshlwBmhWPp2VHHRXWjjSk+aFe4kJ9xTtpnlcaoFzRbrri9DX2wBZ+V3Jtef2iVPWwZT153qL0DYjZBVUPNZhF4nZGTUTGHqGugq7tPmdyIUnGRHJDRzLlseJCmHurhg2lcV2JtuYcVilY3SOAPC1rp+s3pRz9hL9qzl3M5XN9RuIVfZrs0hUsgpNfXL+u0b3aZRaNeS8Tcwu2dof6uCC1Xo5GplgEVCkF/bruDyRVGE0jh55WoYzLt4PNOCyDauwMZhdTV96vQXfAmRK8ReTnuW0yLVuK/o7qVytsC44uKyvhaGlGL0eExwc4XQwYKuEJ1XXMWTQhuSoiKKAj2CjXXKKC5kRly7yseTQ8Qp9nJFqd3rlTMcFdYxRo4rhFYA7lWvZExwoyLQpU7iK2TokVJksHFUaGGZGiXA/BU30o8H/RhpNrKChAA41KnrUUEAnT+IcVzmcwoDzjCL1RhqTq7om37djMPb9P4aMe93+ra2NkRR5Ny5c6xcuRKAoaEhOjs7Z9Rex+O/xD/Rf8EKfzQbxuiWMeniYkO9keSTq/+CpU/97xz3phIzkWFYJQhp+OXRhTw6HmC1J8GYN8Efom18aNkRwvbKG/O0dkqvuvqo9PuPrOBEzsfrFjyCU1Q4fUsPax8XYE/RN7OixLNJKV0zaFHjeqKOVmPr5NpXsODBb1XdVr9+OprjfmEht0vrysv+y3sPyZjM18TNPCW2sUhM4BEK3Cq30KUmeaZyilY1zTKi2ObIzaXe/s8EF8MMykmnjaWnfjJteT3n/nCulwdzy3nJyoN0ObOWMyflZVV8sc1gdK0w0yJX7LPNhzSZR26bih5riXZ6OJYGkdt85HWxZdf5TDlhz2ybH8d6OZD084/u/U2TUhhRK2p8buera15TGmbq1V/tGWhWNa2eZ2M969XjGa7/vgORu8UtfEXw8xH5D3xW2sHiy3JcHxgrJwsvVAXuHlnLQM7NNfIAV6nHkBFMnZ++K+5it+jlX9oPsspVX0zxB9FF/CaxgMuUIRaUksB+KG3nuBDGduXlPLn7MR4WF+BELrvv+uVDXK9WepKrwHfFKzkvFqUVtxTu5QbSjOPiO7ZrQVXplZK81nmY9sUubhvr4AfjHVxROEtMcPKEWOQ97y08xCLiZJFw1uFLVG+RDwUYxWOazKi1M1coIDIp2AmqU9ejCoyIXjqUqYCmvv8qcKu4jYByhk2MWh5fomUR/d1b5qzvcIEix2vXruW2227D6/WSSqXYvXs3N910U9P2oQ/bX2oVYozTv80qUzpXqFVc41KDMaNZk11oy9/SdYJUu8Tt4+1cFx7nJV3nK7avRa71bYk2gbcHjuAQitXJYh4F6VQamemJTfVm4jcCvRVgtD9FaJGn7rGmX+8b4ib2isWwTLs6yajg5YOJrZyzTWnx3pt7EGfIQUK180C+i//JbQDgJuUkL1COzfo4GrlGmvmSeSE1+FbQ5wZYEeWb7AP8OLecfzi6jh9ee2jqC12FuWqyCqu2zTyGrWBMxpODJdvOyJSUSP/7liUZsTz5gIRrIIMctE/plCOV/SyoAo+mwtwR7+R9nj1zRoybjUYSj2o5GFjpUqs9Exu9nuqtvle+7qI5FjDJUaGNB4UFXJ88zs8Ob2TXTUkoFeNwCCrv6zjIz+K9HI36+aS4g1NCC6+QD3KVziniIaGHdeoofrHAw6nWCnJs9WwazLv4TWIB65RRXqtzRzhJiK9deYQ9oQ380hFmrTPBUMaJLEMBibOCn88I2zkuhlmmRFAEkb+SD7Cecc4TYKdyljbSRHDxfXEdV8pneUhayIDi52jSS+/ZEV7AOJMeKGREtmeHyuT4q9JmuqU0h5UwG6Uxrs6dYa06Zllu2exFJ4dIPwE8FOihmO8xiodPSFfwz/KDhOe5QJkNpYIYQ9GfulOpnOnXczQBeKVykFHBA2r9euq5wAVhjS984Qv56U9/yle/+lXsdjvbt29nx44dTWt/NtmcFwP0BFl/46kngapW0tRcw+yGpC8sMZ+YTeUuffRM/9khqjjEAn/eNb2efC1irIeqwpm0m3vEbj43tppvBfcRTE1NG9XjhlHNh7gROEOOut0czHSSr1ae4IbEEbq9eURUPs7lpITidbc2N8zLJx8Hf3FsOsjxrNAAG23jvC91ObeLS+menCDm8bNGHaN3Bmqyao4bZsfaDFhVCrxYoPXLylJOFGCXbYgHCt0cjbtZGUgXv9AS3yLmsxaNvAxU+BrrPIi1v/XQosWEnXVVt7PH5aLdW0mrbISiwjcn+rh3spPXOw/SKabr6vNMYRx/sw1qzDaiZ6bbrPdZqO93M5+f+nafrxwlqGb4obSOv/XsQcrmefKEjc26sSYIcK13mJ/Hp2Qwt4or2SCPYEchg4016jjrGWWvrcBRubXq/rVrYNBZrCDxGuWJMvk8XSKVnnY7vYECb1zUzwdPruI65QzP8Q7wSKGDo9kg6dJ97bQQQhZE0ti4VulnoZpgoVq8d0VwcVhsY4MywpcLv+MRoZsfiWu4M7GEEaEoB1ikxHgJB3mB4yS35RYRx4lHLfBJz4PsK7TxA3EtXnKsVcdZoCbYoI6aRpT15+eX4nLuEvsAeGkpyt1JinfLu9kjdHG12o/LoHmulSQ6Xw4ReoLcRpo2dep6NasomXU8DX2OAbxeL3/913/dlLaykzK2KifOjCgfEcLsFnp4sXIEX5P1j81CNT9WfVlQq0IgF5oka9A/lJ9uUWYjanm3AhWRuZd1DfKdoV4AYl5fuQ09gagmoaj2+5lF9syS0vTjJNqfqpkdbzaV5w3ZK8pFvyt+HwNSkM8Fr+Kvk3uxo5COT11/2WiOnhCsFiM8pYT5tn8ra5Ux7hCXECZDm5rCRYEJXHSQ4iblVNWoRz1jfK6ug4sxcmwGsz6+JvIUDxS6ed++Pv5r5xEC9tIDJzJValwP2WApaGyzmkuFFcozM1r0F6YlBBqT+MqJe6W/pVPJCicLgB/GFvNEKsSbXE+yzWZd3neuMNvZvvn2ddU/R+qZbW1kRlbftnZfcaBwo3qaiOLia+IWbiqc5KuHevn3yzMEmQoK+MUCYSnLMqI8JneQFux8lq3Iko1xwcM7Co+wggmWpkb5oW0FHx1Zy2Ilxhopwiopy2OFDrbZRrHrZg0GC0X3dUEn0fgfaT1XB8ZIj+a5f8zDPaeW84bwcX4wsYjrE2e4nlNcrz9+JL4nriMjSHSoMn41y63iShzIpLDzLOUEK9Wi1ZuXPCnBToqpl7h+Mcgd6lKWigkE4I3uQ2ySxlBiWa4iwXXBQXbHWzglhLjVtopvqxvYrgxxndLPIsytF5+tnGSzMoKHPAVd1b9hwcut0ipupZhvcLl8lsuSA2zwFtupNtb0XtjzSZDrWT7XuPTCqlVgpb/STqy2bEkgRgGBCVwV5LjayTG2ZdzffMBIePWVksxIstEe7mLM2J/rCJxVpGquYebfWkYpMndlcILfjbcjAKErPCiTpQQWE5eKeq3cGn0BqfVyVQ+M67oDNnrjMT4V+bXltGA2miMophEElU/Kd+OhQAaJ40ILg/gRUVjHGMeFFv5N2sWNyimeqZ42raJU69jmGhd7FNkKggDfXLibb0308e9P9vIv157HnTDRG+tIqtEOUH/MRn1xvZAjGRK7goRipXuxLnI97botkeBMrwu7FjHWyHIJWUVkd6qVVzufYoMtwnyhmRK4mRKR2TyTtL7X00Y1Szg9zAI6GgTgxcoR7rUtJiPY2SYP8cnHF/APGwcJa9tHs3y+Zy8A7znrpSCrdIppRihGYLX7QReTXKv084fsIhKqwG3CIrrUJOcFH/9dGo6vdj7Flbbz/CTWi0vN49ZFYguixITo5mi8QMxvR1YFNq1QuHtflr2FLm5WT1Uclw2FvWI3J4QwKUVCQUAWpnTwR2jlNpbx3sKDdKlJlqoTnBRaEFUFRRDZYRvm1+oynGmZN7oPsrU9RW5ChdIYKsSybOM8u4IR/oqTRBUHtye7+Q/pMjwUWKJGea5ygjbS5fMgUGBZYPq42aiO8tnCnRwSWvkvaQsPSws5EmjFltjLGk9tu835fEkz42AXavb/aUGOayUlGJcp8SzrAuPT1qtFjLW/Z3qyZAQmsc86M9VoH6T/28wv2UyaMd8kWV8hT3u4mkWV55IkzxdBtvSJ1UW34l43/3qym42+OK9YE8Ee9xBGQgq7yJ2MTSMZMyk2olXHM1sO0+UI9STsmGn5rTxUjcTY+KB8lXKAV3Gg/L0LmWWx8yzjfLnty9RhbgoM8V+ZtRyS23ir/FhT7ZCaiUuNJBf7qfLqllN8cWwlX3iwnXeuG8BmMDYwRnL1sxLGyLnVODVbrl2TUtiFK1m0bGNrWzESbNZGJIMESJFspfexvnpeJMMeoZOwlGO9NH/E+EJjJs8k/bNiJqTeeL2bzdLWateOyqcLd6ECLgr8fFLlk3u7+ddnSdhPRyvWTSh23qjsYykxVIrOB1qCnh2FlymHeZlymBgO7hD7WKZOsEqNsFfo4vvSOr6VXc3/5ZagIlIwJPa9oHCEn0VX0a5MwjpokXL84FQHC+0p7s32MqE6eZnyVPmeZkflBvsAd+cXEhSy+JUMA0KIzfIQ69wJxjMSt4nL2C928jzlOG+XH+XttptQhOLF9Uihk8WOSf5u03kW9frI9LZhoygXcgPsGSN9KlY+P+54kudzjOdynNMEeVJs51+lXXQzyd8EHicQj1ecA+N4cCKzRR3hfRN38qPgFo6IrTzq6CUvjLFOnf+ZlWqolmw4n0T9aUGOq2FE9OJSCwRKwvCk4GBE8rIkPoEacJvKKsxOgDHJT1vWCMZwk24COdZQLdpnFmXW/tZHnK3abTZ5lrOFaeTBLKGnGsGoJ0u+ah/mOYpcLXr8k6fCLG/J8Er7CZy2IDIlHWWpGEmj0Tfjb1ntt9XDOIbS8QKFnDLtwae/WWljI9qfmratft3JRAGv3zyaVOuhqX9wh6Ix3hvcy5cTa/ictJ2/kg/QQ237xgs1U9IISb4YZBk2QeVNrcf4zNhqvvREF2/fXJlkWuH7HXJO88yudV2a6ZX1MyNyNIsr7CxWgixFgKt5jct9RQmSpPtbOpWESJZo3saPhnt4qXgUoUlexrVwoBBGRGVZcnhO9zMXEbSZXB9mL9T1FJSw9K1lat0XKEf5RtbF++7o4u9udrM4OlEOFqj9RYkCFF+8zZwrAILkeIlypPz3VepZVhQi/FZcxi7lLGnBRqdaef/YrI4wpPg4lwriQuWt2Uf4wMg1LBViTAhuHhB6+XMdOQZ4cfoQt3AUpyZiUHTP21SOm5RTOvKu8peOI3wvt4q/cBxlT6GDY/kQX3mik1VPxbneN8zCdnVqrC8P4t7ahi+WJ3mHzoMclZ74GD2McbVwjAecS/iIeydXuU+zLj/M0sKE6XnQfvvfBddwRGxFVBV2uxaxVo4gl47KbFZ8JmS0mgRjJoVwLhSe9uS4IIg8ZWtnR66Y4WpTZVQE8kjIiQKSOp3oNtuMXYOVncpcopp22QrGqHOzCYb2kKxGks3QLBJxoaQWQDnaZRNUCopQfICHneXse0yq8NVCPcS4GvQPu9AiD5GSn6TV9Fa16VdjRrzZi2Q90Srjd3Isy98FD/HzXB8fye1ilTqO0w7LpDi3qGfIxczbrGUxNZeoh/heaGKswSUqvL3tCO86v4Un+sfYuFma5v6goZobRrUXA6tCNVJIF/012afxJVMvoTAWDLkz0g4Fma+ra9huH616zM3CsOLGIcisbUISXq0CCHOBmfTZeK01K3ErgYNblBMcnmzjA79YzqtXKuykWNzFJSrk5Zn5NXeS4tXKk8U/LJwjTwlBekKQEVRCZOlSEwyoPhBARmQEL12Gl3I9sQdDUMogcdwSPcvmwDhhMct19kGiqpMRxc3+bBvvS25APi/yN87D7LCNlF/sfFs78d24CJ/O2lCzH89NZHhe9DTr4sN8NnAVvY5JoqqCAxk3mfJ42id0ckBo40rlHI8J3QC0iRmCcoat6jBD+Pi1dy2v50CFBNXoatIMi8ELSXYbxdOCHFeTOvTICXrkqQx4FzLLCqXpNp05tZl+qlmh/RgO0tinXVgXAo2S3flIZKpGEMyIXjMIhZX36pxC9+C/qjfB//t9L69a48URyRb1k5FsMSrW50My8Tq2QqMvGXqdul6Wo6GQU6o6vlQjtlaEWq/5Bxg7l6Ftgashv1WA53CErYnjnAl04A66+XFsEcvEGKtD0ZptzCcuFtLbCLyizE3eIW4b7yB1JMHlbVmUicz0anY6FwmzsWd27PU4XjiWBisqVpomuGrSJE1SEXaWk/nU8SwPRYL4hRzLJPOkpbnADY5z5c9WGv5amA/SoL9uzeR31a7rZlQArGfG9XGhkz1iF++QH2VlPsInjlzBV1jACjHKmFpMpIvixEUBVw0/4OOEaCdFsM6Z2gnBzcmIm42qCKhcp/Tzc3Fl0VNbVTkhhGhXU5YRa8Dy9+yP2vhX2y6WpuO82HWKT6S38GrnYa62n2edbYKXOY6zu9DJf2bX8fNcHx/zPgxAcs8wjhZXWX4khV3lMe/eM4YjmmXZqTh+cvzWtoJwNolTLTDkCDIW9mBTZQolLfRD4sJy355XOMplanGmI43EPucCfqFkuDH61LQcLWjsZa3WdvPlgDFbPC3IMcxdVmWtk2v22QgPhaoX1Hyjlu54vnTJVg/UWiSv2drOZuuRpTqiv6OpYjLR6VA7LUwgxfLIfT7yAYmMD0KxYJEs1yDHjfyG1aQ2etgcYlWDeauSudWsBTWJhT4yZmy/2vb667orAIuD4zi8Ls7kvTyRaWW1LWrap4sBF4N0ol4scUzy07FF7DkU4pu7nsJjIMZlnbCJ9Ed/jNr3Vuvpr+FpY7xEgCVtn1rkWE+Mtf/DzrINXLIgMVRwAx7e6jrAhUSj4/BCWo7W09dqlqJWmAkJukYdYJdcnOXtJcHbC4/yGdvlLChEOSt6+aitWJ3OSYHLbSNst42U5SwKcLxUcU5CZVKw41PtdZPjv5X3ckJp4Smu4imhjWvUs1wtn2Wf0MlqdZy7xcU8RWtVja7VPdUecKCmRJ7l6Oc7mWLxs8OFEFfbixImm6Bylf08/YqPu/K9yKqAVKq6mpvIwEQGTsWKM67hjuLzYmsbjlie1m0dfObR/TwltfGbsQ6emgzTrSRYRI5+IUSblGGxI8W+dAtLxRgvzR2qsMxcUnK/uE1chjtY4Eb19LRzZxY8bIbd4MVMkJ825BgaT5arRmwbacuq4Ij2tx2loSz7uYTR6cK43Ph5vlCLENdb9a1eGB/IZgR5tqTZ0q0ikmV7D7xu8yhf29fOjh4Pr1HPkVhTTEyqaMPE79jqd6iWfAfTEzjB/MFljBxr2+phRpDNZiXS8QKhRR68pf/165i5qFSrEKm/mWrRlI2ZKN+dXMSfc8Kyr8Z2zI5hLnGpEGOA9a4YfjHPX68cwWPT3bNKyW5m41GLJBuJsBbt0qOanl5LRuV4rEyG6yoCXyLLnsQka50xTuT8TKo2Qk3K7bjUYZa4rcEseqwtr9We8bMVzIhWtWerPpC0nChfLNzO39ueiYzI360+y2omGM46eTwZ4IujG7jK3YYzm2UCF7vFBQTEHOvECAuEJCOEuCtrZ4M6yjJ1gjvEPp6hnDHN+2klQ6s6xBBZOtTijViAcoT1ucoJ1Cq/UR4BG2qFLtkZcpBWJLrFFF/33QPAf2aLlUTTqm3a7/9yx3HuyvfyP6zl9S0nTGVL0vGi5l9aHiy/IPqWBdh4cpQzPg/9GTcuEf4+9zBDeHlc7kDJiVyvHqWP2LREaQmVdxV280nbFdwqreIBdSEBsrxV3mNJYOshtdUCi2Z/X2xE+WlFjqGxH9hMC1Ov5svqjareKkQXClY3snqjinMNI5GoRvjmOoI8l3ILUYAb++JcuSDJe+9ZyAa3jw06Ypx7bGRaH+pJ1DMrCmJGkLXPRjhDjmn17qvB6gVLH/GtJ/nO2Ka2zN9XrLKXOBUvX1v69Vd15Bgc9ZFRJVyC9TRro9PdjcqPjL/zpeZcocEmqLwufIIfnVrMNZ2ViXfajIjZGDS7bowzKPqCIG4jkdbIcKQUcTZKOQzSClkj0Aa8ackAbz2ywUBRnp7YLfTwPXEdX5TvKC9rJJ9E+9vMZq0aZhJFhpk7agBcmxkkhY3/eGoh29sCXL4kzfOeXWDR3UOcnXSRtvtxTKq8z3MUFTg06eenI8uK40CEuOJEVWEMDxlsVZPiF6oJHhEX8GfK8WnfuXRylBwi5/HRRZJswMchuYV8SuaG4Eh5/TtzC/hBbiUvth/nFmex9PSbXAcZVtxcbR/C6ak8HyOqGz857p3s4CplgGVSxnSm5UjWjzRZQBJEFp4Yxbu9HUe4gxO3uQgV0rzWewhy0M0k3QYbOjP0EeNF8lPcJ/ayWh3jXnEx/y5dwT/KD3M+EKQzPl7XFTWbIGM9RDmLhG2eAo0XH3u7wKj3ZNaaEtDbkWjrXwqoRXYuFFmuV6PcCKyKFlh9V1ebWmGCWtXytDd+h8KLV0/w4xOdrDt5DnFpcb+ObR1FayumR7mNRKxaVL1aIRCzIjL1ep2aRX+roR6SbIQWHZaj2bJG2njuC0rxll1rZqaallL/vVUEu1Y/9f9HZTspRaLH3ryZjvnERleUb0aWciTmZlUwPa3AhgbTyHCJEJtJi/QyC02aMQ1b28p6e0pJSNLyYHH7kq+xdCpZrownGdwtJgrFdT6c3srXfPfO7Ae4RLBUjXK9eqZimdk1rcFsea3k2NnavTULf+kqlpjfWhjleCzI7U+E+K+9PgqKgIxApzfP9r40fqfEAk+WDeIE7SGF/z7ajYzIIbGN5WqEy9VztJIih4gvaOPxmA8HCr3ESFOUYLSrKS7TEeMzBPCRxUeB01EXDr+fgZyXb9vWAHCTfJJkws6z1JOEyHJXbgl5RG6wneUHuaKE4lr7YLm9TbbpNrIAeVXk0+lNhEmTwMGhQgv35bt59UTReUNV4fS4SLeQ4t8mr4QSB+8QUlyWTnJDeJS/Wh/hw3t7+V1+ES8NnkCN1X8PeqZ6hmfKZ/igtAunmsenZhnAz2dtl+NsKfCO2P1FqzsdqsnvtO9nAsuItWDjrC1I34xabQyXBmNrIrIBLw5kPMw8hF9vdNqKGM8HYc4jzljKYVVQZD5gFXGbD//j+fRC1uPaRQn2nvfwrSfaeMXGohYsusZFKFJZCERPkmu5ARiXab+r8SFXzwMvHS/UdDDRiKtVlNpoA2fWN/125eUm40FPyobOirQKaUSDpr+el7m5miUZKTjxinNf3nSuYBNUnuk/z22ngqzaPFXGVe7zVVax00VwjclzZpp7Y3GbCnKt6YxLle/kSKZIlKFcJnrqeyeZXheugUz5BVKDW1QQUXiD81Czfo4ZY6bJeVZtGe/HHdEUL1KOTlu31uygWZtWlp9zjWq/kdn1eJltjMtKFQ8nbA5sgoqLAgOKn92nF/Dh9CImZRsuUSbsVRAEWNua5tCYm1ul1QD0CnEG1ADtkylGbZ5y24uEOCDQIy7imP0alssRzgs+zgjByk6kwUuObiFJVrVxu7SUTcowfxAW82LlCMF0AsXj4sf5ZQC8z/1oXfeDp+QQ46qbCYrXws/zS4HiC8EGW4Rh1c37UpeX1+8jynu8j/OUHOJQtoOPnVzOp541yIefOcTHb2vhbmEB13Oi/Dv+Pt9DPiVzjTpQtR8daoqkYOeo2EZCcPDn8iF+JK3l46HruFE+yV6xiw/K95flL/UmVNeTn6WHGUE+ZQ/TU0gwLoQttmoeLnly7PRKuHP1C7vziRwOv4SMgELRf7ARmBU/MKKWu8VcR5GfFNq5U1jMLvUcO9Shhra90LIKDfVUepvN1LV+ite4bKaoGS22gCjA32wa4223L+LFI5M4OxRG2guwK4j/l8MVlcmsjnWuXh60iC1UulwY92ssOGJm9aRJJLT19W2YaVm19Sosv5giZkSy/D7SxlbbqKWnbT0P3dmOceNYXcKlk4BnhZ/EFrEzGClFgXXXRUkHLEcyRQ1ySfc4reSz0ZtYiyaXxrG7L1gmvFCUETmWBsuetlLYRSYgkd8UxB73IQP5wJQC2X7PMDLTpRshex4FkTbx4onaO0MOJJc4K8JZj0Vho+2bySmsZpLmgijPNs+lRZzq01IpzlLivMINaVUipdqIFFwE3DkOJ1o4KyyhVchwSgkyoBbvQ6N4KtqL4SSmOukBxlU34+KCqfbFGAsKMVJ2F4flFiQRhpSi3/YWaZS/KTyOhIoz5GAbcSDOJnWMlzpOVpSuroZVUpT3uveiotIi5GgXMxwqtPDlzDpudgzQKmT4M/tp7s13kcDBmz0HsQsKG2wRNhAhJqzlQ79t5QNrTvKKjhSfHV3FGZefl8jHGVXd7Cu00elKQRo+I21HUOEdyqPT+vFmZR+/E5Zwl7iEMTy0qGmuk08zjJcjSgvjkocHhAVco56t+1wZrXIbIch67GSUcVwEPXkGLbZpFi55ctwofGoeNyoyAgMEWEqs9kY61Hr70XsEWm0711iqRvAIPbSqk+wT2tmojtXtlnEhosV6WGmOzYjhbEjxXDhUQI2y0VXQ4pK5oi/FHbu9POMvimNypL1ASEu6AGZzNvRR3WZEhbT2rGQbtTS7esmE/jwYX3iMxLgsewF+e8zHw9EQH3YfoR40Yzw34qpyMUH/G9djD+gRCmzpLkWN9a4QUJkop7MntLReK5FnzQaOiUwFMdb3z7E0WLaqgmJhHCgSY3tcLhNkl6Z/1jloAHglmSuCExxMh1ks1S6NO9+oNQYbSYQzbtOMqK/R5rHZqJbY2yy4BRm3INNKcWx2immusw+iqkWL4ww2BmUPJ5QgXWKKpVKc+3Nd/CS/HCh6AP9jYTddAZmAkEcSVBKqnY9mtuF3yKTSdhaIKW60DXCFbZiwmAW3ffqx1kmKNTgEhRVSJR9Za5vgLa4D3JNfwBn8TKp2+qQEz3OcJixWnp+/ch7lrZNX85Uj3fzDc5O87rEBvn52IUfzAWKCExWRVzqO4Aw52JkfYUjxQhpT3Kye5mb5NFC0zjtNiMNSe7nyzv9K69hRGKppp2eF2XChVjIk5sH9a2aO2pcwtJMioTZMjM3aMVt+oTMwvcj8jfIkQ4Kfr4ubyFicZmMUcC7QKIE1ixjrC4bMFNPI1RzJJxoixoaCB89eOMG9xzz4BkViHoWYRyG6xlVV89kI9L9hPefdzN1Cg5HEVnuQWsknjFXStL8dLS4cS4Plf/rpd+2YDw5IfGtwEa93HiQkVn+IN2OcG8eglf92o2NV094a/zULZu3V0/4Lgme572wxMqaRVe2ftoywc1olu2njP2JIuqNY2IBItqgpDhpIhW6sa8RY+yydSiLe2l+uJKnBsTRYofVf257hp7llHJMN0+EXOWq9UJpFj6u5sMwExmhuNQ2+2QyM2bVmnH2y+q4aVBUS6nQC2ggEoThL5xEKLLfFudkxwCbbOH4hz1rbBFulooh3THFxp3MZPiHPpFp8ln8zvYoR2QWywhopwvucj3KLo79IjOcYa2xR3uw+yLPs/WySxnir+4Dpi59HKPCfCx5hVHHznl93sWfcx86uBFHBjVriAO9J7wTgWvsQf+E8XtdvHyLLTeppNkuj2JBZpYwSVDOMFwtdP23xtI8cXwyJcBeiD3YUrg+MslZJ8oTSy3V260mIZmrjzOBocSE5zX8DM+I0V76wFwUh1qMUVdMIQV8ox6LWPL+7282STcVzcWxxjhUU9cdlqyvMZSHVoCee1SLI2hQwVEaBraZgcxOZaW3oy0pr62jITWSK5AjKU+ga0pqP59Jgxe9TUR0t7OL2416+N7iQN7kOsNEWqXrczXr5q7fyWz0wswycC8xmvF/vHeZXQz0cSPpZH7SXI7Z6wgpUaoYtKuoZ1y+Xe2aq2l2Fj7FueTnpLpIluitICKCkS4apaHU5ghx2cWPPBPJkgY8PXcYXvffhES4uiygr6ImoldTBShJklmjbyP3cyv6wlszO6vqqRfIbvS6PyCHuzC/k79z1+Vc3OgO6WEryZvdBjkWPc1pcx+LscX4grCCqOrnMNsrjSjvdQpKdwiC7XEPYhQYjlyE3kxN5vBZjMa1KuKu47QCckP3cm+9miZQo+7obIcUmeZd7P0fkEMOym9FxNx02NyOF4vXyWufBadvU8/wPhUTeEN0LwDl8fMS2i4/YdvFi+SluMCSFDuLjv6VNvEHef1EUPpspLjxzbBLmkoBWS1xoVr8Umh/GFwXoltJ0SxZzJ1jfqJpNKuTs9JuCnlRcihrNGZNiCwgCvHxrlA//toMPH8pyZEuWYFYl5VEIaUTRxAKrnnLT+u30LyRWU7VWSZm1HtD6ZFNjIp+2X31/9Q4G+u/LDgWGyOSPTrXz03PtfMj9CAsl6xtvs8avVdRbX0LZSi9thUbJcD0Wfvp1G1nPql2XqLDLO8aBQgvrSU0jxUC5Mp3xcwVJNla0g3LBm4oy0Mb1tGqRpWVyJIMrqXOt0CXi6e3lNFnTRn8chsChm/bNqSL3F7q43jZoqVHX0Gxf9UZh9eJq9bfZ50ai0FZ9aCbqkVtZYbUtynKp/pneme5rRSiLwxZlcShDr1r0Tr8/381WaYTXuQ7jqFMqcV++m9vkxQSlPGExy0Nn25FQ+GzPXlqk4rjXxteBfAufzW7my94/WEoxHC0uhGGBYbz8JLeMf7Htsdy3S5ArHDEU9Rin7AEkFBaLUxHn+/JdjCku8qrInznP4K7yQpWN5pgIBHEisyCe5AOF+7hP7GWxOv2cuCjQq8ZxUEBGmFUBNOPzaT4dU5425Hi2qEZ6zbL7Z3vj0WO/0ME6dXRaxn0t1CLq9U7TXUiLnksVDRNjY2RNIwMGycRSKcX2xWl++CM/r/MqHFucI+lVGNzqoGcP0OeDu6bK1VppnK3cAsA8iqzBGOXXP2TMXmDMolTaC2Fo0VTCi147bNZfo3OBFjE0rvvzgVYutw1XEONGy2dXQ62Ex1qWgo0Q5XqKzujlJkYITmn2SaRV+pu122mxydOixkYphN5SrQK6sV0mumC+LgaCrV9eGuMJH7jCTqRTSeRSn61mmfI+N0Ehi00X4TunePledhUrxSgOQaGjStJeTLYhBnw44xdOtzyXkrdG2tY/Z2pZIjZzv0bYGojWmtnVNQotknuj4yw3Un/yGcC3skVnjPMFN6scJUtORP75/CbWOmNsckdZ5J3kl/EFPJxtY5kYrUqMAe7I93K1bZCHCl2cV9x0idZBLz1EAdNy6vsK7Twut9EjJHku/YD1S1k2muO23CLuK/TwTh5mGVFeopjneoTJ8BrlSf5L3MQ29TxbSkVUZoJqDitzjacdOa72xl3Ly7ERWPnFzgSb1ZHaK80hLpQjxaWCavZUdaOOKWcoRo9ftnycf7izl/49k7C4OC6T3qkbp1F20Gif9YRI08jqEx/lbKFiTBujv0ZXCu3/bDRXMVNivC5yExkcVI92ayRKm0rXMJhycGzIRouY4zXOw5b9mQ3qmb0wW8eqKMZMIsT1LJsLGEny+byLffEgb183Wl5HnxRnj8vFCHCJKFcQZEOZZz0x1kPbpvwipJNSoPM0lkttupJTtm4aidZGV4WTCdDizBNTi4UftCjxIjHJFbYhvplZzWk1yPvdj7JITE6LIo8pTv5laCthKcu/d+1Hjl46iZdziUvxOaEPAM30eT2TpMF/dO3j2/nVjMpujuSK9+sl9iSvajnFkayfu5OdnMz5UEqlNU4oIY7IQVZZRMdHCk4iqotn2s9yQA5zT34BL3dOL1LSCN7mfrLmOvr7+8ujT7JVOEtPqfR0Ld70N8oTOIIObMLsCG0MRzH6rDsPGf/s9Of14GlHjgHsQQfjMWgPmb/tWtnVWKEecl3t73r2MRPMpM1a28zndOJcaIvnkkw0TUZhkWAHRWLQRp7XXDnBtx5u4Z+3FDjZVzxn0VJ56fymIBkfpDwKncftoBVNMOljvbKLCh9h55QfsX48JE5NRR9qPSjMCHWFbELXr7K0IpKBPh/5gFTWpALEchLveLSYSf5u994KvZ++3dmM20aT6LR+G6FP+LRyiLgQXtqNICbbef/IRp6/OsrKZTJ5pkix3k5tWjKdEXrvYh302+nJtR75gITr8ViFxMYelytmF6SwE/kx88BCXimK1Cax4St52kuCysudxzmveHCoMh9K7+AtrifLvrkABVXghBwgr4oEHDK/TfTw3JbBS8qZ5E+oTdxmEtBqhCSfdLQxmnHzF8EzbO9M4hAUwvY8ggCriPA8IuQnsiQm8vxz6nJiqpOF4nSZmHZfistT18fftR/j06NreJnjOKKFPGi2rjpWUssNTFK0rJBqyh0k1GnR/pkEImVETgjBchnv+cLThhzrE4eS2LlXXMRzoycq1klg5xPSFeyIDXKjehqXbrtqJ20uTNzna1urpAqr9mb7ELAiGfqp+rnUF1tZtM3Gum2m9mwzgRZJu1aN8lvRz97bHFx9DTy5LkfKU1Smj7RP6bfPXamwoqcT/6BcQSgr2qxCkM0inIJTqqkHN6vSV01+pLdu0/dLgxzN4lgaREaX9FWSV/zwtmLk8V1th1iVmR5ZmemYNZJ2s3aN3zWSADlfpcibCSnk5NtnlnLNkiQvXBVFpvhQNlqpadBLLsoV7IzJdLX2qYtAl63aBjKV2nQ9edbpksuzKAYEHeaJTz6hwPLSFPM7XPunWWd9O7uKPXIHuzpiPLN7gq8d7uK5gcFpJXzh0rLy+2OHUZNd7/O0VmLfhOKggEi7TqIzpji5NbmQlwXP8JzAIKSnz2zIkQz2Fid7kh3EVTtbXWM4DbZo+nvPOXuADa4oS9tkVDWJIELUH6LLPjXjZ+U53yhmIpHRf9bf+xOqnZjiYGGocrtGkkbDZAirU8eTjebIC7XvK7PF04Ycn4naCSHjQsZHnucoJ6at40QmhZ07xD5+KyznPYUHsclOloaKN0urzN9mYaZtzpZUG0lxs7yMGyW5cxUlnom+84KhStRYDzVkR7DBuYQd10CcYJ8NT6oYDesYnbpsfR6Fr21uZ/n6Ea486adnj26s1JByVHO8MFumjRTt5qu/TozJfRqy0ZzledcnUpWxZ2zKExeQIlni+RB+Mc9Gd4ycyT2/0ShJvYmgVhIKK2u0hmUUdUb15xMZWeTJpJ/P7hwoJoBSSV6NvsPVUC8xNsI1kJlyKdF5HgO6qnwGzb5hrDtEFZugEFcd+CwcAtbbJir+VlV4sNDNttYYr3lJEmdSZOIJO1HZTkjKT/P2/hNJnj2sfO2bidkknFsRuLPeVj4wvLH893Psp3mh4xTOsItbx1ewzR3hWf5iAa5peQS6a35cduIWFPZk2tgfXsC2XFHbbKwIOjjkYoGt6AIkCNBmz/FoOsxyOckaV/yiTGjPRnMc8HbwaKFzmoTDKNm7GPOenjbk+IQQoleNs6ikhzGbbXCg8Gn5bqI4GRJ8PC52MjlpZ2mgUqxer0xipgbm8zkgqvVtpsT4YrkQyyVoGyxwcMFQJzHWcMPGSX71kI9nL4ux6JSN/r4CMY9CMCWW/wd44bkhYh6FQwsFkl4bi07ZigRDRxxqRb6NUgezCmdGGMeP8SVM/70xaqyPxusJp96vVosOtrvyJErJMbVkP9UISzPGbbWXrGnHWGO2YibEuJb+3XgOa816GNvpz7gJ2/MEu8RimEFHgs1IsvGzXkM8bV+6yLKVJEMjxsC060WTdbiM3+ucMPQoqCK/Zhmvp7a20tHi4qvjRenO63ZMIIfsSCkI2/NECg5CJYcBoz5f72s9E2J3IQi28WWyVlR8PgrfmFklznZfzUreMt7nbhVW8KvhhQC8IHCWq70j/PvIWo7kW7k6OcLuVBvP8J7HJqimxFi7Hs8NqdyZ7OJm/xB3JLtYYE+BRXcDtgLnMlOs5jrXeX4QW0xeFfnL0Glu8p1HEKpbTlpVnW32OXWGHCgq/G+ijx4lw2pxgoIqVE2onG8ninrwtCHHu9Rz05ZZ/eCdIZVOEmwuEelqqOXj2AzD9XptdebiQq8Htd7uLyRZnq/p6wshrXjGggRj6yTed/9CPjRxig6fG59HKSfn6QnyglEbQa9Cf4tKxyjYjQlStaLIFlFMKxJmvLHqZRXR/lSFU0VuIoO7ygtMuVKejhCXHSuAsDNPopTJbfXQrta/Zo5P0+Q744tEE6svWo05KexCcNlM17P6XK197TzvSwTZsCCLFMuT6XWVE+/MoEkg9ATaivSa6Yyrra+PGGtyofymkpWb5pNcWidf6mfFccXyhG05lhUiODqrv1DlVJEvjK7m8UwLS92T+JJp5KSdsaREZ4vMw5EgS7sq9aBWUWQzaA4wZi92RoKtLasH1fZpvE6MXuPG7WtVH7Xq02wjv2bHb2znYorQ/ybXy69yRWL8sa79LLQXHSPWBxPcHWnneM4PwJjgtrwHaNdaQvaQQ8TpEfnCwgPYExlwmI9Vn1QgZfeX/36mf5hn+ofpz3n4wPAGlmXHWN5WmNG9bi6i9woCd4lLIA832Pp54+R1fN13z7T1LjZCrMfThhxDfdMnzT4ZzcjgrUV89ftIYuPb4kbepOyd8kUOusgh4YhNNq1PetR6AMyGgJiVEH5aosGosUZIXrFgFI8i8979S7khleSFm2KE22xEOhTOtRcjyRqKcotCsez04ULZK1aK5Sv3X6dzhhWsohJmjhXG6UGorIyn316KFJOsKhK5Ilk62rzsOeedVWRuJrBynJh2HIYXiNmM5YIKRyd9rPWZ68cr9tXeXK9tOejm/qOtvHHFaHGM9Lqm6YeN0WMreYWe9FaTVxijyEXJhKs42xGZ+n2lsKuoQ9YS+EoOGEYNtNbmQ2e95BWBqzujlvs+Kfu5L9/DvYUenILM89rP85dri0VlxLjMR37VxXDGQdhbGUSxsvKrdn80W6ca4az3JdAKZvuZTQGbel8262nP6gXB2I5xudkLea1IabOfhT/NFWcX3td3lMU+BShe6zfmz9ESSPPc3nFGck46Hdb3WO2+sZQs/9b5BB8bWcOPznezMzTBG/1HTY/l6KSPpfL0gkd9HTJL4imSHWGkQKzcttVvqn1nhJVFZb0yNeN6NkGli0nO4+WRQkfxuFUBySRBDy5OknzJk2PR68CpVneHsBKOzxTNfpNVVRCDLuyl6Fg1V4w3J6/nnYXdQDHBcBgvPWqB43KQ7aG5FanXGz1oBEai9LQnyTPAC3oj7Fic4vuPtfDOn3bzjM4JnrUgwobtPvr7CvgmRZJehZRHoWPUxkh7gcGtDsIjOmJqLLhg4rms0QzBZaPWpIo2FozSIn0CHlQmopSn+0vLtIp4FePKhLhn47LplNxcVVLUMBOJTj064mrrjOacnLf52BA218nO1eyFFHYRydgYz9rZ2JEGwVlOsjOS4rrbrENzXLZz00ixmQe2tkwjzGEnmd7iMqPkQ4rl2X3Oy+ce7eK97QfxitPlOClV4kOZHUzIDjYH4lwljnNj6ygrPCnAVb4eFjlSDGcc3FLSjs4UZpE5oxzDihxq35u1M9P9zwbNaMvs+VmNzFXrQ6P2izN5Zvd72ngyE+Jn8V4A2pw51viSFdfx4naFxRRdT3pd9e9jgT3Nh10P863MSvbHWvlUYS1/v/gkw+MCdyS7eFnwDD6KcqfLxEqvZc0bfllLjhMpD5uVKeeWmbzA1BqH1doy+40/6H2Mv528lgTF77KqiMeiCmC9M+MpbDws9HCHdCU31lx7drjkyfF5xY2iOGnT1TifqRa4Ucw0Eci47hNymN/nF/D2kmi92kD5mOehknm9gztiC3hKCPNu8Um2i6Om6881Lhb98VyhKR7HTUCPkuEd149xNOPi9oe9vPXRVl4Um+CWbZOMr6QcQfakRDpGbRxbnKN8eXcIxQIi6BwE6pBamEVEjQVEjJZt9balrS+FnEXv4xJh1kpK6y27DjzlZY0yZrq9EXNNmI0SnpkQaDNdt7as25mlm/PA/F9XAipuu1L0/S0l4xkr25X7a5Kgp//bCE2moa1jXF+ffKeXdJT3qx+rkSx2A1FXVTgTsfPAoQB3nAzwitBp1rmmFz4AOOtpI5cW+fq6x3GIUy9dWmlrrb1jGS82FDrtc3vt10MCNcyWKF9MMD4/m33tNqOt0552Pjyyvvz3Fn+MW9qmW4qZvfDWm3DrFWXe4jlMXhX5hryONx3agINiMukLFoxydtLBWM7Ouu7KtrR7z7rQJP99pIud7efpbnCsNprAXA+0Z4NdUHinaz//m1vOOcXHEaWFLeL0+3g1OEMOVBUOyS0sSw7zNXELx8RwsYz8HOOSJ8cOQeEXuT7+xvVUedl8OjBYbddI0tAGKUJIKJLhA/kW7it0c41viHW2iWkkWV/V6XnBczyPSq11VhXpV/zTLIqahbm6KVvqs5rgD1tPcpmeBBtJixR2zZveuBqkWJ415Fl5bYbBmI2v7W7lnv8L8ulrzxBe4uLJdTk6SusuGLWVtckdo7ZSJFmsJC9amd5SKd6y5rdWP0rnwW04H/rfUC+V0ZeIrtYeTBU4KRNI4FzcxhYTD1ArzOQhq99mLso1m0E/1upJnmsqTM5zZNxJWM2Qe2wE6YYFpv7EVtHgWtIK4zqZUl0Q10B+WmW8TK+LjA/scZ1swmATp8fp0yLf2dfFYNLBRucEH+p4ggX26dXDtPP7/fOL+YvuQRyiWvm7636TwZiNTF7gBYEB0+OdDWZzD306kGKYLk2ay+NqZMbTuO6XJ1by+hWD/NexHgDetugkTsMLlf6zVUKzHsYE8nL0FXgrxzg3Dl9Pr6YgCBye9PG78Q6eFziHS1SmbQ9w+aoctw9muXeyg5eF+i33a4Za0pSZQmtrM2lWRx7lO9lVdAvW9/BqUeMCAn/I9/BTx1IyisQu6Rx5aeYlqevFJU+OC4gsEaLckVvI9fZzNUtMztfNpR6NljYwXWEXKyiQmwCPkOeUEuRc1se/2R5pmOh/KrWJsJBhhWc6OR5XnPiEvGWZSqv+6/+eDWZCJOZCZlGPc4O23sVAio2QYnl6gvChm4f5yO86eP/DvbwjM8SCjqKkwjcpEh4RCSPS31cg5VEIj4hEOhQ8Pgl6JFzJkjOAEZEs9ErFIgulRfXIBKYtK0V/5UimqvWZ5m+s7cdYGOToWZEzGQ9+V/Upeo3QzmScGolxNTTqQFFr5kG/fF7GWpWXH1GErGRDCJZKNPf5kKAiimuEXvNby9pNv41/sLiuFk02+ii7kpAojVPtfzOZxxNH7Hzh4XZeHBjguq4RHGLte1tKsdHnniLPWsS4Qr8cBaeg8PzgYF3H9CdYo5bDix5z5TY0Ey20o8VF+pxI0CHzue3HSU3kK4jxtJeq0uzGbK/jBa3wzsgT3F/o4s6RblY7o9zgO19s23gvXR5EDtrZtijN3hPmlShrYa45kRLyct+5HvxCnhdLJyv2q/3m1bTHdkHlyUKYhdIkw3hxKjK3SGOMz2mvnwbkuF1IExFUOqWYJTFOqjYOFsJc3WE+zaYvMVov6rUPqzXwjN8vtSX5d+mhclnJRrHeFkG22Paw3MKI4iaDjVc4j9Xs02ynufQROMEpVeiKL9YkvIrKbfMZyWsQWgTtXy4/x3cHOvjYYz18qmeI5Kbi95GOoquFb1LkXHsBsBEeEcvfgVicuC+R4ArylpErprGNeuFp04UmDwc90S23Y7hmKiK0YSfofI+1dX871sGLAgOsDOSB6Vn32viczUPVSIxrtWXmtmGGWq4R8ybVqTMZdGkoi8+l8ht1Mc+NnCG/KWhZKtosEa4aUTaWntavm/EVybDVd8Y2NDx0wMl/7m3jTeFjbHVXehZXQ4ctw1lbgKVhtSyjSPRMkXJ7XObweR/LHOZJkc3ETD2yZ7vP+dxfI/f6evvWyDE0mnSoOYv8KrcIQRToyCXobgM8YCp3MtwrK/pZQ1phxSO8YQc3E+FmpifhGfclxfLEov+fvfcOs+Qq7/w/FW6OncP05NHMSBrlHEACRE4GIxYExgGDhcF414vj2usf6/Uah/WuwdgGLzYWYDBJIEACJZCEJCRQHKUZTZ7pmZ6O9/bNoap+f9yu23XrVtWtuqG7R/T3eeaZvlWnTqVT53zPe77v+4Z4OpeguClEMNu8arKaCIsKX9j4MI/kB3hY3chVldpqjJVF384Y+O7gAX5aHeaa0DSXaicpChM9vWZgOeDBmYoFAmyXF5vooO7w4O8LUo1HGE5YE+e04uNfc2dRUkX7QW4plq6b/Z0QPv3jFASavDrd4q2Bo7w9cMSy3mt9U7zZf4R3+g+01Ll1Q/9l/Ni1ktKwfSWJsVcSYrQYr7bW2A46UdGSPt60Pc3cooSYqjRoj6M5kXBerEssitEa2QjnRYJZap26lbUj2Ex8zGX0Z9RwnIGEGfdL/UGkHYnG34bBTZkvNumf9XJZRWI0sdx22g171QmM37X5W29lRTb/a6jX9IxWu62JAnzklXPcdiDJoXyY4PFiUyg3qzjHOnQiW4w2R5Ewkt5KXKq3RZ0Y68fqZWoTuBqC2eU69Gv48rP9fOpnI7w6MtVEjFu9nwuCKR6ba7SyBQ08uBKX+NELYa6OeNNHdoJu94dObdMpAksnY5jVClGnY6Ldd9drzIdj3DY7yv+86DCb9wQ8Rxsywq2BxfN96X4j8yXeuWUGv6Dy1dQmXvT19+QZfW9xjEPlSNvH/zA7zP9b2NEWr3i57xS7IjkeLAwzJhbYJaXavg63OOMtx+MJlargI4iAX7Z+6MNyiWHZ2tqT1ODl+TnCEV/DdrvoCW4andWs1u1MtxcxHY11+QTNlVdwt2QURsuxcXu34KbjaZd0rFWrMSxbjqV0hX4N/JLGyUmtHvc4vxQP+bkJhUSpcbueaa8e5g0ayWlRsV0qbKlLtnL0s8hm1mRVNcg4dMyXZCZLIUKigimzaksvaq9wmvjalWlnAmUnuzD/vVoYT1R5y3mLfPXYJv7b/JHldzJfqut+YZngGomtDt36aixrRjDb/LsYZSlFemOZ+WG1vuqxf9rP/757iJBa4VPjPyMpVTz3JxeF5vn2TM3ypMdLrsSlenr27AsCp7M+Lhm1t9o5IaPIhETFMgmEGcZxppsWXa/1OK2GmMfCVuS6U1itMNmV69bz0hIBpssBZhfgpBLntsO7eOvGOcYnDGY3m2yMDX2euW90kFq0lKu5ub+lc4sC/PVlB/nuiQH+/NBO3jR0mrcMTRGSVFe+Hm6eY0BUkWlf6/uu5FH+5HSSk5Ug433uY1nr+y6vzLHZn2M8COlIb4MtwEuAHAMMSGVLWYS5cVl9ZKIAuyLNQvFOP3Kr443X46bhd8NCZlVHLz2enSYTHT9TC6c5xyUrI6FbTXRgdbCC2TFKTfoYiCqkyjI+IBtRSYdV0oHaR5FYsiKztE8nx47XaM46ZnEPVhnPLCNhGJJ6APX4tfV3uKT3DM6XkHYk6nV/6/4Eu31pzqnOWqa8tCLI7SxTt9suzZnovGLV26UFpHSF148vcOfzm3hiLspF/ZV6vGxjhAkjITYSZP3/Bmc6lveZrcBGS3E4X9PKG+uKnVRgePnln0r7yJQl/ubsZ4jJInqcWcd7MvW109UgQyYNe91BMAv37QtwQcRepueEnCrxmycv49XRU/zq1uUQcEJActTXOoWy7FZbdisHalXnSq769epcmgZiMkBWkbhnfohHjw8wnZMZClS4QpD5L+ecYPdWg37dSH7tyLDHWPJW0WqsnrVt29D76KXrGQ1V+PWzpnhVZIovnZrg/c9dyJ5Imj/adrB2Dod25NTu9H03cLqhvLG+h3MD/Ed6Mx/oP1CPEvPj3CBXhufq39EWf57/Ovgcj+QGeFty0rL/duIkE75CPenKSuAlQY6fKSXYFVjEv/QS9Jc5VQqQiImEpNZOGt2AqtXIthOsyKJVo/USwNsJOVXi6+lN/HLfYct6u412O2BXdRsIyIouQdtZDFYAdgTUTJDzZYGwpBLI+ZgcqtZSTAc00n4ZUBg5oHF6R+070MmMb9EQZ9YcicBk7TWW06FrQC3j31o8s6aEDyYnKP23b1HhZ8dC/PhYlD8dfLph4mvulK2SBXSCdkOzuSXIa5EQG6FHjnjjnkW+NzXM+YlppPlSgxOc0WkOrC3HZv2wToyN+mXd+my0FvsWFXyLjW3FGGVlVCwSkypoCK7lWcZ3qmrw5cxW3rKl2Z0nnK+d58ETUW70HWrab4cfZYdRgVdEplE1gY2+HGcHrP1bjPBKTjuNBb8WfTw6hRfrcVkT+EZ6ExPxMt9dGOPkiQA+UeWyLQXeuinFVRuyiALsjxTZGTdxBrNxwOtvI5m2GEec+g9becx8sbayYzrX1nH4w+AB/sv+c5ksBes+VV7fv5MjtRljviJzSoBPzJzLq6OnuClxhM/Mn8Vn5s/iPyWO8qZ4zbH1nOAifVLjxHStRmB5SZDjcwJppKUB1PhCf2f/uQQEhX859ynPDndeMVP283wuysv7vC/FeVne8LqcHBIUbkwcqx/bK/S647UjFW5C53SMFh2bLTqwGBvJahOpNDhG6RhKKkwVfVw0LZKN1CzFm1DZO1KLgVyJ+4HmJWvL+qoSWETd0fcbCa1jfFuT9dkqu5mRWPkWaxE0VA0+/8AoN284wkRcQ0k1VmvupI0EeSUsxlZwS5DXUnQKJ7x8R47vPRPnkSMhru5vTgDiRIz17cWoISrKUlSKzLhEjMY2ZFzJ0CNT6NBJOdTaz67tCtv3V/jpVIRXTVgnSnHCF1NbkGW4/uwiCMvfs37O9JTKVMbHOeOtQ2HqxOxHxVEOFiKcO1RkPFDiLwf2LbXD7uuI9Qgva4XoOoXAbKeeto51SZDzisxpwjw0O8z7rlpgz1gJVYN4ULUMEegUutAKjuWdVuFMjsyeYUO2P37RUf74sc38bDHBZYnuhXY1G/iUVIkt/hy/O/gcfz17Dndlx5iuBvmz4Sf585k9PF1Mcqoa4gP9B/ELGpv9+frxVpwkr0r4BZVAn9925V8KrMsqWkKK+fH7rTuKW859nOdzsY6IsVtN65C/TEJ27zFtez4Xy2peYipXU0XCa3RmZgWrGMN2WBFibIRZamBFlLsgobAiv62Qy4tE+mqayWhOXNYalzXSAaEevQKWl7Ctlr59iwpaQESRG89tpRs1HmO89voAYZBSGC2IVjASokNHBeYrfs6OZJravp1Gs52JXytLnN236GTJaYUVtRx7aIvmthbyafzyFQt8/qd9nP3GEn3p2rPSQ6/pbUiHbv0NZsWGd1yMUifG+nY9MkTspEIx2miFtrou8yTxwpEc//HMJl41sbf1fRnecXpO4f78MH9zw4nlMcHQRgEe3B/kgmgaXwtJhbENfKzvOU5EQwznMxBY1t72gsSuNil20x/blXX6Pswxv+3K2xFIJ6vmfeo4Xzg1gSgKXDBR4M8vn6Iv3GgZbpCF6XWajBJWjqldgY0vBnS2QtoXqPK6jSluPT1OVYUL4hnCXVxFN+vML0oW+VNxL/+wsJOnin08VezjT4f38kwxwYlK2HW9fkFlLhxjnMZY+Q2S1Ezv002f8eTYCbII58Va5MFdQieDln6sv4X+tW3HsBYzYzfBzHuJXljgoPU7WZUlaiNBdpJbmIm0R5hDZzlB0+BkSmbTziLB4zL9BElvE3gsPMwl+ZNsWmicHdajVRhgDqEF1oTYqM00X6uVzMKqDj0Ws/FaDp/2MbBJ4JGfhfn2viRvGz5FSFQRbAY9nXh00ynHCubEJl6OOdNxyaYCjx0P8R/PJvnAJbUVMd+iwukdQpNuXXeY02F0ysuMSw2OoEA9zrEu8THqkS2x9D1J6Qrx/hBZ1cfsnMrggH3AJTM5XVB8RCSFgXEBBesEJXefGOCXE+4lFVIyQAJIUMZsKV5tImsHKwLm1rnUrUHCMva5x+/C6pracWL7f5ObAfjNa2a5dnve6jCg1lcZVynMcOqHjX1fKxLtxRrtlTeYDUpXDC7yxUMjfCq3nWF/kf+76znXdXmFkiqxawT+xv8438+M8eX0Fr6c2sSv9x/iF3yTrStgyegBjOMsz5Ri65bjtuHFYavb57Q7bzeWjrxGvegVetHxnzGkwspZTSfIdl7LHuHFMvHAVJSgTyPhU1ASIXyLCpsyCsSmG4hxOqySMBBjK6uxDidirP9dX0LfaP3ejLIJYzQCnRg/8XCUL96eRNUayfvvbj7ARfFGzWY3SLAXZ716WaMMwmWbb0Ugeh4/26v8h2YrmY5wXONHz0Tq5LgSl+ifXt5vXglodMxbjktsJM/F6LL1WIceKSJ2cnmSZqmBBy7fVHPK0WJBwN6CZH5fGwY1yrMiz54KcO5YqSlucua0ynzFz24bvfBaJbutYB53rMYeYyZQo0TIiUjbncMJ7bR9r1FhzO9paK7CWy5Y5JptNWJs9LEwpjTXya0miaA0x9U2wy6ut5u+21ym7sisw+X3a+5rzM92OFThv51/lOmCj39+cbytnA5uoffRvr4AbxBO8ZrYFLOhKAfnooz5ip45zGrjJUeO7YJwr3R61lYfdLuWZHNa3pVE15cIbfRqK4JW5NXYObkhG20QEiu0s1x3bMbHcLKKMLC0zJWukMjLEINjfRrnnJDqkgpojhRg/lutNPeeVnIIs7XPqDM1Qic++bBKtQovPBHh7kejHD3lZ2u0wES4xGs2LJDwVQnJKnGfgmIh3bdr9262e3F+6lo4rQ6JRFswr2p0iImBCmeNLRNQ88qB2fJr1R6gWYYBNeuxblXWcfACDaiy6XDj0GQkyvP5Wp19sjsLnN7X+kSNG/pneOipIOeHs00TgiemwuwO2EepaEcmsdrxq90mpbAKKdiLttuNFVpwT5QXqzLZssR548UmUqgkfC0zP7YiyO3AySptZVV2dNazep6msej8vhxqEm45OMIfHDqHv9zeO+sxGFb1gA2U2bCheVUF1v5k8yVDjp0+utUM6eXmutqdEa/UDExKBiirAiVF6JpmaVXiu3ohDHbex04EuAsE2WlZzwy9446FVcYjlfq2WsdeZVOmtm1yqMqGGbmeIMSYjtdOS+y0zG1cIjeH9NKz7+XDze3kyDMhvvi9JH1ylUIJPrzrBC8fbbTSKfNFc0jjJnjpVFt9H3YhFy3LtqEH7Jou3qrtmpO1dDlkIIBP0qgqreMa17MuWkyMwnmx3l7033paaGM7Ma4q6LCyaOfLtTIlTURu2Voa35Nf1PCHxQYtfPB4ESXh4/7jMa4I2aeLbjtKRH8QISi7IqbtOrJ5aWdnzAqdA9yOnY9WhtizochwrNkg4Bat/C2MRNq8z805LX02WqSktr1v8xhkiIP8m7tO8tkXx/jvR3bz3zftQxbbj1lsh1bfSDfiYUv9QUR5XVbREmLCj1RdIx+73jA7iHeow9F5wWDB6BVBNjuUVDWBpzNxrkymOq97NazE3QrD1oqAGLIW9YKsWCFbEOvhcYrRmqYXYMOMXHfMA+qJQJyIcTEKVZtIBHbQCU4+vPzbaKkOpES+eWucHx8I86GLp7k0sGC7tGcZ+9PlQGj+FtohxrZl27CitUWM7UJAmQc8q7/tynSAHaNlPnd3H6cRGcmqtjIKy9jZLGvKdUmNXq4WK7nW/mphBms6Zr0t6bIMHUrCx2RK5tGjYe59LsJbhqaISM3EuJWl9uxIhr8+PsI7SovEi+UaEZkvcUINc2jBz38dtc6K59RWnPpv8+pBq+tz3dYtLMJnHFYgRGZYVplPNcofnCLsVOKS5cqZEXaE2EyGneJ8u4ZD6LeWx5lw5e4yl+48yp//aJRPTW1nWCgw5C/xmoGVywTZCfRvR5kvogbK0N/b853x5LhrMH6orRIiuKnHqt4OYOxUe21BtprdhSW1K8R4xdHlZWbX53RBkJ2W0rwgEVZ4cbZ2numhKumwyoYZmcmhKiCQyNeiV2xaEOqWOXMcWnBPhsOmLHzGBCNAg/XvZ3fG+fYdfQD84+uO0B9SwGW0Q7dLwg1lTJM6N9mh7Op2Op+bkGyeCYuXvmK+BBbksBNYWWn7YyqbhyocPuknsbN2z0ZdMDSvJBglF9CYClovz5LVOBtRCY/LZCPVevIaqE3sgtlanScWfHz+wSTHUj4uG8zwofHDnBO1Zhmt2seuSI6LIyn+4FtjXL9pkZBP5fsvxkmXZW4ancQvLl9rSytYi1VBt1Zgq0lUQ39/JhBfr+Ncqwme3STRAyqJEAOLFY7N+3jyRJALJ7zLW8x9ZCuJhRtfDWhOV24b8Qe8PQtDwiVj1CAdsgi/f94xfu+Rrfy0EufcyOKaJsdWk8V1y3EvYP6ArYiL223tnNvDB25nqXIbKLxdstytLHbQWRgaT3DzXlcabYbR8kKU9Q71ySNBLtlWWxpOh2vb9EQgx2ISsEyMjRY+r8RYt+oZ6zAS4bRJSpEOq9z1ozgAn3rN0Rox7hCuHG1bZGV008aNUizzuax+t0VmzO3WTf9gnOwFTfGoe9TuYyGVdGa5rZhjERvbg5XsQrcezw+rdUJsXFnQVzp0h9G0TpqjIlVF4vc+P8ar+mf4vV0n8ItaWzGEje/n5omj7MtFuXNulGpJ5Tc3HGZzKeWJGLs9pw6tWG3abteOrI5fNbgxFHUzpKXdcW0kz3hyPsr/eW4jAH919zBf+uVjCMIy4XWjJ7Yiu2636XBjLTZbsx0NJy6ebdNx+nubLxEcCvC+ndP89bOb+JWzp5Ei7lauV2NsbxUesJf4+SDHrbLXrAD0zFNA+0klcG+RaOUZ2k0xvFvLWUcN2807WwukuEO4DTxvtvzGQrWB/d6+DUxp/dyU2svwjAwjGntHJM47rdRJihstnDn0llkbCjQQHDN0ohwJaAwGywyMC9C9OPRdd3RqtdRt5VzXEOatHafSdlaTjMdIStflO2brsW9RYctwmacPBLnsZbWwmOawbUaYibFRcqOnhg7nxTohhtoES29Lx/o0aqsdtX2aLossKUiLNT265YTHYeA2T3REAc6OZjk7eqDWN1YAwy11YjG2gxCUIdN4feY23NWB3620z2vbsTMerYRhwsY6bTfZuEicATZaVuVWD2yG2aBgToduVc6IVtIKKyuyDqM/imVWUyeYVlAvOauC/JyKz4W8zW5/N+VBKxlVzC3aIsfpdJpvf/vbHDp0iGAwyJVXXsn1118PwOTkJLfeeiunTp0iHo9zww03cMkllwCQzWb5/Oc/Tzqd5r3vfS+bN9fiD87OznL77bdz5MgRVFVl9+7dvPGNbyQWi3V+h2uEMNkuk1hdX5c0WN0QvzvW76HRGgcHTzA9nyNpPzM5mcvG7eNVtgMvUSI6lUEY6/EaTxMaNW1VBQ6f9vOBy2pahSerO3khv5M3Bp5heqjKVYdE+qdF9p6rMmxTn5XV2CidsIKVnEKHbgGM5kR2bykSLbXpxNligO9lJAAzaXGy6K2ahS8o9YSYmNvlFSM5bn8shqqCKDamgtajk/gWlaZ4xsaVhmAWjm2tEeJoTqxPnhL5ZWJc08SrpAO1ETuYBWS4emuOTSX3Qf+dBtwmBza3UUy6KJGxkk90rQ31auXT7XlXwtfCpl8w9weBgSAfv/Awf/rkVj7w6nmqCWd5hN4PVj3ogpf7Tndk2K0V2er6WmVJdTMuGctHAyp7FyKMhTtLqOFmdU0v56XO1YZncqyqKrfccgt9fX18+MMfZmFhga985Sskk0n27NnDLbfcwu7du3nnO9/JwYMH+cY3vsHY2Bjj4+M89NBDnHXWWWzZsoU77riDm2++mVKpxOc+9zl27drFhz70ISqVCnfddRef+9zn+OhHP4oo2lsp6mjzY/QaNsuu8XVLO1qHC1IAKyhbWGk4vM/JRT8VtXuBGtsJndb1990Bjs368Pk0RuJVTu8QeHThCgDOmZKWLXR2rNgFzI5WOuExWvt06EviOrIRlZwq8PC+GL90RWq5oJXjaiu4WFZ1varSze+nnZTi3XJAKiqNdXaRmBitVOOJKomIwpPPhLj4/ELt1IYwbWbnOfOEykiModamEoZ2NTwj8+Lm8hIpXv62i1FQVHj6eIBf2JqFHnV3XR+I3UQWaff83Ww/vcBKG6NaaJ13Jwr80p5ZfvpihGvPLtS3W1l2deIqV6DY1/rUjanSlyVFXsmwDuOxTk6DOtxYmfXtVmOVoGh889ggr9nQXmZfK4OBl9jYdnWtFXgmx3Nzc0xOTvIrv/IrxGIxhoaGOP/883n++eeJRCIUCgXe9KY34fP5GB4e5umnn+app55ifHycSqVCMpkkkUhQrdY6yyNHjpDP53nrW9+KsOTC/u53v5uPf/zjHDp0iB07djhfUNybMLuTtI+tst9YBva2OdY1XDo6tBNmakXhMDg0wWGQv2ajV3dfe3SaArQbJNnOeuwWPz0Q4rLty53+luhB5is1N97poSo776/WPP2HaqRFd3Jyu5xoZzk2QyfF6YBAoqRxrE8jUdKoSLBlxMIy0c4g6sJi5Ib4too/7Ek77BQhwvzNtkts7J6VUXPcQ1IiCPCrl83zye8MMjhQZXefUtcQm7MtGiNO1CdPw42TKd1pFGoTKr28OaoKwMkZH35UJoJFFI9dmtG7fUWs/d222jrpb3X0IELJaqKjcdKQftn4/e8ZLvDtA30syAKhYGP4MjvyaiS+djCHsfQKKyJt3mZnRTZHxNAldkbNsmXMZMN488adae46uLQ630YCEiv0Ijb2SkQ1sYILs2wjBgYG+PjHP94geRBFkXK5zOnTpxkYGMDnWx7sx8bGmJmZAeDKK6/k/vvv5+///u955StfCYAgCFSrVRYXl+Od+v1+PvKRjzA+Pt72jRmhJHz1f92oq53zdmxltOj49OXeXs26zPW3da7+QOuIEXoZ478eoxttwVhXN+tzi3IVHj8SZM/GWravL+wc4Uh2O/2+ecJLsobU2UFO76hFrNCtwO1EqHALY0a+I88FeWFvmHdcaZ1xrCOY2ohX8uPk6NH2N6Vbb+06ca8WZuO3sErWQr3f2jNe4qZr03zmX4Z5blaua4eNGnYzQTA6a1qtMugrDZND1bqUQifG0ZzIZFngq3cluCpZs2zp6cI9XX8P+0fAW59l1Q+aj3Wqz87xbYX6TDOsxlWnsda4z8qwZCUVaFVnAwzPwPjON8fLnDNa5Lb747aH6tbfYhSqvuVtrUivOfGNV5KsZ+ZrZSE2/zMfb2XssHumeh3X7CkyU/SjmsMdr2KbsoRVn+rRKNoOPFuORVEkEFh+aJOTkzz11FO8/e1vZ2pqimCwsSMKhUJMTU0BNWL9+7//+6iqiizXTr19+3aGh4f59Kc/zXXXXceFF15IJBJhbGysk/tqsOp1ap2zq9sNLFOhtnEOr+klvejunI63++0aVlbgHnx0RicFaFwWXuto91qPlmSmF2TO3VSiGIVPHvsILx+/k9/J/BCodR6645OecczYkRqJsa4VzYdVyDZbjK2c74zRBXToDlXln4S4/fND3Pzaec7eWIYO+bHlBLMFcXTrNNITdEpmrazOq4zrN2QRLtX48q39/P5vTVFTvInMDwvAchvQ21F4SU+cjagMz8hNFuN0uKYvTvtlDsjDEIa35U8BcDgr8m//MMKVo1neOWKfmENH13S7XiYjnax+GKUwbutb4TbgZWXM6vts1Z857bfb19LQZPH+BAFu3D7HH/94glddnmUwuUxE7YwDjWnQ7dHKeuyUaAmaV/DMGSZbkW2zZdlKjqGPi2ZH2+cPBZiIlHAUKa4lGc8Kt/+2o1Xs3buX//iP/6BarXLppZdy7rnncvKkdSemactTE1EUG3TEkiRx880384Mf/IA777yT22+/nfPOO68jhzzjR70WyZEdgTN+9E7yDcA1UW4nFmfX9UHzJYgs/d2FBm73/KwcFroFN2F/urJC4AJ6h7sjXCUSVJnPSgQ2lBlOHuF3Mj9kw0zts9Yz4/VPiw3XrsedNTtO6f9XfY2mBKPznf5bh5EYj5ySmdUEEoNVvvbFQT54wwIXbeuOvMfpW6nD4puwi1/cFdi1ZTepxluVW2WLsRHGZ/+yHTm+/Wycvaf8bN9ZXIqlXYPuhKm3o+mhal1OoWe/M5NkI6a0ftLhSaIZkdv+bYg3jszypsik6+vsumNbN0OUtTrHGpj82MFtX7pSY60r0m56fxPxCpsHK5w47SM8XjvOLnGNG9iRVqOzqtnKa1XO7netv5ZcE2S7bb5Fpem96D4CB+cDXDaRt03IVIeb0H29wip+F4JmZK4eUCqVWFxcZGpqim9/+9u85jWvYXFxkQMHDnDzzTfXy911110cPXqUX//1X29ZZ7lc5qmnnuLee+9FkiR++7d/u0GiYcTCwgJ33303O3wHCIu1wU8LtN/YewHVv9zqxHJnqRoFNx7/xc7jyPYKC7Ed9FUOd6Wu1XjPXt+l8X1pARGhpNb/t4P5voznNJ9X9QucWJTZdzTA9efmWRhSuEs4jxvTz5IPq/grAtmQxuCcWD9Wr68YrtUjL2WCMpPhXHELkeARyj4N/1KZsq/1Pc/N+Nj3bAiARFTlsnPyyJXla/dy79Dc5s1lGvYXlWZde6+/BycdvZdzt6rHZv+Cb2vXvik30J//i/MBFhSBLecva939SxzZb5FdzK7tlGUoSVCSRAJK7V3GSjB7KMDp0zJXxeY86/6EYAt7T1By/270566Xd3hPrdrvSr+rdrDWxk83cD0uBiUenw6THFAZGXeOzpArbiEhHbXdLxs4edXXuF3/bez3oLkvd4Kxny9HhIbzuUHV13x+4zVUfSBV4MFnw5ybyDIYqHrzCdLRi/7V5XXk1SAHKju44YYb6Otz4T3ZBtq2HAcCAYaGhhgaGmJubo6f/OQnXHTRRRQKhYZyhUKhSWphxPT0NH6/n2Qyid/v57LLLmP37t184hOf4ODBg+zevdvxOjYXHiCeWEq51eOx0BxbtpUlkYLhuEJnFyflPHwha8DaVMfSzG9/5U3szH23YZeTpcFKalBfCViNOUDBMENfuiTH92/8spSl30v/21o8ciZtmK+xo/AVlp0uKj6Jhw7GiQjQvzHF93aO8KWZ87g5cAcDLMWSHagSDdYsd5sOy/XU0iNLVmC7cG2nF15HZOJ2Iiw7UkVoTvJhhKbBQz8Y5sUnaksEv/tLM2yMlhs0qfozsZSRWD1Kc+9kLmOqryEhho5OvwW3VpNWFg4nmYTVdRths39/pPmb6imWrmMkEOB3vj3Ge7ZOs3FLjWjU41pbJIixW2koLTlvwrLVeX7Szz99a4g/2bSPzaHGscQKnmMFe7FEmZ+7zXuw7ZMM7XPF31UbUOTuWX7drLR1pS5zH2CHHBzJJLnv6Si/+eFpBKF5RUzH6YXXMdD3/fpvc5Ib7B6Tr/nv+rhfcO8ErR9TiUuWGfXMUg47qZx+rZbSkHmBu+8f59feerCWaLNVH2SHTvpXq5UTl9exwAgH5BbBGjqEZ3J88OBBvvvd7/LRj360Hl1CEARkWWZkZIS5uTmKxWKdEJ86dYpt27bZ1vfDH/6QarXKe97znvq2aDSKz+fDjVFbja5MHhOrD7TdDDtu6u4Iq60Tchthw0Fr2078326j1XtxPUkywa38wqxXM3sojwQUXpipaYuntH4+E/lLsuVGC0X/tEg4WuvcjYkXwF0kCqdEH0Y89J1knRhfdX6OHRvLDWGJ7EIOdQM9kbO4iRTQjfrgjFhaNyIWVHnX1Yt88wuDfPBjpwiFlvtpsw7dTD50EpyNqGyYWSbP0ZzI8UdD/PMP+njvlik2BwquNOOrHZXHTVtWEj40SUSReyu7sroWq/OtVD/aybhmN262igHs9HxfNZziu8/Eefq5EBecW7Akxk5oJW9QVNj7rJ/JlI+UVuMlC/Mi24dKvPbsLIEW+mOn8xrHAvN1uKnTnNRpcl5mIFRB6nSxwGqsb8U/WuV5WCPwzCzHxsZYXFzkO9/5DldffTXpdJqHH36Yq6++mm3bthEOh7ntttu4/vrrOXToEMeOHeNtb3ubbX1XX301n/nMZ7jnnns477zzAHjooYfw+/1s2bKl7RvrJYwN1S7veqt87Ob9PSHIsDZIcgt0Qxve7edn9X5bldXh5jq6Qeg2DFT40QthnptQ+Orxm3h66HE+Ef5a3eN/eEl77MWDOhtRKWe1ukRch5PVGODh79WWtq4+P8cvvynVtL/r7dsE2+fZznewWrFazxBI6QqvnMjw0wMhnn4swu7X1Lwt6052SwQZnCdXOikenpF56vEQt9yT4KO7T3BBfw5YjpADzlnvdFgS5R4ZCtrtr6xInF1drfoHt8Tcrt5ekmQvRoVWY6W5TNt9SZ+PN+3J8PjjES44t/WqBNjrkotlgaeOBAmUFMqKwPNTAZ44ESIeVNk9UiIRrBkiNoyrPH48xHefjRP2qVy2qcC5O8ucu7HU0lkPvMdJNv+2ijnuW1T4xgMDvHJLO1m5bODkdN/h5N8qhK6qyD2Le67DMzkOh8P86q/+Kt/73vf45Cc/STgc5tJLL+Xaa69FFEXe97738a1vfYu/+7u/I5FIcOONNzI8bJ+FYOPGjXzgAx/grrvu4r777kOWZTZt2sT73/9+QqFQRzenw83H1+pYM/G1+rBbxSR0OocbZy8n2HakduSgU/JsPn4VB3gjkTV3ON2w7Ld7Pa3O36njaCykMpeSSZQ0hpNHeOHoK0lv+no99S4sd5hG56hW0HWj3/jSAIIAb79pzvmAp2vf6R/+6jRbxpbboduYnSuGtTBhfIlgsSiysCjSn6m1dZ0M63GujW3N3O4mh6r18oGsyO33xrj3iTB/sOcYZ8XdSSlcbTdHhoCOE6XYxbL3+v22S2xfSuik7/ViQTY+x4wggd99xk6zxXUuI/Hwk0HufD7GaLyCLIEsauwaLvE7r5xl20C57uCmX8MNu7IcW/CRLYk8NRnir24d5N0vS/Hm7YsNPCJbFPjU9wb43V+YRdYVfBYE2kpq0QrLEjq49c4+nj0V5GOXtI4C4wTXBp4ufG+r8S20pUmYmJjgN37jN2z3feQjH/FU3+bNm1057LUDc8PyMkBbLWnb/W7lneoW7RIIpwQkQPNMzs0g4SV2ZwfoZsNv9Z5WGl4mPnp6Xrd47GCIjaMVznvWz19f/Fl+mX9oiDOsoykpgwOiOZEcNQ3xkz+r9cJvv2lumfwYLBGJvEggJfLP9ya44eWLDcTYDm4nDu1IVhxTqZ4hS3lnCk6mfRxf8PPuXfOMzch1wltrf7U2qJNiI0k2/j95yse/fr0fuaLwvy46zHDI8M4ME5m2pRPGPs44MXLR97lJomAseyahF/2tl9VPJ4upl/7a6XxW91iuChQqApGwaqs3hmVHZTMefCHELfcmOX9DkY/dMMP2QXdpl+XFCtukCsqYj3PHSlQV+MoDCXZESpw9Wqrf81cfTPDCZIDJeR+bhyr1ezM/L6soF7qVWP/W9FVDWO73+6dFbn0kxo8PRfj9G6YJOzhZu/EH0sv1Qiq0Fr6plRHsrgK6QVLbLW/ltOel4+gGHMmyFVFYQWtaNzLLnUlw8+5/fDDM6YzML17YGBDYaSA5Piuzeay8XHc4BdQIrLnjtxoIjNnMmq55svaOPvQ7p+p1mlEqCfzj54bpD2i85fJmM4bbEERWqzDmMjrcfh8/b21spbEnlOWSjTG+fVuSD75/piGrHcDhuMLhqQBln0DsJMT7q2gaTD0RZt+TYdSCyHRK5h0bp3ndhnkkIx8x9U+eemIvekcHOFmEjfu6IQczwkwyO1n1XAnYGYWcJrfdNFh4mUT/1Y+GeO5EkPe8fxqgadKmKHBob5i87OPRR5NUFYFwQCUY0CiVBX70swj/9VUznDvmbqy0G4PfdUmaaFDlsw8P8N/fOYOah1vvj3Lf/pqYrT9qLc80wrgqZ+VwZ+zX9fs7tSDzrUfi/M3bTrIRd7ISN1ipEKYrjZcsOXaC56gTbdRvxmp3bJ6Dp6+QdW0tzBDdwingu5t4lE64YkueQqWZwNp1jrmiwOOHQ/zJ20+SkSU2LWgEQ2mORTQSS6+wlcOd1X5dcyzFa5q5L31uiPOvX+TScwsMLDn0pcMq1YrAt/9jgGRE5cNvn0cUGpcgvTwLr9YiHW6sVWdyx21nCde3605e5jKOFvQuIi5WeexonP/1v8f4yAenObwose/ZEAUBDj8dJiLXSEUipDKXlcjkJTYPlblyKMPW8TLnDBYILFpYhc2WXbcTeLOMoovyCbf7OoWTr4PTRLEbhLOdMarVSt1KkXs3k+hQVCUSVojEGvdlIypHHw/zyMNRZudkrrpcJJxXkSWNYyk/QZ9GqSrwy5ctNBHjdjiEX9Z42/mLfP2JJF95MM7h0362DJfZMVriwFSA2x+L8t7zFxzr0K3Fxt/BrAjDy/cEy8Q4nBf5v9/vQxI1xhNVSDfW5zXzr9M2r32OqsGXn+3nsl0l19b4lcBLghzbWW3dHrPaS+/twuuH6Tp4eg/RzUQd3X5vVgS3VQYlq21WxLAVifNJ4JPca+H2TQYYG6mwtQh7zy3zt7FXEM8vkChpTVY8I8zSCqtwWwD+gMbohjJTk36euDPJI99LMjxWIZeT6B+oMHPKz2hflQ8aiLFbOFmKvcCJSJijY3STJHqpr1tEqh2Naq8nBe87b5bzhgrceTjOx/96nP5wlevOyhGqVnnznmnOH162TqkalBWBoLy0lDtfcs6a2CqzZqvMci5kE72A+TvX26ZaEeiisa6h7rVa30rX3+pcv3reAg/HwnzhH0a44Lw8yYkySRFeeDbE8VM+rtmS4+YbF5iRSmzdpXfijbHFKjbrGG4MYsY2Jwiwpb/MA89F6I9WuWpXgb/+1iAAtz8e49UXZumPLvepVklDjNB/G1cDdefYDTMyc6LG0Rk/n7px0pUzaCfw2t8KwG0v9nHbi/DmPWnefF6GaMD9eNIrnPHkuBptJAJrjejaEdhuLy05efnafaTdGjzdfgydfIjdfF5OHU2rdKFu6wfnkDteHQWN1yUsCHzpwQRvuKbmbXwsVnuu18ceglQthfMmGi0Hdnpju+2iWJNUvPBMiK/eMsRvvmOOQklkqK/KqVmZsWsybB6rIFgQY7dW426RZCuYv7tuEeRWDiK9HnjMcJogu9UNtouwT+OajVmu2ZhlviAR8akEZGsdo7hQosFdrpU1uMeplHuZPdWtY7ZvUenpN/DzBDdOarExeM1YnvPPLvHI/jBzBwLMFkW2DVW4+ZULhPy1tjvTpTllK+PVn795ih++GKFvSOOOvbUb2DxW5ugpP//lX8Z4z8tTvObCRnLuND4ZV+70fj8dVpkcqvLjzw4xMVChL6w4T0pXGPo3+IevmeYv7hzmO88k+M4zCf753ceJBDpLnNYpznhyfKZgpWblVpZzu4+0mxa1VmRBC4irkrzDvPSk/+4GCXZzbrvO2qvjpfHaf3wshChrvHJbLSzFAXkYqjAqzJMO1MSbxnBa7UIUYfuuIooiEI+o7NleIzJbxirkw2qTIcxtOtaVtiB1y6lvJSIMtLJAGferBschr8vXbUe/sUF/qMU5zVpg3TK8Ss6RvSLGXhyz1wlx53DTj5v74dGkwlsvdxfGzKp+s9bXa3pn/fsUBHjlzhwvnA7wzMHa1PHoKT8b+stMzvv59/sTXHNVjkjB/lp06AYKs/P1hhmZ9KLIE3vD/MV7phFXiG8auYWbb+288SITyTInUrW4/XfvizEQqXLt9rzlaky1Iq69UG7rWNtw6nCtPlKvBLkXIYvcwK1cxui9a7WvE+gzc70DckMG3XSgXgicqsKPngpz5SsWEQQ4trXKlNYP1BKBPBaGHdVpEqVKy9jEZujly7LBAS8MF12f5s//ZZg//80pBpO167Rz5utEd+0GdpMdN8/X3GG3Ir+9tCyuZPlOot+0PXm28l0wWotXgRQb37luOe6FBdltX7WO1mjly2HnlNbqt5MzW9Vm/HBTt5v+z9zfnzVU4qPXzbIoSWwLldg6UEbT4ON3jfLgU2FefnHeobbmlbtsRG3o+6efDLNxoMKoVl5R/wuv39Un3jLFe2/ZBMB/PJ5EFlV2Dpfoi69OGNB1ctwBWqV2NO9bazDHBwbnwfBMcZ7rhAS3Ir7GjsgcA7PVNTnJLNzoxzUNvnZ3gtMzPi68LMsx3zKJHZVmGRXmmdL62cF03YLMkrzCaEU2WpXNBDodEDDTlktevchzP4k1bDOGDYrmRNfPoZuwksO0stTrhKiXy+rm86423FxD1wceJ/K7StZic9/WyzbwUibI7cTZbRf6BLjVufRybvv+Xq0cuiXKxv5eEuHKrc0EeEOkTDq33IZa+XWYSbGOo1M+to14XwUzXqeOXhJUUYTPvvs4H/zyRgCqqsgtT/Tz2xvme3ZOJ6yTY5dw+kCNnYVVGa+RDNr9cDvprKw68pXWTzrBrdW4E1h1Pm4jPnSLHDrdp6rCvT+L8o73zuLz1Ujp3hGJUWG+Zi0uV0n7a2GK0n6ZRLkxXbSx47TqRJcJ9TKKOZFvfmqEV127SHi8Qp5GnbI5RJzXgdOpfDsTzZUcuFuhHXLktg13+/7sZBmeVpZaWIRXIoqG235qJSxoxucoShqcGbYFW9hZTFfiW2s1hq6ETM4r3K5qgTXpvGRTgc8+NMDOTSXO2li2XK07PSMjiRrBTbX2nMiL+Bcknn4uxMycDAWRh58M88ZLly/CzTfiFIfaa3haL4gGNHaOl9h/MsBVu/I8vC/Mtx6JsTtR5EuPDzGXkfmjd8wQi3f1tJZYJ8ct4MVZq9WssdcfsFP9bjswszVztUPQ6WhliemGtbjVtlZ1tCLInQ4mkgQbR8pUl0IRZSMqibLGlFSzFj8WHmdHdbrhGD1jmZ3EwooQlyTICSKn9gV45rYEI0NVbrjOXqNndd+t7rWV97U+0WzXWdKqPqP1uBO4+S56PZkrRlsv/9rBjUbSTJB1tBPpplXoJ6t6zxS51zpWjiRb9RlrYRLcCm4m+VaT08s2Fyj4U3zmm/34fRrbN5QZHahyydkFxgarfPOOBHc+Wav81Vcs8o5XZdifkvjXfx+kL1ll00SZsKRx0xtSXLWhCJnutHsvkb7a4Q/nby6iiPArv7jABSezPPlYlJ8c7OOycwrcdn+caHBlIlmsk2MbdEpkzdbk1Z7ZWhF3J2cDHXaN30ujr8Ql21BGKxHNwwyzdrib9RrRC6lBJK6Qz0hEcyKTQ1UeC28ADX4s7WaUeRLlao0MG0ivFQHWcf+/DCBKGn0bKpRyIplZmQ3hCA/cu5HB4SqX7clzzRVZBME+soWT9dyLds/tPjfntarPSJDbhZUUyWq/m+tZLbiZRDs58RrRS4deN+eyG+ztHBvXelKNtYhOpQqdENhWoTRXe1xtB63GXeO39/KxLFf+eoFHXwxxPCezkJH4y1uGiIVVNA0iQYVcUeKuR+Lc9UjNnPqut83zirMLTfWuBryGmz006+enB0Jcf0WOfFhl144SGy4oMHNa5vYvDbJluEwyorIS0ul1cmxALz60tfjxeiEl9g4PUsN+Yxkn3acdOiHDXp6x2au3l7CzJrdjadGPuXxnkfsfD/P63cszjVFhnierO5liEMKwwz/ddLxZYqFj3/01LfGFl2YJhlTOiVUZHi3zyo+dIuoi3fRqwPzujL+diPJyO+lO8h8vbdYL2XeLaknrqB27m8w4k0krx71eWWWN5zKew62VXu+zYHmiBNarUi9F4rxaY1E3/XCMfWc37sfq++n0uzLCi08KWJNkAbhiZ4Erlrb/0pVpXpj0o2pw8baa4+uzxwP81a2DfPRds5y7rYSYAr8Du3OaJK5EaE0rTGck/vi7o2yfKHHxrgIqMDlUZfY7Sb78gwSlisgfvH2m69dmh3VyzNoksOA8WLb66LqlhfUiJXHa5mUJ2IqQe5WpWBHTlSDEVtcB7UkPjGUAFBV++GSEiQtzZCMqx2I+prR+ni7uBuD84AtAzYoMNGiR7ZAYrbB9c5G3vavm9BDNiZxeqNaJsZ21WC9rd29u4aademnL5vduRZzNJBm6Q4ba7UdWo12az2v3bI1L124GTd3ZEXpPku2uyeo9mO+11X11Y4XBqM00ht1bLTitGK70Nejw4nPgdl8rrOT35nU10c2YEPRrlKsCn75jgP/za6foj6qcu7HEv310kmIU9h3187dfGuL9Nyzw8nOWHf2MBNVOprgSIWftvquBiMJbL1/knqcj/ONXBnj/L86zAbhrf5BzJkp85A3zyCvo47pOjtcI2tG4eim3GtEE2oUd4fZqcfDyTN1kjjM7n600jpz0k8qIvP8VzVHch32zADxZ3QnUolfUI1csOedlZmQKGZHAbpVEuYpSgfSUj1d+8DTQeH9OpLgV2hl87Nqp27qyEbV+/XbHmM/RqGeWmtpXq/S8vXAAXS04DeJeNZNWaCfNbitU4pKr2K869DaiT6Ba9SndyuC4VrAWjECtZBK9xFr43tyS5VYkectwhddfnOGbD8fYNlrh2rPzdUvx2GAF0PjS/fEGcqzDzmq8Uu3VTJD3ngzwF3eO8PF3TXPZjgKvvSDLJ28f4NHnQmxfLLPvSIA/eNvsihJjWCfHq4rVnMFaYa0TaLcdaDeeqx1Ztivbijg7Oey5lVn87HCAs88rIC11EgfkYVgK6j5dGbQ85oA8zI5qjSB/4w83oFQF+ibKjO8ukpmtff7VqnerltX9dvrcW+nA7chO1mDldjOBsRug7KRD3cJaGJzdwg1ZduNYZJeQyFjG6ji78ka0shBD83dsJMg1uOvzzJbn1dZyGtFrYvlSGDvW6rfXynhlN4EbTii869pFfvmTG3jgefjGT+JctLXIA89F6mUu39E6X3mr79XumqDz6Fi+RYWUX+av7xkG4BPfGKRQWX4Ob7h+kc9+s59fui7N1pGVi8+s4+eeHPdKt6TDbol3LaLV9a31DhDae8ZurKTGmL5Wx3XDwmzsCK3apVgSePTRGM88HiZfkNjxxxU4b9lqbMaUMsgUg0xJ/YwK8wxuKfGKazP4fConTwTYsbPATb84RyTafP9G0gmNsZGN99fNNmEcKLxYjO1+u30PXtvMWpDreIHbVQCn52U1udPbq90Arg+0bsm0cZuThVmvz03c13b22ZU1xvO2GzdsJ8ELrk/nGr0kxZ2uZJ4JY8VaQrvyu/M3F9mwo8Tm8/Mc3B/kF7eV2bm9yJPPhMnNe6N3Xi3HVt+9l4gii0GZf78vwfaJEjfekOaOh2I89ny4vv9vvzTEWy9f5NqznZOg9AovOXLsdlazUpqltT5weoGVRvFMlG3oaEc60OqYbkgv7NrmEwdqKUbzhaUl6v6a2XhUaiTHU8ogU8pgfZ+uPc4mknzl80MAXHN9mutfY/+R6Peph4BzSkXd66gf7aJXkpi1+k13IoVxOt5KrmImiW76Uy8WJ7uB2gsxNocv1NuwXbIEO+gTw1bP17zf2CdWfWs/9Fg32/VaIMu9HJtafSvGMuZtrSah4J4kv+flaX7/CyNcE1G56i2pehu/4+/GAHj/y1Ktb6YDuPU5smr733ssyqEZPzd9+DTF8Qqv3lJm7/8I8urzc7zuoizFisBwYvUcY19y5NgKa0Fr9VKDU8QAM9w6D7qBmZS7qX+twEssZDM0Debma45Ig/0VZud9vBDchqDIvJDfSb9vvu6QZyTL1yov1P8ODC53NE8+HeF1b03Vfxs11uWsRgTrRCFW97SWYLRwG2FHlK22d8PSulLolBB7OYfxfq0GcfM2t/KVdg0ZXu/dMvGNy/bbqpxxEmmWbjjpt3V0KwmODjf942rJ+la6v+7kPr22Mavyxu/H3MfY9SFuSfJoX83Zev+DUd5xeZZsTuT/+8txAN59bcrTtbtBO1zKTIw1Db54X4K7n47ySzfOkUgqjDwZ4G9vG+DSswvcePUiggDx7l12W3jJkGPjS9O0lyYhdjNbbVXW6ZheIR9WuxYexy5hh9e4xStBKqzQjjUjF1J57MHlBp0viVz0lhSnB6JMKYP0++YBeLq4m/ODLzClDNYtxlAL45byyaSmauS6b6DC294113AO8/NwYzHuBF6fv5f2akeQW5273YHQLEHp9beln0ufxKwUnEiyEa0cIqG1I5IdOu1DEnnRFSE2xgZPlLTl7S5Isrnt6f2fE9yEKGtX6+m04rcaOBMMG70YH+yIs1c5kw6ddP7B22b4xK1D/Jc/3tiw/1Xn5zq74C5Cb7up4zK//4URAMYnSpx1VZb8UyE++Plhzt5a5KbXpSjJa2OF5Ywnx6UQFIPLv/ceCPD3X60tKX/mjya7dp61ZiEzopvyADcDu1vHp5WEnSOOm4mDccDrFRk0w40VWb+nUkkgkxcZGykRi6rsPxii8rqw5TGjwjyj8nxDpry0XyZ7SubUc0F+7+PHicWd20tZhqDDfrv338sJh1sHSf392ZGYXrxfJ90zNFqknZ6beV+3nqcXCYERds/Kq2W9FanOh92F8vN6DcbrbzVhMsKcNMeOKDvB6p6nh6pN28zIW3/WgL3lvfF45+ey1saxtSC/MMK4ctYL6BNpL+NnKyvy1rPLvGcuxfefiFJRBXaMlPnw65vDnrWS9bhNFOYVwSw8cyzALfcnOD3vI9Jf5Rf/7CTnzQg8+oUEX3siCUBfTMEnN16r3flLxc6vqxXOeHJsxvaJMoKgsX2ivNqX0lWslKXTamB3WioyYy2Q5tWyCuvn7vQZmAcMWdKIDFY5dX+AU9O1wXnyixLpj9QmgcO+2VroNmWQKa2/bjHWcUAexn84g9+vtSTG6bAKc6bfNBOl1XzGnZzfiij2ekJk57jpVG4toNMJRqtJgw47CYIdkWtXTuF2ktBgKW6DGBthZeVvd9XEzYTaK3q14uEmPrpVX2n1zr1MnNaCZdwJxsg6RniJsmP1PF5+bY6XX7tsKa4CsgepTnOknpaX4wqqCn9x2wAHJv1ccsMi116dY+twmQ0zMv/8b0M8dzjI5Rfl2LGtyDnDjREpVnv1/yVHjsNBjX/8g5NU2ghPZYfV+tDWwmDZqeZqpcmyG22pEeYBU7cwebE0WV2D287ODRk4esLPT58NASAINdmQZuGnYJRSQK3cY0+NULynysnHBnjFa1PebuQMhvndtWs9XUuwmqjYOZ3Z7W+qc4n86cQvHRBakkDjdXRDfuMmRrVebqVhfC5mi7Ldc3Lbd7hdsTJaoVuRx25EevHaZ3cyRrgxsriRoun33atoRUb0QnLmxajiVppnluFYEeFeEtBURuT3P1VzDHzTH5xibHeJREkjkReZ92k8dzjIh94xx4U7i/V7yrP6qwc6XnLkGGoEwu/zPstfS3D6YNsZ6FdKLmCG0XLSbufr9Vyttumwe45WliavRKDdmLtW9UQ2l5m+taYX1tTaAO17e7RuLTZjR3WaA/IwhX8vMvdjuPzKIq/89Qzbdy3HvfQqJelksrCSaHVfVprTlZbVuG1Hrdqnlfyl3UmAToq9WEeN53JzT05lvMigvF5bN2BnTXZzfrcTFrfXbLa8dtt44+Z5e3Ve7eQa7CZO4bzYESl2i1bvq1t9hlfDktuVhG7EJfYKTaNOjN//kSm2jJQ5Ztj/5L1xYmGF7eflyZvkH71YIWkHL0ly3E2shtXYrZXTC1odu5LEZ6U0y93suL0SAbfnt9OZGuUsX/nsMItzyylzhz8ZRhquDdDmMG5Qk1EMHlzgvrsG+a2PnSKRVEiHVVq5Z7j1yHeL1SbTRkKvk+J277Gde+n28+wUrUidndXYi7TAlbObA3HstG/o1TNVFJgqyEQHlpds3FjZ9Wsqy87X1jRhs3jm5km7MbxcqyRF3cZKWvHtVgN7kezJDC/t2WpFp90+sN0Y7U6kshdWYrvzZw3Xse/HMeLzEuecU0RV4Z77Y9x+d5J3vHm+ntBqLeKMJ8fFoIbcRe9Xq5e9kgS5F8TYDVaaPHebIK8FCYoZdiTAzbvU7+dLnxvi6PFAwz55pPG5NViPlzqb8ViNNO99PMJlV2fQQrUVlVbX2U2spFOc12s408/VSziRaKt97Whx6/VZkAivfYOiwENPRth9WRZ/21dicW2Ge83MyPz43wZ4zX8+jdTjUdNMuu2euR1JXgt9odVKk5vvw43Bod1xw6u+2uv3bHwfnfZx7d5nLyOBuA0PGM6LxCIqv/H2OV48FuCZgwEefCqMX9YIhVQiAY3/cfMUsQn7rHdrIX/CGU+O7bAWHq5XWHVqa2XA7cWyeqeW1dWGmyV8c3mnjtPqGe88u8D0jIwswvQpPyP/sOzOPqUMNqSNHvbN1sO4nRweZPufK/z0UxHu/G6S5HiFX/lvk55m6sZBulPHpIZ6bXSya1m6YfWuu/ltdot0mjXEdnV7uY52jvF67WaSrJMDN5a9SlngwFNhJnYW6A9WPZ3XLWJDVV7/sdM9qRucI2Q4lTeS5K5eTxe+xXauy43EqR2/FrfOoVbX4RXdklt06vTeDQ7kJiyj1b5wXuTi3UVGB6rc+7Oayfr3frkWQSmxsYwkWX/jbhw0AYr0Xjb7kiPH7epV2mkE3cJqWYu9YjVCnnUrJq0V3MY8dQM3URCcSLH5b73s+ZfkePBHMaZP12xip38zj/CvmxACYp0YG9NH69tG5XmC47DlL4C/V5m/34+mUrcst7yfpcHXapB2S6DcEiTz/XciYejmO211ro7rcUmAoPlZpgMCSFCymcB4Jblpf20oSJS7Qy7bJcxW1lCrfcZ34PNrvP0j3SGunp+bzfM3wtZxr42JSOs6rCeelsd6kP248bno9nfnRb5mpYFebSv6Wpnst0uSO+E+9XOOV/jAL82wbXOJYLDxO7B6P2spTOxLjhyDOYbm2rUgd9vpzivcauYsj3XQWZm3r2X0kky57dytnqHesT70RIS5mcbF4sRjp1m8eoz5Sj8A85V+Fkt9xAML9Pvmma4MMiXNopU1pHvSzN8vsXVPHtlHS+ikqxtwM/hbalxdyjGsyq21CaURnZAhr1IH27J+5y7faX+nxNkLefSiz+0UnZJUV5OcpYlMO6iUBMp5kUhf61S6TdFHejD5X8mJqPncRpilOGa4JcYtSX8HKyO9HBO9ykTsIp300gDo92mcs8s6KPFakP844SVJjq2wlrLyrKVG4dSxe7H0WG0/EwjyanTubsiy7jz24guhhn2Lv7Wd2Uv7GGa2nhlvvtJPPLDQUO5Upp/Q/zpCv1ThP/3XGTbubB01vR1roxVhMhIsfb9tWRdyDX0S19FSZ5sTQa9ykm5YA3uBVqTYCZ2QYrv33lDGQgrSLazV9+EFX/uDDeQWZDZdmGPj+UW2XZ4jGO2BlMJlqLp6+VWeiHYqX7CLWtMqCRK012a9jgN2cJoIeLW6rmY8aKccCmvBevxzQ46NaCfdsBPczuBWghQ7WWfaWTLUj11H9+BkCTF3mMODZY6zrDMW70vDpX0Adcuxjn7fPGga4myZ2DNTRPwl3vnbU4gt+plOLI9eLJGtlu69WEctZQYW2+3qaJXowUlOYnfutYZOCLFdPW6Jspf3Xi/XwUqWfvxLEbmF2vM79mSEY09GiPRV2XxRwfEYL8/S7rl5JcurBbfW2bY00B60357r7pEfj1diudJBB4ywCwm4FgjyzyU5ht7NmNw6C6y1ME5ujm3XyQZqs/G1htW2fLjB5rMLPHZPsv5bGhIaNMY6dCty/BP78b+QYRHY8958EzGuW2Hb1KQ64YA8XP/bmL7abV1eLJStBvRWDmluCUG7ZVYb3SLGTvUa35eb83Wia25lwT8T3km7uOI/zTN/ws+LD0bZdEG+JTHWYUdu231W3Y5UYq6zG+S7m46zXh1Z2yXIVuhWUh03JLPdxClWll87B3qvZYznWC2sQcqydtGO5XclI1D0eoDo1JLcrWWlbsDtO7ByuHFjnexWRxmKNW4L3TPD9Hs2gbT8rnVifF7geeZOLKLIIjfcNMueq6yjvrsigG2QKyMh1omyvs38u9U5jXIM42+3WMtkqVvENSiJFLtMgt1OcKD9+3AjtYDOJjArhXas6l5x/usXAbj+A82TYuN1uJWvrBRa9YVeInR0k3hanbMrxLzDlY+GujrUKpuzEPYibKodH6pU4M8/OcYFl+R49RtTLetzcpzsVXpzN1gnxyuIXlsq27EItoNudQLdzjDUK1h14nb3367nuh0GRisMjJeYO7kc63jo8CTsDMCSg51uSS49qVDOSrzv08cYlhVXnvRGHHsyyLMPx7l4j599j8VZOORn2ytzbLyqYBsjGRoJVat9VmWtSFivLJ8rhbV6/U7vyqlsK6LsFU6TnnqkFNMz7FT73GldnTgzGicyiXK1gcx2i2CbCXI3n5/tOT3KmerX4nUVsgdW616gmwS5XmcXxsl2wt+1e55CXsTn0xgcso5j3K68dKXDuK7NHvwlgtVYtu906cwtzMvz3U4E0Ct4eSduNbDGZ67/bTdR8dp5+oNaAzEG0P5sCoChW2BGGwJqmfKk2SzzyOz9fpxXvWnB02TpuXujPHhLLQzchBBk30/iAEw/F2Tk/EkCUc0Tseo2WkVRcBNlwaqMHZkwEwkzmUubiM5ag/ld6SnFO6mv2wQZmgndsZh9SBWvkTScyq/mO9PPbXUNbq3q5rqc2ri5rBHtaMitjvMq1+pNGLtGtHQo7FIEGKtje+JcatIod2JddmtNbofIhsIqv/2HJz0f5xbZiAr5nlVfx9rr1VcQrYJstzvDORO0rN1At5akVtJy7Db8UKeJE5y0r151dq/95Rl+8G9DTduj3z/F0JUzSAMC5UMq2WcDgMIT30my+aI8Q9vKztdtGPBOTwWa9gs+jW2/U+05MXYjuXBCJxY9u31ut682MbaTr9iV6+a52q3X/J678QxX+z10E26iwHjZ1+pc3cJakrtAcz/rmgybQlm2E96wV87sTtGhwNtY2kqy0O0AAieP+0HQGHfIjOcW+VDvOdZLp0dpA06hRLxiLRHibnZSbpb8OnWqWImwb90mxG7raBUhwc1y4Z6rsjz5wxinjzUGGMr8e5nMvzcSYDGioeYETu0POpJjc4d/8a+k2XPjIpJfQ3wiT+5Xy/hqQTE4wMpYjK2snZ3W1QtrZzdhRy6trttcdkIM2+7rJTo9V6+s0C8leCGtB++JUMkL7HxDFrFLMcpbwYtkw43fQLeT0DTV364TosuJdzsReKC35LldotwrzEzLlIpiV8jxSuCMJ8f5kEq0zWO72SBWKiD6SsFuyQ9adHJdiGzRC6Lc6v148ex2m5q33ZBf5mU5QYCr3pTmW//QKvomRH8hQOabZR75Sj+7XpYlYOUQavFuD8jDkKj9PTEYrBNjI6a0/uaNLjAqzLd1XCuCuxYJsNtrdltPu7B6V+2+h3XUsBbbG0DqiI+D90TZcl2eYNx5DPIyGfMCL2TeSTrSaRKabpFsrxZ1r3KY+nEeJRhewuutJQd4gAsuWQEtRBdxxpNj8O6N2SkpXm0S3JQFaaW9kF2SZCty6DWxiE5qO/2wvbwzL9o5L2Gl3HptW5XbcUGeD/3VMf7x9zY5Xo88IcI/bSb0uUm+9N83c+Hb5jn/8kVkv2ZPii3QLhF2qsuKnE1p/S1Jm25p3Pe9KE99KYl/UKPvapWrf2EKnyklaa8tqCtFbtuB3Ttbi6R4rRFNp/elX2Ora3XrwNhu27Cr85L3p7jk/SnX1+a0v5fvwyr2dbt1tEwu1CZZ7QTtOkC2GhedDE5eVm07CRvXavzsBfFe7URiLwlyDM7emGcSGXb6UOw+hG5GqfBqAQCXS0pS4zYrZz4r664rOYTDR+T13bl5lq06JL0Otx7Wbi0HkYTCb3/qCN//2gCSrPL8fXE0pfEc839ZZPFmEe2qfthQ5qk7BZ7+RpK+sRLxiQrBPpX8tMSuN2c4PTHQdI4prZ9+rbWF2gpTymDTtlFpOfyUHXlzIs86DsjDjOypZQEszwqcvk3i1ts2cOEXyuyU7J3N3BARNxKGbsJp4uGWyOrvyc0kplWZ1STPbqOXrOT5zdfh1trqhTy3g5WYcPUyQkmnsCPXnRBkK9lgSWoeS9ppp56kJ21G/TDvdzuWeCGd3/1Bgr6RCnuutg4NCvaa53ZXhPXjVpMgv2TIsRndkEysloW4HZ1SJ5650JljRjuzdEsnNifZg81HYvyIugG3M3EnzbDVsXaWdHPZVp2kz6/x8l+bA2B4R4kffba5047/0+H631UB0ASmF4JkTssU5mrvWbouQAytIwuxFRl2KmMkypZlW5DG2e19vPPfT6BW4cU7ozz1xSQbFuag9WU4opuko1OLezct9m7w82JVbucdu7W26nCS0hj14W6xmisBbmVBa4VAd9OpUzcNeHn/bp6Dq4g6HWYl7HaYVYCRzSWiCcXzcd0suxp4yZLjdrEaL8xztjKLD6jdzGf1OjoMSdVrhwponp328l216mBaWfdbdXJeSLW+3XzMWVfn2XHlESafC1JISzz4hQEqxeUJhD+qsPnlOfq3Vdh0VYGDvmGg5qQ3pfWTc7hFN8TXCdOV5eP1OMx2dTZYl+3Ou7TyMCX1MyrPI70F3vmGE/XdnYYq84KVJrC9xFqWYtiRDzeW3JVoC53qyqG9tuTmmE7f32qGbewm2tU3e73/TrXctpkn2xjP3RBkL3rknRd3Ryu81smwGT/X5HilrY1Ox3k6n1l31cKK6RZuCbKTZacdkuzZKWEVSbET7GIfW5Ux7neVsc6mjCDCxJ4iAGddkwNArcJP7uhj8aTM7jdlCSXVhs7baXCdUgbZ7EFWYSTBrcpYpb3Wz9kKDWV0j/w2e6+OrbsdThzs0Mqy/vMMN2RlpQldu+dr9f1B521hJQg0NEceWWtacvBmVW7Xyu9UnxXcZgo1wuuYCu7Hs07H1F6FroPVcyxsmxzn83nuvvtunn32WcbGxviVX/kVACYnJ7n11ls5deoU8XicG264gUsuuQSAbDbL5z//edLpNO9973vZvHkzALOzs9x+++0cOXIEVVXZvXs3b3zjG4nFYp3fYQ/hlOShU6e5dsPfdGrBdSLIdtnOWmU4c5sqdi1mPGoXbkO69SKrYSYsc+4vZuq/D8jDloPllDJoa7V1Q3rtMF9ZPpee2rqTOs2kWr/OKQbbIhFW92lH3L2ik3t0S7qnK4PcG/o9/lz5Ek8Xd9ePbxfrpLz3yJv04d2YYJnr8PoejdfTCVH2kiFTx1oizzqc7qPbk41ukGanbIjLqDo68rkmzx6Tu/RyLE+HVUrV3nOFtshxpVLhn//5n/H5fNx4442MjY0BUK1WueWWW9i9ezfvfOc7OXjwIN/4xjcYGxtjfHychx56iLPOOostW7Zwxx13cPPNN1Mqlfjc5z7Hrl27+NCHPkSlUuGuu+7ic5/7HB/96EcRRedZQjaoUXGYWay2TKJT4uNm1usUds0NOpVCtPrQ035ZjxLmiG6lXG4HbiYxdtrgTmUx3YLxnTtZio2DqnmAna4MkldDrs5nJMGdlGmnDp1062TU7jyLJYuYdBY4wvambfHAQtP5Wl1XK1jVA42k2m5y4QSvpNxIps2TpJVAO9b7tSD1MMLtPUwpg1yhBT0R4nbIc6tjnN5xt4iyDq96bR2rQZo7JcWtynaDNOswPh+3Y7zXTIDthiA1H9NKerjWjWFtkeOHHnqIXC7Hxz72Mfx+f3374cOHKRQKvOlNb8Ln8zE8PMzTTz/NU089xfj4OJVKhWQySSKRoFqtEbIjR46Qz+d561vfiiDUHtq73/1uPv7xj3Po0CF27Njh+fpWghCvBOldKfTqWoxLbiVJxJiDzYsT30palZ1IsvGjdopIsdIwvz+3xNgMO4LmFnZE1EgyncoBFAuN06hgKG25b5ot1hXkk84XCRBONR9j2GY8pw79ebgl20YY73++0t+S8Br3uynv9V31++ZbS11syJK5PbkZ+Lul0XYT1aTb6LXD6krB7eRntScgKxktoxUR9dJ3gv0z7mZ8+HacANtxlu+KU5+Nb5Tdfr3MWkFbrOipp57immuuaSDGAKdPn2ZgYACfz1ffNjY2xszMDABXXnkln/3sZ7njjju46aabABAEgWq1yuLiIolEbfDz+/185CMfqf9eafSa8KwlYuwF7Tg96eX30Hzf5g/XSRLSC4LspBV3Q5LXKuzekxtiPF/pp6gEgULL81gRRTO5tUJTGQOh9WeSDbtUoBxLOdZnPqZVeR1CVeHsz9/J4TdfiTbQvEJlvD+na26AmXgvHasT7nhgwRWZ7ffNW5arbwtBUQnaknbzhESvU4edVMUIfbB31Mie4c6JXY3nbfN96asx3ZIVmetpJalxQ4xXmxRboZf6ZS/E2O0kx0tkHlf1tUjk4/b5tKNh7sUY14lvjRntBRv1Bs8sTVEUpqam2LlzJ5/61KfIZrPs3LmTN7/5zZRKJYLBxssOhUJMTU0BMDAwwO///u+jqiqyXDv19u3bGR4e5tOf/jTXXXcdF154IZFIpC7VaIWsH4otHOKcSNBKEp1OSHG7KVdXY/nKyuqUEsOWDdpLJr62s++1OM5LIo8zAfo7t+vgzVZDJ2mCFysvUCeNOmEtx1IUdSKpE0dTGR2B7LKPQSm6rJk2l2sFY3lLorxkKdZkiaOvu4ziQByt4JB314G8m2GXsLvoeFQjzATa+LfxfZSVoGtLttn6bEfUzHILK3TsLNZlEuH5/D10yHQiwPp7dCuVcVOneb9X7flaJMVm9CKtvB3Mcd7btf73qo1bja3tPp+2rMoefYm6DX2sLq3AuTyztXw+j6qqPPfcc7z5zW9GFEW++c1v8t3vftfWgU7TlsmHKIoNOmJJkrj55pv5wQ9+wJ133sntt9/Oeeed17ZDnpcEDisJr8TY6iN2Q5C9ZvDqBkluZVmy+gC/TcwAAHRISURBVIjbiQtpFwvSLbzGMF7r8JLxTodxIHUadM2ky4kIwzJpNBJc8+9SNGm7zwynfW6gk2tbMru0PdefrDFaG1Zrdbz52pyIfJ2cLz2roknCYYRuXW5FePV3oVQDFAuBhmN1mOtwY1kGZ323W7gKzWex3y2J8Cqv6IU0ohVhNT9H42pMOzIYu3qtJj1eCHI3EtKsNNqRXnjNBNruBMjq2bsh2KOSe4dcqxUdK4uyEXbPySmBig5jNkI3xxrLdptA62Nz1d+iYBfQluUY4MYbb2Tjxo0AvP71r+crX/kK119/fQMR1iFJDlYZIBAI8Ja3vIXXve51PPXUU9x777185jOf4bd/+7cbJBqWmLwW5r3YZVYG5iw7bpcBUkthZCZs9re6U7vj7FAEkqq7OIYpMdxUf14L4qarDy4MM7H/2rbOq0OfLQaULiR4sfl7rcIqa5O5TenvJ79k+egHckt/b4aWjna1ARy2pCPcuPSdK1WDUtz4NyCWVaRK7fv0lWtvp+LPYIvF2n+qVERQFAQyS8d21tNV/BbMdrHxp+KrWB/cQj0iVazux7TN6VwW9at+w3OUjTYQ9zKy/cX97C7ATfUxz6sEbbm8JDvbYfySmaxvJyhZ90RhsQDsrP/ebNiXV0NL+5sREYqAc2p0J4SF2vXkLcIQeqGiOdPxTt+N/r3YoWzYvyUrccP+2jN3et5+w3MtK8Gl343Pf3k7Tftq72UDgOFZ78SMiOA8kuS0YL1MuEXZ1YR+ZeaxJGUKx2Y1bkFz28hpQeKpPq440Phu3Top689eh117b8bOhm+lVVkddu/R/M6Mv8zPqsTymKqPM0HTfvM28z4dej3BpXqMdTcdazGmWcHy+ELvhRWeyXEkEkEQhAbSOjw8TLVaRZZlCoXGxlAoFJqkFkZMT0/j9/tJJpP4/X4uu+wydu/ezSc+8QkOHjzI7t27Ha8nu+UnCMnpVTHxm9GubGK1A66fMPztNBM/YaVjdWmVOWf/xTx31nPA8ixXP69X67XxQ18L790r3C5NeY3P6aSTc3K2061P+vbXHgryhX4DgTPKIDQVfzqFyDiaJBMxW3jtdAUGjP/gTwjPnmD6gleyuOeXWh/QCoZz5uJZ6zIKRBajzmWsYOi/9eOt0FDnUjIpo0XZFiZbglu99N/s3Mln50/z71G3g7YDbCzZOoKhxoHWzups1jS7sbAZrcV2lspe6ZrdWoXtI6HYj2vmlZabqvDvppUTq+euP+vl4wP1VYFl7fry+4gHFlgs9TW9Eyd9uVu0suSvJcvyidZFAHeW4isO7OSRHfuBzkJaWsH4XXQrfKQOq/dl9Y6sxvtuTX+s6jFv08e8AO7GOKs6tdQwHG6OMNRNeGZzPp+PwcFBjh07xujoKADz8/PIssz4+Dh33303xWKxTohPnTrFtm3bbOv74Q9/SLVa5T3veU99WzQaxefzWVqh7dAuMXWTPcduWaET9CJl7agw31LE7/a6VlKT3ImHcjcCpncLtlpph3bTTpsyt50prR805zjFdrpVR+STTXKJiQfvYeDpb1FObKWS2Er6/A80H5Zw7mpTl/8F2ewpVF+EcHolXCsaMXTcesBrdd1OMBJnnSjbaaidYCcDceuUaCznui6jc6GNQ6EbGNvVEbY7EjZoHsxXmgRDayJs65Rp45CpP3PzWohYmMevmY7JJCnHUg3vqWjxno2tsji3ueEduXk3biKemGEVd9vr+7KKcrIahNpN0hUjdOdJ43PzIoVxetZuZW1eYJWB1Cy9sHvuq5G05UwJSNDWVb7sZS/jrrvuoq+vj1AoxPe//30uueQStm3bRjgc5rbbbuP666/n0KFDHDt2jLe97W22dV199dV85jOf4Z577uG8884DaqHi/H4/W7ZsaeumvKDdOMJe0CvLcFOoGbtZcRsBzN1+NHaE3C3MH2+3Plard9ZNwtxtwusGVo52YE+KrQixcTA1alyNRMYvFenfe5Lttz6MnC9z8Bd+m8JQjMhilNQ5b6AydAOaHEYqWL8jO8JbJ5+iTDW+seX9toNOyHa7x5pJtRNRdkuSzbAiumI5YEmKddS3tbAMdxu6hVNvc2aSbBzM3eiMnbLGeXGY8jphtCLDbpxDrfTykpohoDjr6APZGIFsrKGNlGOpZiJudnA1XKvdN21H7tzE3TaTL6/acLtt3STKXmJO20G/56IS5EjW3ippbCtWqyhuo9F0C1bRS8xE2ej7sw53aGsUv/zyy1lcXOQrX/kKAOeeey5veMMbkGWZ973vfXzrW9/i7/7u70gkEtx4440MD9uTw40bN/KBD3yAu+66i/vuuw9Zltm0aRPvf//7CYVaLxkel/ooO9yFVWpL83YjVlvi0Ao9s67YzDBX6nn0iiQb0ZW0nKs067XKcmdFiu0sX8U5k6LNMLDGAwtEnpph9P/tpdofJLHtfM554KH6/lA2hBioET5N8qGGaoO8nNuKnKIJxaS1xqzbVuJgylsaUbvrMtfXqpwZ+n1ZWZ7NUg4na7KdE6IToba1KLeI22yG1SBvNaHSf9vVqZc1k7V+33xbzkpe0pO3gpvVEy9hBp2cRt1KeKzqMG+zfccWFn8nS7JXImdOtAONxMsxqYiL7H29DAPYjiOl8VkYn5XTSlu7cc/tIpdYJQDyarW2e18suX45xU/uNBLISlmgj0t99Ma8sgxB86JdWENYWFjg7rvv5vgNt1Pus9bAnenoRufhFL/UDboV5P+c/Rfz3M7HOzrvSn147tJydg9uJiCtwrJBc6KKYiHRYPEyWxIHT7zI7v/7Q574m7cxcucLjN/5PABnv+tG/iO8geLAMLETBYILp4lPzpDYfxelvk2UNryW4tjljuTULRFdLRSTqutr8EqWneQZnvTOBpgJ8pHLdvB79z/KF4ct7sFkKW5Fju00xGBPkN0gGEoTDyx4tpK1kyHQ7ngj3EYBqcPCWmwkrkYC7KRHB3iT/wjfLW+p/za3A6vj9TJWkyM3kyIz3EySdLSKkw3d18x2G26041Zt4sbJIF/bYNZ+28BKXtMiGo0Ou+/OSkPeDuzelz7+d2uMdQoX265xzeo6jAReSw3DD9/DDTfcQF+f9wmKG5wZ4o8zDL0OjN8q/JGX8El2ddTLtliOcXOvU8ogm23Sp7rNKtSpNtkL1goh1mFFjK0sxYulvnpn7j+9pUn3aEYmPMpzv/pacgxx9GVxIvvmePHNb2LnosDEjx+if/+jTcfIU88RntrHwjVfAoeId8GUaEsqV5sYe70Gu7JuLOSd6JhbQfWXAMPqms2g7GTp7cYgbIYdKW4n62In6cfdZGy0LGMixTohtiPAdtvD6aDt+29Fpp3KlKIZezmNA1kzW/OdYBX6rxup4FcS7erHlWKB4tyIq3NYymyMhggDvPQEZoLs1ULt9L7q4f6k9uWURtiR4E5WnZ2OPSAP418By/E6Oe4Aq7Es5Kac1dKR0yy/Jdm2IchuibHb/a1IOlgHPl9JZ4KVhn7frUgx1KQTdlauuvUpnMK3mGf4sRdRhDg7vnsXAKcvOYvE0Wku/ft/of/KK5uIcan/Mkrjr0X19yNUBpEWyyiJWkQLKV0L6KP/NsNMLvXyRtgdu5bhNAHQYSZIThZCOzRpUO3gEEPZK8yDcTCUtrWiGcmWPqDbZfjrNVqRCLvJgC47svt+wP492ZFYMzF2kt8Y95vLWElz7OJ42ya9AVfSCzOcYm+7TRe/GnBDhO3etU89SawwXv/drp9AE0wk3KoVGL8lzysdWL8v/X2Yv8fpyiBTvllGpdmukOSXGs54cjynJchpjSasXgjPVyJNarvZeJwynEGzDkmHGw1gu/ni27kXN0S5lUVZR6tlmdVEuzIK/R2anUV0UhxbGjSdtJDhwxUu/vQXG7aposTIYy+iCRKCphi2hxGXYmJKhXk0YSeCIYSvFck1wsriandMK4K9VuGGILeCkz7V88BsE3nCznps5zSnhwgzwu2yMNT6onb0mFZ1m6/RS71OS9ewLD1yIsWtYCWrMOvrfXHJdp8VrIiysZ3YOXg6Zoc0W5VdTKTcTIjM5duR4DjBixxEv4YGtKkbdwO7hEBuHDetol42ZRPF2/1brQ60IspGkgztJRTRt3XbR8mOZ0S0+LrluB3YPdB2SXM7xLhdousGTjpTK9jFwfQSb9FoPfYaGqcduJVgWJFlaL0s060UpOZ6utE52BFj/V3XO/+lQT1m6KCNS8B1sqWqJA+nSG1JUEyWUCUZUanpqlVfArFS60hL2vlUxt9FeSBDes/NgACCAEoOTEH1rSClS47kthWZtivTCWE21teqnlbX7xXdllS4jYG8fAFJx4gGRlgto3uxALpJM+5lkPdiqbSC8drNGeaM8iM3xNhMfPXvymq7vi2fKHbNAdWuHp0k202gWlqVbcLRuYFty7aTdTicKzg7z+Cz+8iNDrOwazuJw8c471+/wpFXvYwT1121TBitYEXwW4TZ0+GWGHsh0G6cam2jyBiv20CSXU82DN+73TdnJspmkjzsm2WKQUddsl266l6Ep11NvCTJsR3afeC9JLqt4IYItzNTtxP9m4myMdySU7QEO+jXr8eOdDpXK7h6DxbJGN1657bzcXebDDdsVwbrpNhqQA9kY7YDN9QGbzk3x+Aj/0T01AFOX3gFI08+Uqti4mYqmVGi6b8FoMxZCBTQBBnKGggGq68UcXUfnRJjt8fanadVOTfW6W5asJ00p+3Ab6NldISF1MJNf2EV8s/L8a3q7KRuNxZsK0c+q+8IaIg1bCQxOtHJxbOIpQKxyX34MvP0Z1Sk/AK+koAqB9B8UUr9uwkP7qlNJrGQEgUEy2go+rb5zXnC6aCriCk6Wdbbl3Ei7LTS4BT2zzNMBFSoVrn47/+FmfPP4dgrr23YFzt4Hv70DKHZEyxuPhdN9jdc585vfpX4iSMUk/1MTU3jz+UoJvuJH5tj/OGDpLfGKPQlkcuNfW3t/WxsTN+eeo7+F4/jzywSTOWRygWq0a2cuvQqKtFG7bj+/MyTj3B8gaHFpXHL4vt17VSrKAiaSjFRQC4UECtlxOoJtLCINFyhvBghODlONbRseGgZg9zCsmxJqpfKOFn+rSabOsd4gZ30++Z5mto47ZSsZ61H+GoXP1/kuAW56jSOZiewCzFjRYidlo2aYKMzM38c5rA9RuLabp75VuiULFvBKRB6fXuLZaOVgllPbEQrUgyNnTywPFDGMvjnj5EZFNh0519RStTOM/LkIyi+cdSKn/CJf2o4n58XASjFJNR4EKa834+ZWHolxEIqi5Zs7ajktt5OJBxeLM7dkFa4HXDbIshOcGE9bFrqbdfi6DXBiI0MwCgRaZVoRIcdMbayGvsy81SDUTQfhCcfYuNDj+KfP04luQ3NN4agBNCqQ2ilEmJhASmQI3LkAcTKDNXYWWR3/iZqaLyx/Y0sr0wYibPebvRvWI+iYlUGmtuanVXZU4QLmolz7PhJKqEglUgYqVKhHLewiAYXCM4tEp2cxZcpkDl3AG24yNiRBygNRvAfLCJWqlTknWy+5zsE0wucvvAKqpEJkgcfR/UNkNqyneDcNJmxEYILGaKnBUQ2kNu8FTk7zfDew0w8cBhfPk052k9+eIhKJEZuJExhYCe50QlAJpCJMPHwffQ98z0K41chlYZRff2o0RihhWfZ+dW/ZmHnxRQGN1IefQXhTLj+/MwEuOJXGrblYhnkQpbAwikqoTz+eYHhpx5BrFTID49x6vLr8JUH6s89OnmULXfdTmh2EkFT0RDQRAlEGSU0hJSfppwYw5edQyxnUUIDFAYGECtlsmMRsuMjqLJMeUggn9iCkshT1ZYcBJ2szWY4yGis4mKboRvQ5iv9dbIMMLU0TsfuPkXpmSo7fq2Av8uG3k7zJ3QDPzfk2A3JdRuUvlO4jbXYFEbJ4UOwXUKzWeYyD0pOyy1O0D+gVs4ZRSXYMhC9V6LdikzbaZidyHKv0SrFs/6MplNbahsNg7iV17zZyUeoFNj2nb8nNllLf6oJIv58lkpsJ1X5EkILX0YCFLEfpBAqfZT9l6CIwyiZHFLOj7hYq6tdktuuldgNMYYaie7keB1urcSdWJPdWo8bZDAGWC2bu9EzNsAlme3lMny3j9efqNEB0AzL0IZL9RgjUURSIeT8PNVwiIpwjLP//X+gCQJTl17A2E+fRJX8pC/7NNLJmuBey6QAUJeS4ChTxwn1BYlwD3LmReJP/HfKQ6+lPLiTanw3xQEfSkCzbD9mEqyTX+N2sxXaOnrKMkluJbeoY8kSaexb5FyWxJEX2fDgjwimZ6gEI/iKOTLjmwjNnkZQVQRVRZVlpEoFJRAgOzqBGuhDUBKEjuTRKBBYPECxfwxBiOPL/AQ1OMpi327i0yBWphCqSaT5w/iKZUrjN1CNbSSz+TL8k/mGS1QFKMgpCrEKojJNcD5LMFsicfIEUvabiOVFSvEB/PkC4KPQ/0EUzkZZetSaP4pSSCL4NQaf+RGCVqXiewIlshsCAsWhGH5/ASUQJf7ijwjNHiBx9csYndmDVDiJb/4xpOIUmgCl5AigIVSr5DZfQ6l/kL5DDzLw3D+Q2nEWlUgUJTLPhvueYerytyEHXkUgE0RKZUEMIKRr9yZE5/EVnqTQtwvVN4JUPo6cn0aNDxGfOkby5BSKnEfOzyDm70NUyqAJIEB2bIJiX4jI7GlKySjpia3MXPja+vNq6bBrev+tJE/GcV3/ez5Q+66GtoQJnJrjqf++gPw7gwib/GiKhiAJK8Kjeo0znhzPqUlSSqugVe7RqVOcDivi1so6DM6E2Mug6FTW7AhgHrq9Lps6ldf3lZWSJYG2I+Ju4pt60kw7vVeTFKMbZNkppbcVKQaTdcsidbOd008+UawTq7O/+D/xZ07XywiaihIYRymPE8p8GYBs5NdQpVFITADLgz1UagQz4ENJBDqSQrQDI+m1I7p2xFjf55UggzWZ189jrM+JJDtZj7str9Ahlrunj3YV9aDNulrV66Vfq5O+JQv6dP5CCKeYZgtAk0W5wVoMy9+VqhA/fpjQXJXxh7+FL79YK7/rRgAETWNgf4ZycjtUxhD3zVA+/BiVhUPL9zK/D3//LgASsaMgQ6G0FSG+FeH0PiLzjyAoC2i+OOFLdpB47LOogTHUYATVn0DKziEoGTQpihIbI3AqgeqLIy+EQRDRfDEKAzE0UUbOn0YJ9uEvWvdjy4S51icYnfbAIvxbcAF/Kkf46AKRqTLh2Tyh+ZNEJveT3bCZwtAmVF+QYv8oWmCc0sBW8mdvRxN9aIJEcKEKUhAxKyIIArL+vejni4OwFC6+Eqz1MerpaRRAWTgOgNR3HWJlGOaAg9MIPEl5aZ8O4/MG8PVtWzr2UuBSpHgIsbJAITmI6hury1q0TAohlqRy/7eJjqUIFO+p1yHIeQT/LJrgJ3R6CkQfgdNfWd6vVvDP/Qw1OEx+26+CMoomRRHSueXrWABZi1Lc/kp8C08SWTyAOJsGLU7mnA8RqV6BNFdCSKVr956aNtz3RqpMADngEDUX6D6YryImL1xqXFABhJEkWiKCoGRA05BzzxPJl1CTffQ//zf0P3+M+FQSRcqS2jLGzAUjaJKFttAKxsmmi0g3Zivz4kAfvAn8V+UIDpQIPzrHxP99nP3/eAPTYe+GrlbGMeM4n1Rb+8B0ijOeHM9Xk8xWlh/USgQm7zRrk20WMxeEuBPvWltP2m4v1VqgHjvSQv/oxYteh5E8dytHPSyn2qz/9kiUnVJ6m5N3tEraYZVwwArGpdX0Wa8gfOoZskNBRp7+GQC5LR8jtu93a3/zChR5O1BGUAsgLsfJlTbupJoIUImLkKptsyKJ3YSZEOu/7YiusYyb+rp1fW5JcjtwklR0LYyUDTxbodusx9y/OJVvlXlOt7IvR2xYrqsYSzX1MWK5Svj0AiMPHiJyehFfLot/MY0SHkQNjqBGLiAXvQL//G2EDj4BQKHvJsoTr6u1wyVyoxO18vy+2v/pA5TTBwDIHtbPdhJ4sHaPiR34QiqCOEdu4zZmnpohOCAiR/oQhVOUtQCqGkQUs8jhQ/h8KoKWQ9BKCCIIao64lgW1ioBCNbqN6ev+JwjL+uWmiV3SRzgdbGpTwXSQ2OH7iE7tIzKVIrgwiybIFPtGUUMjII+ghi9g8dKP1VLDp0tUQst2A99BvT516R9omVP1+pXUsu+GYiK4OioLh+rPToc+udBh3K8/2wYcXv7Tn9jRdLxOnqFGQqW+jZQC51Px7UETwmhCtEaglyxEQiwJQGniF0ErgxikHDhEQe2HPKgnpwGbb/B4LdS7mhwGLkGfGkuLAAfrhNjYbvz9u5oIv/G6jc9O6tsIqWnEzDDa0rVWkleiJAKUA7PEngclkCS4WKAUC7PxgXs561uneOaXP0I1HEEJBKkG5xAUBcIZ5Hk/xb4EmrxM+/yLGcRKheJAv/3KjkMIwGAoTXkgQrkcIT8apvT+IGl1gHR2oKlsq+g2XqBWu2cQtcMZT47NcEOU3BDobhEuN9mazKTIiE5DzfSqLq8Qy2rt3ixCDVl6IztopaE1eXYLuzB30EiUO80UaBV1wo4UA5bOdmYLZFk6gX/xFKH5PMGTfopjV1AZfyvp8bcys3EWUYswtPc+NClMkcsI8RMi/BDSP6xVkIZ04n+CICJVjxCc+TG5xO/hS6rIR+fq52lFMnUi2w4xNRNic73GupxIsd11eb0eq+uyuhZojm7RjvV4tYhxKzLbLdLspr5W/VLfvkeJTu4nkMmT2XoN2S1XNHwbOlmuPy9NI/78FKHn0vS9cJzw1ALVcILcxiupjL4ardxHpRRFk5MgCAipLMrx/RQWzqOycIhqOoSceB4eq2WMNJJhL6iR59rf1fxOMpOHyUwebiq3TPIEIApEDSRPq5E8ThHPfpXo0efJbjkHsF7x0NugUaYTnZMYefhf8WVnSO+4nuroNrKDQ5BZikajAmUQyiBlC0DBsJoEaso6so+ZBJut6sbn4PSM2oVxclLH0uP1J3bUNxkJtJE820EZDFM5vPyerMis62uc39dwjbb3a7punUQbr1ek1kIkIESM9J5/Q0kua9WLG9/B0AMfY9fXv4BULiBVapGJNFFEUDVEpUJ+cITUzgFUIUqxP8nEjx8juDDH7NkXMHXZtaS2h+vWd6BhzG6Z2EROUL3YPkJLp3Gx7VJu9wovCXLsdVm+18RXh11jsApK7oYUt8qq1G5qWrvzmdHtwdpTTM4lmJd/nLJ/OcEuMLoR9UxC0BDexg2c9MRuYqw6xU2VM9NETjzO0OP/gRIYRw1sQM4+S+XAbcxe/afEjjxN32OzhE7dVztAClPZ+D6U+XORcs8icwofR6n6txJL/0/KgSup+M5D8W8Gbckq5MKKq+8z/m/e3g2Y63IiunbnbZe468faWaytCDJYRxuwIshOMY7tdKP6t9KUIc8DjBEaVgPGezNHiDC2/ZI0yaZ7v8DMedcTOfkIkZN7Kb34HPOX/BqaFLCccAw8NsOW7/6M1I4ryex4GZkLLiGQqT0nWSeUuSxkJoFlKUBl4RDZw9+r/Zg/2OU7toeR5BlJXY0YCYgsEOMbqEIE7XiQyIkak9JJq5gcrltAWWqLcm6O5Om9xI/sJXryMNXETvJn/TGBQgj0iV421VCPDiPpdSKGbgnwasCWkFqQZzMqF19C7uBjjnX2Cvo5yukD9WtsIMlLlmQdcjoJQCQdBQQyF/79cmWaAkLN9l9MqkjZUwQnv4OgRPFVy0SfPc3i5gs59IaXMfDcg+z8xi1I5RKFgWEqYQkl1I9UKpIb3UB+eJz80CjleLLxgk3EucHQ5SKOtjkwwFpIIKPjjCfHi9U40DwQ9SI7kyuy64QW+uF2CHG7ZY1wS6rtAs93A1bPw1bq4SJcTSvCbPcuzSHujLNVI1m2TVLSQk8M9o5B0ByBgsxPSLz4Q8RKkdlL3kUuPsTEz77AwL5nanWNvxqlcilS4EX8pbsYePQfCcw/iiKPUfbtoey/HHXv4/UBr7JQIrjtbUhxP0HhAeTyYYKlH6GKSYripQiLRShVukpu3aKVZEK3ZhlT/jgRXav62pFa6Md4OdbOimyXJc2OJFt9cyd3XwrUNMf+dAxf4SCFgT5Un8/19YH99yUVS0RPaqjBHXUrUvJEhtEf/W8CmTkOvunDZDfubjhGTz5j/N8OgqIglA+D1k8gl6AUzdB/cgyA/qPhZdlK3I9Q7qca2UJ0IcHi7r9DXJgmMPd1Rr//ITQpAIJGObmZqpQls/FsCv0BNt5/J8de8Ua0vjfUw6NJ6dKy9T+TQjEseZute2sFopghlEgTYC8CClntdajpmfp+ZeE4Ut8IvtQPkLUSWiBAdSFG/ESZ8PEfUklsp5q8ktLYm1BC25FO5dAyJ9BYJsRORNgsgahvX4PPyiuc7kEtbV8T96hPmvyJHQ369mXd9ZLsIjkMS32jnEo21KH3WZE0KIkNKCM3EwCKQyr52ieHnyKZczaTOftdSMVF/KlJxHIeX3aGcixKdPoofQd+TGjmOCevfDlTl70M1dcoa7BLo22EuS8rx1KWSU+MsCLL85V+/FUrp9Tu4ownx9lynMWS0LrgEtohslbQQyr5M0ncql9aaelWC8Zzd2J97jY8pUbV0UaKVDOsYrwaw9qYibIVzFkL7RIP2FmK9fibffseIjT1NAB9zz+Mv/QGhIE/JLPjAKFD/0jk8L8B/1Y/ThWjVP3bENQ8pcGb0OQk2kUDSD+G4qH78PVtQ1k4jrIASt/l+HkYAF/hpwhahapyXcN19EprbIVWhLxuHbM4xo7Aer1+NxE67OInW+2zg1VMW3nxGBv2fZ3Fs28isyFJLp6lKk6x++v/in8xw753/zHB0rLVSKr4GDpwlK3f/zyTV7+N9PaLGHrqh+RGtxI7/jyRky9w+PW/SSWcQyqV8OWzBOdmCCwuoPkG6Nv/UxRfgIWdl5HdsJNi/zi+fJpzvvBXABQGNqAG+hGrZQSlgj+bQgPGf/Yo5UOLgED42D0ImsKRt/wZ4UyUQjTA6MNfR8wdJLfjXaS2bSd+8H6kYobU2a9Fk3xsvu1P8GVOkN9yE6k9byY4n0TKHce38CRy5gCoC8j5LIKSA02hKm2kkt6JcuIwClBcOAdJ3ISGgICKnEohRPYwMPUMfg6RH7yZob3noyUlhNRCg0PUWibDOvz9u4htmyDKd+rbNHxUl3KCKQvHCQX2IfVJyDwA/hgV4RKEskLkWAbKRXKh30BVx2FeJ8JPGCbI1vIHI9by8/l5g9marL8zM1nWIfUZcscdr60sQCNxjh6t/a8lo0RTBcPRAWBbvd/05UAZDpDeqZJPHWR47+cYe+wJ5i7/A2IHH0DVjpDZchWpTcMEUlMEilFSYwWq4TCKz4/IOJFMvOH6lg0BRofR2rWpNI7zxbC1rDJa1lhe8+kNBE3TenuGHmFhYYG7776bv94xzolQ6wGp3SXEXul0V5MMu0UrouzFgvzOwjxfDXXXmu/aidDF8o5bGL3hjdIdnShbkWJow9lOrbLhh/9MaOrRpl0ZfgGRNGJQJli8G8HQSaT5VWJ8HUUaBkEmf/Z/q2sqjYOjbolIDB0hHFuenef7buLaS7fyoxc21Le1klS4lTkYyxmPbddK3S3ibkVodbmE8X+n8lawshz7FvaT2PtPlMf+E8qpGAg+ItnPIpJGk0JI5WlUMUIm9kcgCPiF5wgtfLF+/OLuT3L7/zeMKIr82bde4NvFOIkX76UUEYideIHYiX2UY/2IlRJSMYfqD6L4gmhyGDUQoxIZoBIfRSqkyY/tATRiRx4hNL0PqZhBEyXym15FfuMrkIrzCNV8zUKrqfhzIkpolMDMg4jlBYRKBiWyhdDxr1MavApBqyLlT6D6+1FlCMw9ixJIghACQUAT/VQT5yJUFpFyU6i+AHL+FEJlHs03SDV6DlVhK2pZRs0WIL4BZbGEsnACwNIhzojaMrQGCGuO3L35l17Ld77wA1dl/YkdRLa/mlBfESlQxld5BlFdIMWH688iEf0Rsm+OqryLwsgH0XK19mmMimCEFSFea89orcDLu1pN2OmqjbDSWOvk2dxOGkg1NVKtpqZrjtqbB+ohBkM/u4VA+X5ENUPZfymiMo2ozKFKA2iKghgUEapZNLEAqkq5bzO57e+i0re91pfQvHJm5hqWGQUNmCiU+N0DJ7nhhhvo62s/Tb0TznjLsS8XI5Ya6aiOM4GorjTcWJBdxdLsIRw1y0aYdVCqBqKAWKki54qUE5ElxxSttmYvWK9E2OWrNzr1daIrrl9iOgiaiqCU0UQfqpQEDQS1jKimifGtWsEiVILng19GLM9R9Z2FmklQDl6NUDyMn4MI1QxaMlbXqekdot6ZFqovI8T3EFBQpAGU0HnAssa2lWyhFezKGLe3S5C7EUnDSHR1MhtMifXt5v89Q9MYufuDSKUs1fBG5Hzt+Yu5byKqKQra5cicoDj2Wmb3XMzEXX+BqOaIZf6idrxQk0poAEKI8KG/5d3vPsrb3/52QtMFtv74LpTwBpTIZpTI5WR3vR5VjqAG+lH9AyCH6sReSGXRolF887UIDJElqanKW8kFQCscQ5mbRFS24zt8csniFK9b7DVAzEKFG8APWikFGVCHxhGyJ0AQKRXGqRYmAB/l8fciqDmUwR0ICwvIhaeQstMgB1DEjajCIKXAdajhYRB8qAuNS/2VQ3fVH6PZwmlF7F5aZE9GoZ9Q+Q40QSbDL6IsnKCycJDwUBRJyjCfegPylmshV2opk1jL+uB1tAfzezT+1omzUY6hw0pHbi5nJNXK8f1ImWGCe7Zz/Lxpth55A1pxM8Lso5TL50Dy7UBtYrb4s8/i799F+OIPwcQG5nfO4z/9Y4af+ifk/ALFga2o4c30VXKcuurdqMHGsVDnHa2Mkr5S3nF/N3Dmk+NCmEje3eDYrVz3awG9iJtqhFuphVvLuqRmCCjuyrZDuJ00yzu/di/De3/WsK8w0Id/MYdUMUd9hhPXnc+RN1/ZtN0uYHqrONVmCYUdGtqnIJLf9YeUFjIknv1122N8xaehCMXkjZS0CxHjRcTFg2gEKfsvJrr/98js/r8IsSRGlZbUtxFl4Thh3z0IS5E2JWUOTYwDvQ3fZgUrq7JbWJV3Q+6h0aHOOrmCM4wW5QYreSZFNBImtPhvVOMXI6QeQ84fpxrZRb7voyAI+HIP4y8/RVm7hlLf24m/oLAY/yMErYS29LYEymiZWaqpeUSxgCBU+JP/9hbuvfdegqNXsHj2p0EKwvETqNM6QXqhfh3mMFJGtJIXRLe+EVgeKI2OQQ1/L10paFQWjlGev6t2rkO1YysL32tYCvb1bWuwXlUWHrJc3v95JHF65ApfX4yo8FWKgVehLRygWp4hEXscYiCJixTE65G3XGtJimHdUvzzBieibHTusytv3OZP7CB7+HvLBDt9oNYXPA5bLr4RJTWNlpygsnAa39bdsHGC3GYfsJW+jTtRjtcSUHFikn5hAngrmQvegVDJIC/uI/bC/wagf2+I/M4PAMt8xmwwMsfr1uGrunwwHeCMJ8d9U0mqWjPpbWegs4Ob1LDm83WaTtYJvSbGZnixrHdDs2wOXu8JmgpC7V0kX8wRP34YsVKmHE6gyT4ETcWfmSc0Z+8VO3Hf00zc9zTVgJ+9v/EGchuH6pbls//tTqInZjn8xssp7o4gHa0SOzZNqS/K6Ut3QrFGlJ0IccPzVBUQl4O2G9uRlC6BViG38cNIxWMI6WkIJAlkmpf8gqmvIclPIlcPUfXtohS4gYD2CKrcDzrRWiLINc1xTX+ZCwyjKjKRRE0WIoTSELB27HKK+GCXNMRocXWbVKQTomw+rp1rNsLqO44crdFBjteWuOXKfoK528jwdqoLC/j6YijHv4TgH4e+KWR/CEFUqETPJb/5PyMs1o4vq2dTls9GTU0jnpyrO4lBs4RAh79/F9deey3XXnstn/j0o6jPPVe7F0M8VaiR0oXHP9VwrFdyVI/cYIw+ZvO3cfBtCLFlilxWTh9o2raOGmpyitcDEOQnFFJhJN+PCIQyhIPPo6oRitJVlNkNyGBqL7WJSHO7WSfF6/DSBowaZx16X9DUHy0cIpi6jsjxYdg4QXXzAFLyYtS9jyMmBlD3Pk7x0H0kx9OEgsv9UyXcz9yF7yDUwgBsxz1CquL6ftrFGU+OzfBCit0QWDsiarTymVOAdhMrTYRbQcrPEz3+BIJapRruQ4tcSm6wdv9OxNhX9hMp25Nsu2O9Rsi45JN/hlip8LP/8nE23/tdYpNHbcsqviAgoPojVMNJqqE+VDlAJaAQP3EIxR/lok9+i8ymy2uaSclH/FCtvrO+/ihSOUc5OoxYKSCXMow+UiIzNkolkiA881PKsX4Wdl2OEmjM5pOLZwksTLHhga8Rm9xPNZig0n8O5eFfgOoIqAUEtYo4P0l0+hMNx2plH9nw+5DyzyJQQPT5oLKIEAwhaFUUaTuiNks092kq8UvJj/8BwmKpwSlJt9pJfRvR0s8Rji+HrYq9+EcI2z4KLC+rWRFUo1XWSXpgJVVwgpmsthvj2KoOK1jJKowwfn/hdLBGjI+fQE1NUzxUC5UnSiXkcZlo8G6E0RJS6RRshIrsQ6meQzlyHoo0CvFNMDljGS1Ar8tozfUndthYhP6wdm0v/oDZe2y0kStMQNcJWGcwJrSIhn9KgGMoSglf2MexR2epFhTk6Hb8/XPAg7bWfGi9IrCOdbiF0Xps/F9H/NIP1v8WUln8hx9GTWxH2r2d+Au/VduxDao0Sl/nrvofhPJ9de4UTIU9caegUDBmTO8JXhLk2IoQG/PU20GPCOCFgFpJM7pJiFeaDOv3byc5MZOD5FO3ET5+X0OZct/FFJNBSmOvZeihP0UTJErDF6BKARBkqpERIlv9jO+9k9zWN1Dp2wFqFd/iUYTc0yxufxmRdAQh8ziaIFIYnEANhDyFh9Lx4lvfQ2TqBJoocuAtN3HRP/6FbVmpUmT65X+JWEqjCFMEUicQS1kkLU5p9GrEcoaifyeiNEwl6kNQK+THqkiFWSrJ7WR3/AJqMIlYmCNy7F400Ufy2AnE6jEqiS3Epg4x/tC3UGU/ldgwpViMaiRBOdbPwHMP4s/Mo0phChP/Fd/ik8Sf+iPQNISl0ISqGEMVI4hqLW2pvtQeKXwFzRdDFeJogQBqYHPtf18STU7WUtSW+tCyOdTnXwQal12Nf1dTUwiJ5Vl4yX8lmhLqWig3/RvUv8dWsIsWYUVwneIP2xHiVmQYGtu8WMqSODZJ/Pl/p7jpPaiTMSqHHyN38I56mf6zzybgm0ZTfSxuvIbg4i4qwrUopTCVw48h0Y+YTEJ2scFhqlXkhHWC8/MBIzGuLBwiX5IoVLJoVY3pfSkq+dr3WZ80mbKsGduisdw61tEr6Ksc2rVX1cMkCtVZotl/RMv5ELRKQ/ny4NsohWWCp7+NVDqOGkg21WnH41YLLwlyrD/AZmJpP7XQy5rJn1f0isy2Iq1e4DQJqD+HaIa+57+PnJ3Bl50hv+FCUrtuYDmBKPhPfhuhkiez9fXI1SCh47fWti88jn8BtCM/BUDQFHyp00ilyfqxvv4rCZ/8CXJORQ1uRE7/DLl4pHZ8TqXqKxA/+ABitYJYLbD/F3+XUrSP8R9/g74XHyE/tIX5XWcxfVGzFtiIxS07WNyyFDw9GmPyqlcw9tMHEKtVVMlHYeRskAYojVyM6otQjW9aOvI88oYQxXps1Co1EiYQBREqg6+imsqCAuF9GrAAiGjcAEBVWnpkRSgHoHROEEFZRCydRPIV8OXniMzPUO1/NcXxyyEfghKUypdS3nA9aBVAILTwZVQpiRobQy1HEQJlNEFGC4yilgbqwd1hKf6vAkvSYdQT04jJWoBBs4OOrj2ta0DDjctTxZ0fAmF/wzZjtixYDqlmp+k1E2GnxBjG/UbYRZBouC6b8G12Fura+Ruvwer71/+XcqcZ+eF/ru+v/OybZE+F68+umj/E4FkT+Hw1K7CgVUgc+BGaECKvXYCYXF4xMFuKje9Ch/539vD36Lv4t+pJKawsyOt46aDBkap/F6V8EngZ5fl9CD7wJxp1o+sa4nWsFJwm7vG+jUhH55DzT6CFA6jJEJyq9YOloWspDr0HYbGMls1BFQQtSXb7eagRleBiTbpXj2lu02evpFzVjJcEOdZhljoYrcdm6UP/0bCjNauVxbnXMJJ3LwS5ToTjBSJzKqXAHP75Av6UipyJEppdIHT4a4il04CCGhpFEwfQxCL+1F40QUbQqoROv0D8+f0ghhGkAkJpEbEyiajm0RDRpCSq1A9alRrhkNDEIKAiKFmE0jyqEEbQVBB0S2gCxABCbhZN6KcaDqCpAUKnDoCmUpj4GOHJf4Jqgc33fJmxn44RO1Yj3PHjzxM9dRil/3XkY1lUX6CuLYalJASZCJp6AlFRQFORKmVS289mfuceQimR4SfuIn6sFjO4mIxTGDyfcmSRUK4xDqOO+gdr/nBtrJvGJAN1ZGpe/gpJhNgWVEAbWnIW80epB8lORmuZq4TaJ5nf/ltN5FNIZaEMCM2E1ZjlSg/BIyaHGxyf9OXXQCjN2KXNYq8iFzY/g+P7G+oz3p8QSyKkskhgmSUO3HVmrcro9ZmtvvYrQ2pb36h+HildquuJF/gwWvoAIe4iP3+Y7OEj+BPbiU2MkhwVqIb3oKj9FNmNoOao+s5C6bscTtQs9v7zX1V7h8dPIMSStfew9RJyB+8gsv315A7eUSc+dY0vNOiF10nQSx92y9ZWZdaxjpWEMaU1sORQOwGohE/+FZIyBXOl+nhcDQ0xd8WHa/1p//I4qQcc1QLL/bjep7v166qPLb2J3taAM54cBzIiQZtsKct6FtFxeyfa45WAfu7o4YeIH9tHZscvoISH8KcOEjp9AkEpgFpGiWylf+ExQGRk/mnE8iyaGEaTwyAGESpp0FSKgRuoKHGE+DCSMoMoVdFKJSqRi1BzOQRKgIpQKCJQQWMAjQ2oXEI1PY+UmECdXwpJlRhHTZ9ESgyhpicRYyMsxUMDTaSankVMbKZQypNWr6wZWg3QCZcQS0IJKrHLUYILKImNCHKU3LY9RA79a61stci2r364dqAgUA33ofrDoGmI1RJyfh5VDqCJPhAEVDkIgoCgVpEKaUr9m1FFGVGtMrD32wzs/TaL21/G9JW/1mSpd/Wx0uhspi/xWyWrgObwZVAjvNXNA/XjzeUbYu0a9gl6HUukS2/hxpSyOqFVU9NLJFkj2JcjyhP1erILIywef57cTBE4if9n+6n80g0s3Pp/gMYO0UyS69diIMhO1uF2Z/12x81vzluusLTzrYbTQeQjs6CVITONtngSgRIiMkKiD5VXEfafIDSyE790AF/gMAUupZi/qv481NR0bfKSexFl4TjFQ/fVrcP6xCR38A7KS8S3vE6A17GOdZxR0BhI3lqX/mmKSHbHxxHTMygD28nuWB4bdAOG80qeexjLD2bEdc1xL9Hq5bRLiFtFbHCK/hBOB0FVkAqzBOaeQ9NUAgv7UYIDRA5/H1EpEDz5UwS1giomUYNb0IQQmiARmH4EqXgMVR4iH3wrSmgLQry/gYg1OGaRpLywCNjrH82Bxu0yKhnL6Fgu+2MqWy+heOi5pvJ6WCed3JVjr6kl6THIBBYnLkHrq1l3lUQANI1SNIdUmEWs6pZsP0pokEAuZh2nWCniW3gCpR80KYgqRxCAamQT/UdrX5nuFNBOpBOdxDo5kDWlMs7UMo5Jz6Tq28zkU1pSRUhM1ImwfmwteBYN7xSqkH4G//goAscRCwKiP4NYPoGkzaBpEpogUMwlmDtYRKlkKKeX23k5fYBKenuzJesw+A/uMFkPalZpMTmMQE1Nok8YzN9WOxFfrI4zTmjNMggdTvp5oVpCzs0jF1L4FqeIHz+FnDmBUJpDrKZqodPwo4lBNPwIWhVNCqBUw2jJDaCVUYXLKJdOkU+NoGnH65kHzeGz9GdVnt/XYBVexzrWsY4zBWbZTyZ3GaJYwO87id83i3zgVsrJdyDkfESfqlm/qpsH6se0O6Ya0XT8CjDXM54cl2IqRW15ALWTURj36TBrcY1/6wQ3shglF8/W/295PSanMadMaPogHkyJoJYJTt6OkC8SmrnV+Rz+V1DxX4wmNmoapY3vbFpu10mUHnvQqEE1DupuPJydwgOZ9XDmcmppO+X5WlQEI4E2ByQXaZYIAJBdRIgl69bT8KIPGGu8wBTUTHeN0OUJWm5njUwmo6DViKWUBViWR0QMIYyNzmFWYb+syG4dNtbjpnLUrOY6wRWTw5A6QICnoaZ4BjQEdRRlsYAgjAMaWrmKJk4gyGW0fBoxOIev7zEkZlGJouYSqMRRS2HK+QqldJT86UXUKoAPKFNONweDd4JViC6jM1Fw23VIyWGEWJIgtc6xHVJsfM6RtLn08nan6Bd6HyCWZpEXnsY/9xxSYT9icbqWUltOoAoDKP4NlLRLUKoKKlGqC3NIfZtAtUqokNN/UZ4/CZysPxer6BDr1uB1rGMdLxUs92e7CG57JZX0c/gTd6JuvxhlbHu9zzWPk8GUWF/lM27T4WSEtFrJzSeK+NRibXjsIc54cjy99SSTgZTtfqsoB8ZtufhyNIT58VP1MuVYCn8mWS9XimbqSSX8mWR9v15Whz+TxL+YYuP936cS2YCkJvCnT+IrVEH0IVb9qIEBqrGdaOpYLWTXwjSBmf/ApzY6QgGoQgxQELVljahOjI3L5rBMgKE5KHz9WBMZ1Ykx0BQo3A5O1mUzLMNRmZaara7XGHLMiE7j3nqpQzaUk5Y0qEbi7ph+0yRv0P+uX4MFedb3i8wTpJa0pJSPUS5FkIp5RKGMsDAFCAhUEScfR6uWAAmlpJGv7CA3Mw7acsdTnq9Z63tF1IyEWXce8/fvIrjtOgCiG3fW5SGtiLJdQg0zdOu8MYEHShk5ewApdxQ5dYKoWkDOPY9QzVGVtlCVt1EsXo/CEFRlqBqd456t3ctSu8wd/D6wvFLSjWe37lS3jnWs40xHfUVxfh/xnVcBEDzxNY5fNsLixgj9J2sGq/B8kcSztyCIo5QHLiM2qaJJAdRAnHxfFViWw4XTQWY21uLs63xL52jz49bXESrl4ZT1vm7hjCfHlUiGcqjZWqjDKq2wkeSaibNOenUCrMOcgc3427xv4oGHSB6eppIcQSydQlX7UcsgUEUtLCD5FwkeuwNBy1CzlWr1LGVVJYosLRMDUctQlbagCXmkqk7MalMmI/GCRrJmlz/dKte6E4zldWJtl8ddR1Og8MQORIvQLcYYnWZYEWOzxVWHncbX7fFWMD5XO1hdo47myYlqiDM8QSB1J0LxBKrmg9AEGrVlfA0fKgkW0jcQSswhy88RC2fQtCnQhFoaaUEjnXkZleqwaSn/qYYzrgYZ08myTipj215HYGCIwOgwgbEQqFXKsSBqaIBqZAxfaRgEGlIcg7t3rSQCiPmT+BaeIHTsm2hiHIVhVGkYpShR5HVoiXNAEBsmKJXDjwHLWQKD265DTA7jO/xY/Z1mD38Pf/+uGkFeJ7XrWMc61gEsR04pzs6xGH8PvsEMZ936aYob30l650YQZfqe+Sqh4/cAED7ypYbj0+fdgpBaIGQw5m0x+G1IG3cCoGVA2bOd+c01w+D8+Kk6J6vI7hJKdYIznhxv+8cK/bFddWtSbrOvwWQvH52junmgybtd/zufKNJ/NMz85jxDx2uxvPRtxv3631YyDKM8Qj46R6AQRSpOoU2paASR2Y+oziOIVQQUFuevJFO5oX6NlYWDBAZGkaV5fPICVSlClTFUEigMgyKiLBxD6hsBRFgsArVrsLMQ222HZlLnRPKM9dkRWatUpVawItVmgqxft35NlhILE7yQXjOcSLCV9dps/TWe25hkw7f1kob6+/gkAHNpP9XsNvo2vmBwKHi++eQJ0DQBVZSplIMIYgBRzCMKFaqVAMX5eVQla/G8NZTCYfwRmcREBF9QYvbAoqtn0U1ExiYY3u0nGN2LJiZRxA2oi31UgzKRoznE8lwtWQZQCV1Ise/dIEj152kOfVbXN7MkNInJhA9+Cf/0A1Sj51Hofz9KcBflp+9ZuoIYUEJituGd2U0mdcKsO9EBljphfZUksv31LDz+KVdpWZ22r2Md61jHmQR/YgeBgR3Iya2cvuevGXzVJ/Ar9xA58FlCR29FqCygKlI9CqxKiAJXo2QzVKsDqPd/2zKsKJhCFqYPwDeWzztiiJoxNBKAX2nNWzrBGU+OU4/+HQuLiYZtwtKSfe7gHfXle2HhEMK264gsWY5UIHr+q+qzl6Fnatt9Wy8hGktSfvq7hPo2ElmyzkaSw8Srx5CzjzAszaEJfqo5hWTfpRRP5lC1ECqQWzhEDhCE8xHK+/CFF+jfWAGDrFIrTKLksmiaiCCogEzmxfuXrvV8KicP4esLAxWgFiu4snCYysLhBscf/e9W1mAr8mtHNM3QyYRuZdPRihCbibAU7AOsl8qN96PDygruxprrFVaTCGMGOSMaImsswfi3MTWzHQa2lIEXACgXwyiKD0mq4PMXEEStXm52cifVSgBJqiBKVQRBQ4xspjg3i6YZ0k0PbUVSX8QfVvGFVAJRFVEcbTjnSpJjf2IHA1tKRAf3U+BKMvEbILG8NqZlUhSOPEYwUCAarlkE/LmHKEyJVKojTbIfHebUyJG+kwR5mGLgZajyHiqbz0KcUkyTEgUxEQdNsW275t/GtmwnFzImYdA783Xyu451rOPnAZGxCQY3PA08jX/PJgLcCVrNkitWZgCQpGVB8MLJccrFebKHv0d06xvrq4r+xI6GmN3+xI6GvlT/29wPZw9/j0h1CLipp/d5xpNjX3QjLC4P/sZMQ8a/fX3bmohL7r5/bqrPmILTOKsRRI1NFxcAWDyRp1pUQBQIZJ8nmfRTLakUFkrkZ/OUMo3ZYbJTPsLJAMGkD19QJpI4Qaz/OIK4HFghNzBIpTyHUlxE9ssoiy+gKj5qdrLm6zP+beclbyxnjC7gFmYrm/HZ2MFJctHK8mwmQMb3tpKw0zvXn4eL52KM6gCw0Peb+DmIWNqPLC3gk1NIfg2/2OxxpqhBon1TCHKU7FyIcmEpDnNhAX06Xp7fR3JLP4nBE5SLYSqlMP5wClFUUSog1WKsk50uEIj5qOSrqIrWdK5uIzi0lejgs5QC11AOvbG2sSGaRg2V6hCF4g40zYcQihOL/oTFzLVgmPzZOYCKxwQGr69p24KlB6D0AOE50DSWHA5BlAUEQUNLAQhk/ZdSVRJomkho28WATOHwT/Alt1NZqHnT+fq21dudPqk2T9yMGn2ra+s1npj78Yqdax3rWMc6dIQGt7HhvCKCWDMoVopVqoUM2myW4oJEfnqS2DkfROrbxOy9f0hkZBvBievJnrwfOFWP5W4kwfUYyhak2IoY6/t9UevcBN3EmU+OE1vwF0uurajQ2uppF5D91NNBgn1+/CGZYMKPLyihaRpKWcUflvGHZRIbIhz9yTTl3PLMqbRYYcNFA0hyzXxczlc4/MBSVi0BQn0BgokMwbiP5FhjGKpsaojM/IQl6bWCuYzx/pyeT0OsVgOsiKmb6zCfRwyGgebEE3pZJ2ueebuRqLSLVu3ELSF3ew3FQw+j9G0DLiZ38A5Cw2cTTU5TLVSQQgki8bl6WUksIoWLwCIMjDF/YrkjMJI3KayhKFPIvjyyL4+mwslngwTDM8Q3xJD9EB0OER0OAXDyqXlys82ewdHhIGPn9aNUVCSfSGw0hCAKaGprMh1M+BFECPaN4w+pRIaXiG1pHEKNZRt08PHzKXIeUCHEI4hClWB8kexCLcqFUxYwVdGYfHIOySeiqRqSX8QXlFk8lUOObGLDeSX0kPO1yadGLPLTpnqSWwGeMgSUX4r/3Afz6deiGvwHO2lr61jHOtZxJiM0tI2J82tjR2p6E4XswFIfHYfZecrpmoEhd/AuIttfjz++g9zUAYTQ2YC1ZVjfboaRJBth5B0+h0hF3cIZT45hmei0o4n1YvXJzhTJzjSSC8kvIgckRElA0zRURWsgxjomH58jMREmOhhEM1jwNA3y8yXy8yVGzkk2HZc+8gL5uUZHKytyajc5sHsmZuuoG8mCV2c+L7By/DP/7bTNK+zqcLpH8zFOFvQ6uTMtIel/F6aPUpgG8JPYFAEDOTbC75smOjJKqbyVSuoglYWDlOf3I4gaiwWNfEBEq6QopMpU8rV2lwPmDmVAgIlLBgklamn4xi/ob77OfBV/uNYNTD4xR3Q4iHSlSHggsJQYxBrxjVvp3xZGkkuoioymSSjyRvJsRWEYMd6HXHkecpOIFBCjAYTSPIKWR4jNIKhlRIogaAgCqKrIzNPPU5z7ru05jcjPlRDE2sTSHxhGEzUSm5L4InFKDCKSQVBSSFIeARUNCZUYGjI167uIVqkg+HxLET9q36RaSqOqCUrzU6CJdd2xvhy4jnWsYx0/L5CDEluvGUH3cQIIDccoZO2NZMZxUrcUg/cVNjuSvFI448mxHNuApDYvgS+HabKXAnheDhXAH5ERRQFVrVmM9X92iI2E8IUlQEApqyxOFRBEgeHdCQRRWPoHgijgj/iaju/bFCU/V6pfr1Gno8OogTQTOCey58Y66lWK0c45zLAjyl7RCZkxW+rd1uXkkGU3Ey5koZAdaLhv/bkLmaeJRX5CNPQkQp+GpoKyQUHyiVSKCtWSglIOMHhWnEpBQa2oCCIUUmXK2SpzBxfxhSXCfUH8kdrqhgZU8lUCUV+dGCsVldFz+5CDEoV02ZEY+xM7CPeV8Mm1gO/ikgTaxwIBbS8goi1ApaChagkUVSZ38GDtW6ks/SurqEt/myEHJcJ9fiSfhD8q44/I+CJhNEVCqfrRNBFJLiP5SiiVAOVsGcGXpJJPU86mKKVTKNVaZI9S1r/0t4C/f2td96b7JDQ5gABwAni24ZrWk3isYx3r+HlDtagwP7UVAVCLkwxsKRPgWeAiSx6SPfw9yukDNWOCi5To9WNNfbAVoTaOyXJsQ6e31hJnPjnecDZiJtywzWgFNS5D6zDH9XVLkhPjYYZ3Jy33nXpmnuzpYo2ARGRyM0WkgMToHusk4KlJH9UySOExpPAIlcws1co4YnAUZXEODQk0kVL6NNGtyzpTKzg5xOUO3tG0zQpmEu1Eiu2c+aysz/UoA4EyUt9A03476KTaK1F2Q2IbMv44LPN0S0vakGXQlKfeDsuTij6mj+6kurgPtVojokrhCNWy2rACIcoCgaivprUVBUJJP9GREL6QhD8sI4jL2nUBCESXJ2InnphDKSkIokClUOWcjY2aeTPK6QNMPQ3Tco2AapqGpqEbX+snEUUBQTqJIAqoFdVZ8yxAZCBAbCxMpD9EMSOhSUk0eYJcKoeS8uNPjqIVDiMICtVqAKXiR1X8S++8APibdGvm64bmjHXrznTrWMc6fl5hRUx1fyt//y6k3TfWCqamqUrfRlYOM7htlur4BqrBc5h/7E7KqYO10JdL/a5uMfbat5rHxwYpxRIXkPo2IsbLwEwnt90SZzw5FsIxBGINaXtFGgmcOeav/pB14hjdWnMcMjtUmYlWIatx+MdHCPRtRhQ1BAm0yhS+kEy1sgF/QiA2eJr+LbWseEcfmebFe07iT2xHKRzGF5TYeNlQ7TpFDUU8i3ImCJkqkIR8Hl8VYJlEatqs4zUZ0WAFs7KUm6yhxsZmhttoFm6PEQLziMnmZX0zjNExWsEVWW6V9W+ViFF9gtaCJNfuUUCt1sitqkClUIuJbbZEKxooS7w2u3/5vkbOSRIfa5xAGjG4PVaTBskCR3/i3OFIARFfQCIQ9xOIyEh+EQSQAxFEqYwcDCCICoJQkwxpCmiCjChW0dRlK/Pk00FEGeSASiA5SDh6CqVcJp8OM7lXRlUEaoT3xdrzanNJzuu+daxjHev4eYJlOLWlGO++vm3kH/9azQH8hj8mH/tdqnu/Q9j3EMHIFAIvErsEquUR0nOLhHddSHF+G4sH7qzV7UCQG5zwbBygdRj5gJgcRozlWSfHbUCIJetkGSAoPItv8ccom3aiygOgaWilMj75YlQ1iJDYjkawSbdr/l1NvUhiKEClHEKt+lEUmXJxN2VNRF6KJldSd3HqsIo/mAUpSKBPQJQ0fMEt+CMq2RmF6JBCYqyKqjzP8ScaSYs5tEl9u43nphHGVL5mWC3Z62iHCBvhKgmHP9dUzio+sflajFkAjddtlGsYU2AbsVZIkNO7M5JkJ7LvxgmyobzBGjD9QhqkOIKWQfKLqFUNtarWCXMwXtMkVwrVBms0QCDuI9wXIDTQRzDhQ6CMWvVR1YbREueiiVFKR34K8fNh5ByKBQ1lMQuIiMkxKkceX7ovDX//OGH5IULRFGPnVlAqKtWSgFI5zdxRP8XFIHp0lrXy7taxjnWs46UAq5XyumHQ1N/WV9YOL2/TSXIwPEnwnMYVcfn/b+/O46Oq7v6Bf+5MZsu+EpIwhLBEwiohYdEiIJtR4YcPhacqAvZpbYHKi0r7s62tCP7ASl2eijxaqajgUhfA9rGFIiCxCkgDSZQ9K5B93yaZzHZ+f2RmnMkCczHJJJPP+/XKC+bce8893zm5d75zc+65aiWCo+og+QsEB19DeHQcTM2tEIoItDTNhrn+CsxGCeqw0e5tcvlc6+yeqfZJcW/q98mxaG4EEPTtk7VckmLA/hSt0hr4Wa/Ar/GK+8ZBjv8cbvsnDLAOGgpINYCwAbABgxQQiiZYRQRsMXHQGb+GDnXOKowYCwtGtO0bKtgaK6CQjFCrjYiIaRu3aRN+EEIFqzUcrY1AbbkGFrMWFpMO6nD3qdoc2idD17sJz/H/67nR1eEbJbjt31e3ZdfZrqvHAIvGOuc+u3qIh+MqcvsHQrRPIh1jR/uq9t+eO/szVmfjt4COV5g7my6us7L2rw2ObjDAuU3FxTyExPkjKrHtm11TtQ4hQ4dCFzUY+u9Nh0pZAgBobQ6GqdUfzSUBsJjaEti2mwt3uezpxI3fiAKgxrVtnXxh6Mv96G2cxo2IblZn51Y591I41jXVAw2lLYicsw3G/GNoKvwHIIDAhEnwT14KCAuaz2+BAuUI1QsEDqkAhrTVYWhphRB+EEIDq6EcNs1oSCGjYa11PM+h7bNdO3xml88M6K0kud8nx5J/ENDsnry1T5SNMfejVbcQIjQQ1mA/mLXVUDZXwv9aOSRLHSRFI6TWK9BUn4PSfLXT/fg5/rwbdgdsqnAojdegaKmEn2SEn3QBgARJtAIRgE0RDGEbDItNC6P2Tlj9EgDYk7xwR33fvvlyblpzHSKiSpjs8VXgzpJf1/fsRpN2SXVNHRLkzspcl3XYX7skuP3T0Fw55g9uP22b69CQ60331Zd0Nfa1q9dy17nR2K7OkmgAUAWNQHMDcPXUFYQnBEGpbIZaFwRJ1KLp6jcw1Bhhauo480p36ev91pcwMSaivqTqyP91e+2aaDeVtF1ybihpxqCJqQiItMFYZwGkSigUVigUFqgDBVTaz6DAIYhQBWw2LWxhoRBmG6xNb8KmjYUiZixsdQ0QUMOGYghoIdn828bs9bB+nxxrq3bBv64JuGaBgA7w0wGSEjbNIKDCAggzAAmQ/CBaw4AKFWC0QkhqiMBg2NRRsAUNh1BMhSlMgdYoE0zqekBSIKhSByEpEZCzA8rWtjvz1bWfo27cw/BrjYUpWA2VUQeh1EEotW0/irZ/oVBD2WByttMaogEwAsr61g7JuwLJXV5hbc+xjbIuESI0EAr9ELfl1hANlPUdnztu6aJcDtc2uo7xdmufIwFu92hlmFoBBLit2/7BEI6n8HU2lVv7KdGcCd91bq4bKLq6ibD9mK72Y8u+TZqB6iuO4R+RMKboUHvVs99HIiIioPMr0RXZ/0Zgwj2d3qSnDhkBSeEHSQK0UXrohtwCqCxoLT4MpfLf0CoMUEdFApIRisZqwFIPRYMZAYoQAHf2aCz9PjmuPncJNfUaqIL0UCgt8AsMhNVQDqWqCoCAQhsJhS4YkkYJqcUAtDbD1lgKCTb4GYIAjQKoM0FhqAeszQjMMwGwQvipAKEAoITCUue2z9Czb8hqo1CoUXvbHgCAIV4FxLeN19HWfftMaWO8+zRubstC26a7CrhihjVEA2OoDdqQCOcybZ3CuY62Dt+u00n59bRPnttfGe4qIQa+TYrbD5VwXBUWrSbY6tquQrYfHuEYHtJ+3LBrMuc6LYzrnMGuBmJifD2u75knV6cdZbbWET3eNiIiGhhMNZe6uHglobW2rcxYkweLyXEP1liYqi6h8nLbcL226TdvB2B/SmlgCTCvZ9vc75NjY4MZjUUlUIfYpzurPw7AdUqQWGiHTwVaAeOFdOc3G3XISASn3AdFQNswhLqv/p99fr67oQ6PgzJ0MBTBERANFbDVFwOSDcrgKCgCgwAhIOm0kIQJaKkDhAkKtQKSMAIqK6A0QjI3QF3b9sQtq7rtirE1ROOW9LomowEdnyLstsyRkCoBBF5pgmisg3XcCARmt13R1tpnuJC+OAHF+GTnOoEuNyc6kuPOriA7lrldHW6sg4S2pNh67TKUSGzbb10FcK1tCIe54HRbnfarvu05EmBLkh4NGYc7ja+zRLerBBhgEkwDC4dUEFF/dr35/125PnSpQ7nLzYEB+qjubWAn+n1y7ODJVcT2c5sa89OdA78d6zcV/AMocJ/5of3VS0fHtX/s8bdTo7U9MtGAmd8mjKV/da6nDNN3OisD0HH8cWf1G/IOQB1+C7R1M2HIT3dri6nmEpABBKc8goaM1wAAASPSOp3NwZXrjVza4TNhdKnXuc/8W5wPT3DU67gZzvEEsa4OAnP9AllJLRNgIibGROS7uhoK2ReeRuozyXFXuvqTMmC/YtnVgzXs23U2/Un7aU46m1u4s9kJuip3TUy7uns0MOEe1J7Z7tzO0XbnmNIa918y18HyJvt219NpfIDbtzVTfa77flxmieATxIiIiMhTnl5R9gafT46vx5MO6I51bvQLcKPtO5vqy1vjbXviCXJE5I5XjImIvGdAJ8f9BW88IxoYmBQTEXmf4sarEBFRT2NiTETUN/DKMRGRFzAZJiLqm5gcExH1MCbCRET9hyRELzyHrwfU1tbi8OHDKDxbBKPBdOMNyKsiBoehuqzW280gD7CvPPOzJx7x6v7fe/1D9lM/wWOq/2Bf9X3aADWGjRuCuXPnIiwsrEf20W+T4+bmZhw8eBBWq9XbTSEiIiKiXqJUKnHXXXfB39//xivfhH6bHANtCXJra8envRERERGRb9JoND2WGAP9PDkmIiIiIupOnMqNiIiIiMiOyTERERERkR2TYyIiIiIiOybHRERERER2feohIA0NDTh16hQuX76MNWvWOMsrKirw8ccfo6ioCKGhoUhLS0NSUpJzeWZmJj799FPU19dj8ODBuO+++zBkyBAAwJUrV/DOO+8gODgYq1atQmBgYK/H1Z911Sf5+fk4cOAAysvLERwcjJkzZyI1NdW5XE6f+Pv746mnnsKyZcswbtw4AIDNZsPTTz+N+fPnY/r06c56t2zZgjlz5mDatGm99A70X83NzTh8+DDOnTuHmJgYrFq1CgBQXFyM/fv3o7S0FMHBwZg7dy4mT54MAGhqasKbb76J+vp6LF++HPHx8di1axciIyOxaNEiZ91vvfUW/Pz88OCDDzrL3nvvPQgh8MADD/RqnAOFwWDAvn37cPnyZahUKiQnJ+Puu++GJEn44IMPcOHCBcyfPx+33Xabt5s6IHTX8VVTU4Nt27Z1uo+nn34aKpWqt0LyCb3xmRUYGIiMjAx89NFHHfav1+uxdu3ang/Ux/S1/K/PJMf79u3D6dOnodVq3U4GVqsVe/bsQVxcHB599FGcO3cO77zzDn7+858jIiICZWVl+Oijj7Bo0SIkJCTgiy++wO7du/HLX/4SKpUKBw4cwH/8x3/g6tWrOH78OObPn+/FKPuXrvqkqqoKu3btwty5c/Gf//mfKCgowL59+xAWFoaRI0feVJ/ExcWhqKjImRyXlpaipaUFBQUFzuS4vr4ejY2NGDp0qFfej/7EbDZj586dUKlUWLp0KWJiYgAAFosFu3fvxujRo7Fs2TLk5eVh7969iImJQWxsLI4fP45Ro0Zh2LBhOHDgAH76059Cr9cjJyfHWbfNZkNBQQH8/NxPH0VFRW5fZKh7ffzxx2hsbMTq1avR1NSE999/H2FhYYiOjnaWv/HGG0hNTWVC1cO68/hyWLt2LUJDQ932w36Upzc/swAgJCQEP/vZz9zaoFQqezVmX9AX878+M6wiMDAQa9asQVpamlt5dXU1Kisrcc899yAqKgqzZs1CaGio88M6OzsbCQkJmDp1KgYNGoR7770XLS0tyM/PB9B2EgsODkZgYCAsFkuvx9WfddUnWVlZiI2NxaxZsxAZGYnU1FSMHDkSFy9eBHBzfaLX61FUVOTcR15eHiIiIlBQUOAsKyoqglqtxuDBg3s69H7v+PHjMBgM+NGPfoSRI0ciICAAAFBQUICWlhbce++9GDRoEKZPn474+HhkZ2cDaOubwMBAhISEuPVNaWmp84E7JSUlkCQJVqsVFRUVAICWlhbU1NTwi0sPMZlMOH/+PO6++27ExsYiMTER06ZNQ1ZWFsxmM3Q6HYKCgqBQKPhgpF7QnceXQ0BAAIKCgtx+SJ7e/MwCAEmSOvRZT86966v6Yv7XZ5Jjx9XD9sxmMwD3b9AqlcpZXl5ejujoaOcytVqNiIgIVFZWAgDmzJmDV155Benp6Zg6dWpPhuBzuuqTpKQkLFy40K3Mz8/P+Y35ZvpEr9ejuLjYuU1+fj5SU1NhMpmc2xUXFyMuLg4KRZ/5te2zsrOzcfvtt0OtVruVl5eXIyIiwu14iomJcb7H06ZNw+eff46XX34Zd955J4C2vjGbzSgvLwfQ1jfx8fHQ6/XOLy/FxcVQKBSIjY3tjfAGnKqqKlitVrcvho5+S0xMhMFgwObNmzFhwgRotVovtnRg6M7ji7pPb35mUffpi/lfnxlW0ZWYmBhERETg2LFjmDdvHi5duoTKykqMGTMGANDa2trhw0Cn08FoNAIAxowZg40bN0KhUDCp6ibtf4nr6+uRm5vrPNnfTJ8MHToULS0tqKqqQnh4OAoKCjBv3jwUFBQgPz8fUVFRKCoq4pVJD1itVpSVlSExMRHbt29HU1MTEhMTsXDhwi77pqysDAAQERGBxx9/HDabzTlsIiAgABERESgqKkJsbCzy8vIwcuRI2Gw25OfnY+rUqc5l7YdaUPdwPAnUte90Oh1aW1uhVCrxyCOPwGQydUjWqPt19/FFPa8nPrOo53kz/+vzR6dCocCyZcvwpz/9Cenp6RBCYOHChYiIiADQNv6xM64P/uNJqOfYbDZ8+OGHSEpKgl6vd5Z15np9EhISguDgYBQXF6OlpQVKpRKxsbEYPnw4CgoKMHXqVBQXF2PKlCk9F4yPaG5uhs1mw/nz57Fw4UIoFArs27cPn3zySZd/qnXtm85OJI4r+1arFYWFhbjrrrtgsVjw5ZdfAmi7cuzof+p+nhxTTIx7R08cXwDw4osvQpIk5+uFCxe63TBG3aO7PrOAtiT7ySefdCt79NFHERUV1Y0tHri8mf/1+azRaDTi/fffx4wZMzBx4kRcu3YNBw4cQHx8PIYMGQJJktDZE7D57a53/P3vf0dtba3brAU32yd6vR7Xrl1DbW0tRowYAUmSMGLECHz55Zeora2FwWDglWMPOMacLl261HnyT0tLw1/+8hfMmjWr07650U0ker0eZ86cQXFxMVQqFQYPHgwhBFpbW1FVVYWioiIsWLCg+4MhAHAmTTabze044nmu9/XE8QUADz/8MIKDg52vObNSz+jOz6ygoCA88sgjbmVhYWHd01Dyav7X58+s2dnZkCQJaWlpiI2NxdSpUzF27Fh88cUXAACNRoOWlha3bVpaWqDT6bzR3AHl1KlTOH36NFauXOn2ft9snwwdOhTFxcXIz8/HyJEjAQCxsbEwmUz45ptvnFeX6foCAgIgSZLbOK1BgwbBYrHAz8+v07650TjVoUOHoqysDDk5Oc4vLgqFAgkJCTh37hxqa2t55bgHaTQaAHDrO0/6jbpfTxxfABAaGorIyEjnD/u2+3X3Z5ZCoXDrs8jISM5W0Y28mf/1+eTYccJxpVarnXceRkdHu93IZTKZUF1d7TZIm7pffn4+/vd//xcPPPAABg0a5LbsZvtEr9ejqqoK165dw4gRIwDAmYCdPn2aV409pFKpEBkZiatXrzrLampq4Ofnh9jYWFRXVzvHZAFt0+bdqG9iYmIgSRKys7OdfQMAI0aMwJkzZ5zjkqlnREZGQqFQuB1XnvQbdb+eOL6o5/XEZxb1LG/mf30+OU5MTERVVRXS09NRXV2Nc+fO4cyZM84B2RMnTkRRURG++OILVFZW4pNPPkFAQACGDRvm3Yb7sOrqarz99tuYMWMGYmJi0NjY6PwBbr5PhgwZAoPBAI1Gg8jISGf58OHDUV5eziuTMsyYMQOffvopcnJyUFRUhIMHD2Ly5MkYPnw4/P398be//Q0VFRU4efIkrl69iokTJ163PscHf0VFhfOqPsC+6S0ajQZjxozBwYMHUVxcjJycHJw8eRK33nqrt5s2IHX38QW0PeTF9Vza2NjY5ZhKkqenPrOEEB36zGAw9EJEA4M3878+P+Y4KioKK1aswKFDh3D48GEEBQVh7ty5SE5OBtB2RWvp0qU4dOgQDhw4gJiYGKxYsYJ/2uhBmZmZaG5uxtGjR3H06FG3Zb///e9vuk/UajWio6M73FnsuFLJK8eemzJlChoaGvCXv/wFADB27Fjcfffd8PPzw4oVK/Dxxx/jj3/8I0JCQrB06dIOV1I6o9fr0dTUhPDwcGdZTEwMdDod+6YX3Hfffdi7dy9effVVqFQqpKam8gZVL+mJ42vHjh0dyp544gnOd9wNeuozq76+Hlu2bHEri4qKwoYNG7o9hoHIm/mfJDobzUxERERENAD1+WEVRERERES9hckxEREREZEdk2MiIiIiIjsmx0REREREdkyOiYiIiIjsmBwTEREREdkxOSYiIiIismNyTERERERkx+SYiIiIiMiOyTERERERkR2TYyIiIiIiOybHRERERER2TI6JiIiIiOyYHBMRERER2TE5JiIiIiKyY3JMRERERGTH5JiIiIiIyI7JMRERERGRHZNjIiIiIiI7JsdERERERHZMjomIiIiI7JgcExERERHZMTkmIiIiIrJjckxEREREZMfkmIiIiIjIjskxEREREZEdk2MiIiIiIjsmx0REREREdkyOiYiIiIjs/LzdAOpbhBCora1FaWkpLBaLt5tDRETUL2k0GsTGxiIoKAiSJHm7OSSDJIQQ3m4EeZfNZkNlZSVe2LwdiROHIzgsCC0GI5qbzB7XISk18naqVMlaXZKzvkLmdz6FzD+gKGWsr5B3QrQpPV9XKOQdujalTdb6QuH5+kJhlVU3JHnrSzLaopBZt1Lm+n4y1veDzLplrK+GvC+vCpmneqWM1RU2eb/nMrqzbX0ZbZFk1i2z+wGbjMZYZX68WmUcczLrFnLaLbN+uXXbZPaRTUb9cuuW20UmGb8vyohIKLVaGEtKMHH2bMTFxSE8PJyJcj/A5HiAslgsKCsrw2sv7sLI8QnQBWhRX92AS1l5uJydh2u5JVAFjfC4PnX4LbL2rwobLmt9ZZje43UVoYNk1S0FhcpaX4QGeryuNUTelwZjqOdn9uYQo6y6DcFNstZvDWz0eF1TUJ2suuEvb32trt7jdYM1tbLqDlfVyFp/kKrK43UHKz1fFwAGS563ZaSlQlbdISZ5yXRIq+cfDSHN8r5gBhrkre8vo36tvF9zqBpkfpmq9/yiAWpaZdVtrfH8mLbWyavbVCvvfNFaZ/J43ZYGeb9bhkZ56zc3e35ebJCxLgDUt8hLgXLqPE9s66BG0LhxCElJQXDyZKhCQmCurcUtkycjLi4OgwYNgkLuxRnqFRxWMYC0traipKQE777+ARKShkKl9kP0kCicPpaNS1l5KL9W6e0mEhER+QRhNqMhMxMNmZmA9GcEJCYiJCUVF8wm5A+KhrW5GQ2ZmUjbsAGDBw+GSiXvL6rUc5gc+ziDwYDi4mL87cO/Qz8yFhIk6AK0SP/bceRk56O20vMrckRERHQThIDh0iUYLl1CyTtvQ6sfipDUVISkpODkyZOwmc1oPHsWd/7oR4iNjYVWq/V2iwc0Jsc+RgiB+vp6lJSU4MiBzzB46CBYzBaYW804+O5R5GQXwNDY7O1mEhERDVjGa1dhvHYV5fv2Qh0VhZDJKQhJTUXGv/8NADBcvozbli1DXFwcAgM9H8pH3YPJsQ8QQqCqqgolJSX46stTCIsKhbGlFdVltTjxzwzknS2EqVXGODkiIiLqFabKSlQePIDKgwegDApCSPJkhKSkICsjA19//TVarl5F8oIFiIuLQ2hoKG/o6wVMjvuxiooK/HHLDoyaMBwBwf5oqjeg4MI1HHzvM1y9XAyrRe5t2EREROQt1sZG1KQfQ036MSg0GgRNmIiQ1FSczcjAhQsXYKqsxNjbbkN8fDzCwsK83VyfxeS4H3v79fdw6/fGwWa14fjBDHx54BTMvEJMRETU79laW1H/71Oo//cp+AUHI+b++xExcxZycnLw1euv4yfbt3u7iT6LyXE/9sPVK3Ht2jX86+iXuO2uFKTeORH556/gUmYecr8pgLFZ3lQ/RERE1DeowsIQPDkFoampCExKguTnB0NeLqb+n8XQp6V5u3k+jclxPxYaGorQ0FCMHz8ejY2NKC4uRnlRFRY9vAA2qw1Xc4pxOSsPl7Lz0Fgrc/JPIiIi6lWa2FiEpLTNYhEwciSExYKmCxeQnJqK2NhY+Pv7e7uJAwKTYx8RFBSE0aNHY/To0WhpaUFJSQkKLl7FnO/PwPwfzELplfK2RDkrD1Wl8h56QERERD1AkuA/fHhbQpyaAm1sHKxGIxqzszHlgQcQExMDtVrt7VYOOEyOfZBOp8OIESPwq6d+CbPZjNLSUryRsQfTFqRg5v+5DTUVdbiUlYvLWfkoLigF+IxEIiKiXiEplQhIGoPQ1FQET54MdXg4LI2NGDl+POLi4hAdHQ3lQw95u5kDGpNjH6dSqTB06FBsfPYJWK1WVFRUYPszr2DCtDGYPj8FTfUG5GTn41JWHq5cLuIMF0RERN1ModEgaOLEtivEkyZBGRDQNvPE1KmIi4tDREQEHyXdhzA5HkCUSiViYmKw9aXNEEKguroaxcXFMJvMmHTHeLS2tCL3bCEuZ+Uh79wVbzeXiIio31IGBSFkctucxUHjxkOhVqPlyhWMT01FXFwcQkJCOGdxHyUJIfhH9QFOCIGGhgYUFxfj8D+OIiY+GlaLFa2tnl9FliSlvJ3K/IYsq365JxtJ5rd1GdULhdy2yKhbknfoyl0fMtbvyboBQJIx9keSbD1WNwAoZNSvkFs3PK9bKbNuSeapXs5vrvzul3dcyKq/Z38VATnvo9y6bXLqlnn892CcQt4hJ7stsrq/h98Xq4z1FQFtT7YzXLqE23/wA8TGxvJpd/0Ek2PqwGAwoKysDGYz50wmIiK6GRqNBjExMdBqtd5uCsnE5JiIiIiIyI6jv4mIiIiI7JgcExERERHZMTkmIiIiIrJjckxuZs2ahWHDhrmVvfPOOxgxYgQ0Gg1SUlKQkZHhncZ1gy+//BJTp05FYGAgJkyYgL///e9uy30l1vT0dEybNg2BgYFITEzErl273Jb7SpwAkJ2djUceeQRr167tsMyX4nSorq7GkiVLEBAQgIiICGzYsAE2m8zpAvqo0tJSbNq0CbfddptbeWZmJqZMmQKNRoOEhATs3r3bSy3sHleuXMHixYsRHh6OuLg4PPbYY2htbQXgW7EWFRXhvvvuQ2hoKIYNG4bf//73zmW+FKerjRs3QpIkFBYWAvDdOH2eILLbs2ePkCRJxMfHO8u+/vproVKpxKuvviouXLggfvzjH4vY2FjR0tLivYbepGvXromgoCCxdetWkZubK3bs2CG0Wq3Iz88XQvhOrDk5OUKr1YpnnnlG5OTkiNdff10oFApx5MgRIYTvxFlbWyu+973vCaVSKcLCwsTKlSvdlvtKnO0tXbpUTJ8+XWRlZYmDBw+KyMhI8cc//tHbzfrOHnnkEaFSqURUVJTbOai1tVUMGTJE/PSnPxUXLlwQO3bsEEqlUmRlZXmvsd+ByWQSSUlJYtmyZeLcuXPiyJEjYvDgweLJJ5/0qVitVquYPHmyWLJkibh48aL45z//KcLDw8W7777rU3G6ys3NFTqdTgAQBQUFPhvnQMDkmIQQQtTX14uYmBixYsUKtw+m3/zmN2LOnDnO101NTUKn04kDBw54oZXfzauvvipuueUWt7KkpCTxpz/9SQjhO7Fu2rRJTJ8+3a1s/vz54rHHHhNC+E6cZWVl4ne/+524du2aWLlyZYfk2FfidNXU1CRUKpX417/+5SzbuHGjmDp1qhdb1T1++9vfitOnT4s33njD7Rx06NAhERAQ4PalZubMmeLxxx/3Qiu/u/T0dKFSqURTU5OzbOvWrSI5OdmnYr148aIAIEpLS51lq1evFvfff79Pxenq7rvvFitXrnQmx74a50DAYRUEAHjyySdxxx13YPbs2W7l58+fx/jx452vAwICMHLkSFy8eLG3m/id/fCHP0RmZqZbmUqlgsFgAOA7sd5777347//+b7cyrVYLlUoFwHfijI6OxubNmzFkyJBOl/tKnK5yc3NhNpvd4powYUK/jsnh6aefRnJycofy8+fPY9SoUW5zxfbnmBMSErB//34EBAQ4yxzHpy/FOmrUKDQ2NmLw4MHOMsf51pfidPj4449x/vx5/OpXv3KW+WKcAwWTY8I333yDN998E88//3yHZQ0NDQgJCXErCwsLQ0NDQ281r9uoVCrodDoAgM1mwzvvvIPCwkJ8//vfB+A7sSYnJ2PKlCnO18XFxTh8+DCWLFkCwHfivBFfjNPRdte4+ntMN+Jr/ajX63HPPfc4X9tsNuzZswdLlizxqVgVCoXb0+AyMzPx7rvv4uGHH/apOAGgpaUF69evx4svvuiWCPtanAMJk2PC2rVr8etf/xpxcXEdllmtHR8hLUlSv74BaP369dBqtVi+fDm2b98OvV4PwDdjtVqtWLlyJRYuXIjU1FRnWXv9Pc7O+GKcXcUkfPhZTr7Yj642b96Muro6/OxnP/PJWPfu3QudTofk5GQsWrQIixcv9rk4t2zZgqSkJCxevNit3NfiHEiYHA9AaWlpCAwMRGBgIDZv3ozy8nL8/Oc/73RdhULR4UAWQsDPz683mvqduca6detWAMATTzyBzMxMPP/881izZg2ys7MB9O9YO4sTAH7xi1+gsLAQr776qrPMF+PsTH+OsysKRdsp2zUuIQSUSqW3mtTjfLEfHfbv349t27bhgw8+gE6n88lYFyxYgKysLHz44Yf45JNPsHPnTp+KMycnBy+99BJeeumlDst8Kc6Bhj00AP35z39GS0sLAGDhwoUoKChAeHg4AMBiscBkMiEwMBAHDhxAUFAQamtr3bavra1FaGhobzf7prjG6ogxKioKUVFRGDt2LA4dOoS33noLL7zwQr+OtbM4//znP+ONN97AiRMn3GLwtTi70p/j7EpQUBCAtjgiIiKc/+/PMd2IL/Yj0DYF4YoVK7Bz506kpKQA8M1YAwMDccstt+CWW25Bbm4uXnnlFTz00EM+E+cLL7wAg8GASZMmAYDzrzhjx47Fb37zG5+Jc6BhcjwAuQ6fOHjwIMxms/P1vn378NJLL+HYsWOIi4vDmDFjkJ6e7lxuMBiQl5eHsWPH9mqbb5ZrrM8++yxyc3Oxc+dOZ5lSqYRGowGAfh1r+yEx6enpWLduHfbv34+kpCS3Zb4U5/X05zi7MmrUKPj5+eH06dOYP38+AODrr7/u1zHdyJgxY5CTk4OGhgYEBwcDaIt55syZXm7ZzSsvL8eiRYuwfv16PPjgg85yX4r12LFjWL9+PTIzMyFJEoBvz7e+FOemTZuwYcMG5+vi4mLMmjUL//jHP1BWVuYzcQ44Xpolg/qo9tMoZWdnC6VSKV588UVx8eJF8eMf/1jEx8cLk8nkvUbepGPHjgmVSiVef/11kZ+fL3bv3i00Go346quvhBC+E2teXp6IiIgQv/vd70RpaanbjxC+E6erzqZy88U4hRBiyZIlYtKkSeLMmTPi0KFDIjIy0jkdoS9ofw4yGo0iLi5OrFixQly4cEH8z//8j1CpVOL8+fPea+R3YDQaxbRp08TcuXNFSUmJ2/HpS7FWV1eLyMhIsW7dOpGTkyM+++wzMXToUPHcc8/5VJztFRQUOKdy8+U4fR2TY3LT/oNJiLaHgwwbNkyo1WqRmpoqMjMzvdK27vDee++JsWPHCp1OJ8aNGyc++ugjt+W+EOtTTz0lAHT64+ALcbrqLDkWwvfiFEKIyspKsXjxYqHT6URERIR4/PHHhc1m83azuk1n56CMjAyRkpIi1Gq1SEhIEO+++653GtcNjh071uXx+dlnn/lUrBkZGWLmzJkiMDBQDB06VGzatElYLBbnMl+J05VrciyE78bp6yQhfPg2ZyIiIiIiGThbBRERERGRHZNjIiIiIiI7JsdERERERHZMjomIiIiI7JgcExERERHZMTkmIiIiIrJjckxEREREZMfkmIiIiIjIjskxEREREZEdk2MiIupTdu3ahcOHD3dLXc899xxOnz7dLXUR0cDA5JiI+oQrV65g8eLFCA8PR1xcHB577DG0trZ6u1nUyw4dOoRt27bh1ltv7Zb65syZgyVLlqC8vLxb6iMi38fkmIi8zmw2Iy0tDRqNBl988QX27NmD9957D1u3bu3xfT/11FOYNWtWn63P23ozHrPZjDVr1uC1115DZGSks+yhhx5CaGgofvjDH8JqtbptU1ZWhkcffRTDhw+HRqOBXq/HwoULceTIEQDApEmTcP/99+PXv/51r8RARP0fk2Mi8roTJ04gNzcXu3btwpgxY3DnnXdi3bp1+OSTT7zdtA7JmK/ojbjk7mPv3r2Ijo7GHXfc4Sx766230NDQgFOnTqGiogK7d+92LissLMTkyZNx9OhR/OEPf8A333yDgwcPYvbs2Vi7dq1zvXXr1uHtt99GSUnJdw+KiHwek2Mi8rqEhATs378fAQEBzjKtVguVStXp+pIkOf/07u/vjzlz5uDq1atYvnw5AgICMHToULckymAw4L/+678QEhKCYcOGYcuWLbDZbBg2bBg2bdqE9PR0SJKEwsJCZ/1r1qzBkCFD8Pjjj3e5fXtd1Xe97eXGIkkSnnvuOYwfPx46nQ4LFixAWVnZDWPtLC4AuHjxIhYsWAB/f3/ExcXhmWeeuW48rnEBwKpVq7Bq1Sq39rXfh6fv3wcffIB7773XrezMmTP4yU9+gsTERKxZswYZGRnOZWvWrIEkSTh16hSWLFmCxMREjB07Fo899hhOnjzpXC8mJgYpKSnYu3dvh30SEXUgiIj6GKvVKiZNmiS2bdvW6XIAYvbs2eLs2bPixIkTQq/XC39/f/H888+LvLw8sXHjRqHVakVNTY0QQojFixeLtLQ0cfbsWXHs2DGh1+vFm2++KSoqKsSGDRvE9OnTRWlpqbBYLM76Z86cKc6ePStqa2u73L69ruq73vZyYwEgZsyYIbKzs8WJEyfEmDFjxH333edsw4325RqX1WoV8fHxYvXq1SIvL0/89a9/FRqNRhw6dKjTeHJzcwUAUVBQ4NzfypUrxcqVK936xnUfN2qTq+joaHH48GG3sm3btonVq1cLs9ksVq9eLV544QUhhBDV1dVCkiSxdevWTn9H2nv00UfF/fff79G6RDSwMTkmoj5n48aNIiEhQTQ3N3e6HIBbEvWLX/xCjB8/3vm6ublZABAnT54UOTk5QqlUisrKSufyP/zhD2LevHnOfc2cObND/Z9++qkQQtxw+87a7lrfjbaXE4tj/SNHjjiXHzlyRCgUClFTU+PRvhxxCSGE2WwWJ06cEAaDwVl22223iS1btnQaT0FBgUfJses+PH3/GhsbBQDx9ddfu5XX1dWJ5ORkoVAoxLRp00RTU5MQQoivvvpKABD79u0TnnjxxRdFSkqKR+sS0cDm19tXqomIrmf//v3Ytm0bPv/8c+h0ui7XUyqVzv8HBAQgPDzc+dqxXUtLC7KyspxDKBwsFgvi4+Ov2w4/v7bT481u7+DJ9p7G4qBQfDsibsqUKbDZbCgsLEReXt4N9+WIy/H/oKAgLF++HBkZGaitrUVzczPmzZvnUWxdcd2Hp+9ffX09ACAoKMitPCQkBKdPn0ZFRQUGDRrkLBdCyGpTUFCQcx9ERNfD5JiI+ozs7GysWLECO3fuREpKSrfVGxwc7DZWFUCX45n74vbXY7FYAHybLMrZl9FoxPz58zFv3jy8//77iIqKwoMPPtgt7XLlSZs0Gg0AdJnAuibGADBq1ChIkoSLFy961Aaj0Qi1Wu1pk4loAGNyTER9Qnl5ORYtWoT169d3a4KWlJSE+vp6WCwWjB49GkBbQulIKiVJ+k7bt9e+Prnbe8L1ZraMjAwoFAokJCRAo9HI2tf58+dRUlKC7du3O6/Yul71bR+PYx2DweAss1gsHbZx5Wn8oaGhUKlUHs9HHB4ejgULFmDHjh1Yt26d282cAFBXV4fQ0FDn66qqKkRHR3tUNxENbJytgoi8rrW1FYsXL3bOSFBWVub8MZlM36nusWPHIi0tDQ888ABOnDiBixcv4gc/+AGeeOIJAEBERATy8/ORlZUFo9Eoe/v22tcnd3tPPPXUU8jOzsZXX32F9evXY8mSJQgLC5O9L71eD41Gg9deew0FBQV49tlnceLECbf3wTUex+wZ27dvR2FhId5//33s37//um31tE1+fn4YN24cCgoKPH4fduzYAavViilTpmDv3r3IycnBhQsX8NJLL2H69Olu6168eBETJ070uG4iGriYHBOR1508eRInT57E4cOHERsbi5iYGOfP8ePHv3P9b7/9NpKSkrBgwQJMnToVOp0Ov/3tbwEAS5cuRXh4OGbMmNHlPLjX2769zuqTs70nbr/9dixfvhyzZs3CkCFD8PLLL99UW6OiorBnzx68/PLLmDx5Mi5duoRVq1a5DW1oH88bb7yBo0ePYsKECdi9ezfmz59/w/Z62qa77rpL1tzWw4cPx5kzZzB79mxs2LAB48aNw7x583DkyBG88sorbuseO3YMaWlpHtdNRAOXJOTe1UBERF4jSRI+++wzn3oKn0NOTg6Sk5NRVFSEkJCQbqv34MGDWLt2LS5fvux28yMRUWd45ZiIiPqEUaNG4aGHHsLmzZu7rU6LxYJNmzbhmWeeYWJMRB5hckxERH3Gc889h88//xyff/55t9S3efNm3HrrrVi2bFm31EdEvo/DKoiIqE9pamqC0WhEZGTkd66rsLDQbY5lIqIbYXJMRERERGTHYRVERERERHZMjomIiIiI7JgcExERERHZMTkmIiIiIrJjckxEREREZMfkmIiIiIjIjskxEREREZEdk2MiIiIiIjsmx0REREREdkyOiYiIiIjs/j9s3luSGsa9zgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "earthkit.plots.quickmap.plot(\n",
+    "    ds_sfc[\"2t\"].sel(time=\"2020-07-31T00:00:00\"),\n",
+    "    units=\"degC\", x=\"lon\", y=\"lat\",\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "658ac441-3d9d-46ab-9e5a-d4c5a159679b",
+   "metadata": {},
+   "source": [
+    "The wave model data is stored on a **reduced_ll** grid and can be loaded as in the following example.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d0fa5bad-4af2-4cb9-8f76-4814295c41e4",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "a936cb8e-b821-46a2-8fba-e1619e12f097",
-      "cell_type": "markdown",
-      "source": "The data can be visualized as demonstrated below. For example, the 10 metre wind speed on **2020-07-31 at 00:00:00 UTC** is plotted.",
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_wave.grib\n"
+     ]
     },
     {
-      "id": "44680327-cd31-4720-b075-55d1f031489a",
-      "cell_type": "code",
-      "source": "earthkit.plots.quickmap.plot(\n    ds_wave[\"wind\"].sel(time=\"2020-07-31T00:00:00\"),\n    x=\"lon\", y=\"lat\",\n);",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 700x600 with 2 Axes>",
-            "image/png": 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"
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-          "metadata": {}
-        }
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+       "body[data-theme=dark],\n",
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+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
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+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
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+       "\n",
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+       "  display: grid;\n",
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+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
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+       "\n",
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+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 958MB\n",
+       "Dimensions:  (time: 21, value: 315258)\n",
+       "Coordinates:\n",
+       "    lat      (value) float64 3MB ...\n",
+       "    lon      (value) float64 3MB ...\n",
+       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
+       "Dimensions without coordinates: value\n",
+       "Data variables: (12/18)\n",
+       "    cdww     (time, value) float64 53MB ...\n",
+       "    dwi      (time, value) float64 53MB ...\n",
+       "    hmax     (time, value) float64 53MB ...\n",
+       "    mp1      (time, value) float64 53MB ...\n",
+       "    mp2      (time, value) float64 53MB ...\n",
+       "    mwd      (time, value) float64 53MB ...\n",
+       "    ...       ...\n",
+       "    ust      (time, value) float64 53MB ...\n",
+       "    vst      (time, value) float64 53MB ...\n",
+       "    wdw      (time, value) float64 53MB ...\n",
+       "    wind     (time, value) float64 53MB ...\n",
+       "    wmb      (time, value) float64 53MB ...\n",
+       "    wstar    (time, value) float64 53MB ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-524328d0-a617-4af7-8a78-a1a6d7de09f9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-524328d0-a617-4af7-8a78-a1a6d7de09f9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span>value</span>: 315258</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d0cc7db7-f3ea-473e-96c7-647331ea5065' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0cc7db7-f3ea-473e-96c7-647331ea5065' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3e111003-570d-441b-8bea-b9187ea09e5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e111003-570d-441b-8bea-b9187ea09e5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb470e05-4412-41bb-a6b4-7055449851d8' class='xr-var-data-in' type='checkbox'><label for='data-eb470e05-4412-41bb-a6b4-7055449851d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[315258 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c5e2d12-94e6-433c-ae30-66db6a8c1c00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c5e2d12-94e6-433c-ae30-66db6a8c1c00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-287b7b41-5435-431a-bb95-5d1aa73ab40f' class='xr-var-data-in' type='checkbox'><label for='data-287b7b41-5435-431a-bb95-5d1aa73ab40f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[315258 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-b009a770-b4e8-4e08-8ae9-9480f04d3927' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b009a770-b4e8-4e08-8ae9-9480f04d3927' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a66cc098-28a2-4b1a-830a-a56ed32f4dd8' class='xr-var-data-in' type='checkbox'><label for='data-a66cc098-28a2-4b1a-830a-a56ed32f4dd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bc5b76a2-c23e-4eff-9840-8a574533c586' class='xr-section-summary-in' type='checkbox'  ><label for='section-bc5b76a2-c23e-4eff-9840-8a574533c586' class='xr-section-summary' >Data variables: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cdww</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d242acf-9ced-402f-b329-ea97b3bd7031' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d242acf-9ced-402f-b329-ea97b3bd7031' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f57571ae-bccc-4978-9a29-52eeb5fdb4ac' class='xr-var-data-in' type='checkbox'><label for='data-f57571ae-bccc-4978-9a29-52eeb5fdb4ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140233</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cdww</dd><dt><span>GRIB_units :</span></dt><dd>dimensionless</dd><dt><span>GRIB_name :</span></dt><dd>Coefficient of drag with waves</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cdww</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>dimensionless</dd><dt><span>long_name :</span></dt><dd>Coefficient of drag with waves</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dwi</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-56b95ac2-ad82-417e-b262-20cc13093254' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56b95ac2-ad82-417e-b262-20cc13093254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4290daba-ad32-4f6c-bf39-97a7c3b04cca' class='xr-var-data-in' type='checkbox'><label for='data-4290daba-ad32-4f6c-bf39-97a7c3b04cca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140249</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>dwi</dd><dt><span>GRIB_units :</span></dt><dd>degrees</dd><dt><span>GRIB_name :</span></dt><dd>10 metre wind direction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dwi</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>10 metre wind direction</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hmax</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42c0fec8-1fe7-41af-8a1f-7672af55726c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42c0fec8-1fe7-41af-8a1f-7672af55726c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abd21177-a004-4bf9-a123-c349a837d224' class='xr-var-data-in' type='checkbox'><label for='data-abd21177-a004-4bf9-a123-c349a837d224' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140218</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>hmax</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Envelop-maximum individual wave height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hmax</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Envelop-maximum individual wave height</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mp1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1dbc1637-de95-423e-894b-c83246ce9fbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dbc1637-de95-423e-894b-c83246ce9fbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72de101c-c18f-40b1-8a30-b7fdb18f178c' class='xr-var-data-in' type='checkbox'><label for='data-72de101c-c18f-40b1-8a30-b7fdb18f178c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140220</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mp1</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave period based on first moment</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mp1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean wave period based on first moment</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mp2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3addde0f-513d-40ce-b7e3-e6918536719e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3addde0f-513d-40ce-b7e3-e6918536719e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d10f4a1-d623-4c45-ab42-d8a2147e649e' class='xr-var-data-in' type='checkbox'><label for='data-2d10f4a1-d623-4c45-ab42-d8a2147e649e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140221</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mp2</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean zero-crossing wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mp2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean zero-crossing wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mwd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f78b9af8-c3a3-446c-afe6-9446c1679cac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f78b9af8-c3a3-446c-afe6-9446c1679cac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d6e5e3a-96b4-4fdf-ac5a-63eec1d0f8d5' class='xr-var-data-in' type='checkbox'><label for='data-5d6e5e3a-96b4-4fdf-ac5a-63eec1d0f8d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140230</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mwd</dd><dt><span>GRIB_units :</span></dt><dd>Degree true</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave direction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mwd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Degree true</dd><dt><span>long_name :</span></dt><dd>Mean wave direction</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mwp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ad7ef53-f1cd-4e39-b433-d5976e8035c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ad7ef53-f1cd-4e39-b433-d5976e8035c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0728d0b-e344-46a2-a60d-bdcce8c11829' class='xr-var-data-in' type='checkbox'><label for='data-a0728d0b-e344-46a2-a60d-bdcce8c11829' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140232</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mwp</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mwp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phioc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d0cae3f9-97c3-4b87-82c3-febe9a0783b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0cae3f9-97c3-4b87-82c3-febe9a0783b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c847303-7f93-4268-bce8-86c00d6a2f6c' class='xr-var-data-in' type='checkbox'><label for='data-4c847303-7f93-4268-bce8-86c00d6a2f6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140212</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>phioc</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Normalized energy flux into ocean</dd><dt><span>GRIB_cfVarName :</span></dt><dd>phioc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Normalized energy flux into ocean</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pp1d</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8714cf85-896b-439a-8cf7-6a8dc1cf27ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8714cf85-896b-439a-8cf7-6a8dc1cf27ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d27bc99-f450-4f40-851b-75ece611ee69' class='xr-var-data-in' type='checkbox'><label for='data-7d27bc99-f450-4f40-851b-75ece611ee69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140231</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>pp1d</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Peak wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>pp1d</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Peak wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rhoao</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f83918b-088a-439d-958f-71d4be20c9f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f83918b-088a-439d-958f-71d4be20c9f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fd31741-3556-4e85-976e-398f91139c74' class='xr-var-data-in' type='checkbox'><label for='data-1fd31741-3556-4e85-976e-398f91139c74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140209</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>rhoao</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Air density over the oceans</dd><dt><span>GRIB_cfVarName :</span></dt><dd>rhoao</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>kg m**-3</dd><dt><span>long_name :</span></dt><dd>Air density over the oceans</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swh</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fdb10a2d-7ba2-4902-bac6-7eb0eafce537' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdb10a2d-7ba2-4902-bac6-7eb0eafce537' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4b7d977-c481-4977-b43b-0127e836377a' class='xr-var-data-in' type='checkbox'><label for='data-f4b7d977-c481-4977-b43b-0127e836377a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140229</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swh</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Significant height of combined wind waves and swell</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swh</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Significant height of combined wind waves and swell</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tauoc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6ee11867-1f05-4f33-a0da-20c36b6a7356' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ee11867-1f05-4f33-a0da-20c36b6a7356' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e3c9faa-d934-4c6e-a8e3-5e7ccf5ac83f' class='xr-var-data-in' type='checkbox'><label for='data-8e3c9faa-d934-4c6e-a8e3-5e7ccf5ac83f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140214</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tauoc</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Normalized stress into ocean</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tauoc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Normalized stress into ocean</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ust</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9e7e01bb-79c9-4f88-a8af-4a43cdf5dc55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e7e01bb-79c9-4f88-a8af-4a43cdf5dc55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b65c38c-f98f-4205-bc78-d45eea6eb06d' class='xr-var-data-in' type='checkbox'><label for='data-2b65c38c-f98f-4205-bc78-d45eea6eb06d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140215</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ust</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>U-component surface stokes drift</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ust</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>U-component surface stokes drift</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vst</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c79f4a51-6b26-49f0-bac5-509a17ac2729' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c79f4a51-6b26-49f0-bac5-509a17ac2729' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-028a3f06-ee24-46fa-a7f8-5b999a702673' class='xr-var-data-in' type='checkbox'><label for='data-028a3f06-ee24-46fa-a7f8-5b999a702673' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140216</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vst</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>V-component surface stokes drift</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vst</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>V-component surface stokes drift</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wdw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8ebadb44-d4dd-4874-b0af-2e75e7e808fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ebadb44-d4dd-4874-b0af-2e75e7e808fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de32a6b3-c4be-4195-8117-247ae2144a78' class='xr-var-data-in' type='checkbox'><label for='data-de32a6b3-c4be-4195-8117-247ae2144a78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140222</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wdw</dd><dt><span>GRIB_units :</span></dt><dd>radians</dd><dt><span>GRIB_name :</span></dt><dd>Wave spectral directional width</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wdw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>radians</dd><dt><span>long_name :</span></dt><dd>Wave spectral directional width</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9723a926-ee06-4ebb-943a-1c5c99b4ea57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9723a926-ee06-4ebb-943a-1c5c99b4ea57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-881c6c65-2500-4d70-a50d-2a5155f33bbd' class='xr-var-data-in' type='checkbox'><label for='data-881c6c65-2500-4d70-a50d-2a5155f33bbd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140245</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wind</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre wind speed</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wind</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre wind speed</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wmb</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-73e0e9a2-05a6-43d9-b53d-c7ad6fbc8905' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73e0e9a2-05a6-43d9-b53d-c7ad6fbc8905' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcc43f9d-7078-46f4-9118-e36ed5ed4b52' class='xr-var-data-in' type='checkbox'><label for='data-bcc43f9d-7078-46f4-9118-e36ed5ed4b52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140219</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wmb</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Model bathymetry</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wmb</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Model bathymetry</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wstar</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32e4dbae-469b-473f-b416-ccbff9326798' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32e4dbae-469b-473f-b416-ccbff9326798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0c1dfcc-6e8f-4e1b-9afa-60a765d92541' class='xr-var-data-in' type='checkbox'><label for='data-f0c1dfcc-6e8f-4e1b-9afa-60a765d92541' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140208</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wstar</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Free convective velocity over the oceans</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wstar</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Free convective velocity over the oceans</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-01a44a1c-83d6-43bf-b801-f7d768d9e3e5' class='xr-section-summary-in' type='checkbox'  ><label for='section-01a44a1c-83d6-43bf-b801-f7d768d9e3e5' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-001f4653-f173-470a-b86b-840e919f30f6' class='xr-index-data-in' type='checkbox'/><label for='index-001f4653-f173-470a-b86b-840e919f30f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n",
+       "               &#x27;2020-07-31 00:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a94b7b8-a8b9-4380-99d6-1fe7190bce5d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5a94b7b8-a8b9-4380-99d6-1fe7190bce5d' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n",
+       "</dd></dl></div></li></ul></div></div>"
       ],
-      "execution_count": 14
-    },
+      "text/plain": [
+       "<xarray.Dataset> Size: 958MB\n",
+       "Dimensions:  (time: 21, value: 315258)\n",
+       "Coordinates:\n",
+       "    lat      (value) float64 3MB ...\n",
+       "    lon      (value) float64 3MB ...\n",
+       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
+       "Dimensions without coordinates: value\n",
+       "Data variables: (12/18)\n",
+       "    cdww     (time, value) float64 53MB ...\n",
+       "    dwi      (time, value) float64 53MB ...\n",
+       "    hmax     (time, value) float64 53MB ...\n",
+       "    mp1      (time, value) float64 53MB ...\n",
+       "    mp2      (time, value) float64 53MB ...\n",
+       "    mwd      (time, value) float64 53MB ...\n",
+       "    ...       ...\n",
+       "    ust      (time, value) float64 53MB ...\n",
+       "    vst      (time, value) float64 53MB ...\n",
+       "    wdw      (time, value) float64 53MB ...\n",
+       "    wind     (time, value) float64 53MB ...\n",
+       "    wmb      (time, value) float64 53MB ...\n",
+       "    wstar    (time, value) float64 53MB ...\n",
+       "Attributes:\n",
+       "    GRIB_edition:            1\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc..."
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_wave = load_hplp_data(leveltype=\"wave\", gridtype=\"reduced_ll\")\n",
+    "ds_wave"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a936cb8e-b821-46a2-8fba-e1619e12f097",
+   "metadata": {},
+   "source": [
+    "The data can be visualized as demonstrated below. For example, the 10 metre wind speed on **2020-07-31 at 00:00:00 UTC** is plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "44680327-cd31-4720-b075-55d1f031489a",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "ee03b214-8009-4d44-83e2-0783892050a1",
-      "cell_type": "code",
-      "source": "",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [],
-      "execution_count": null
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ]
-}
\ No newline at end of file
+   ],
+   "source": [
+    "earthkit.plots.quickmap.plot(\n",
+    "    ds_wave[\"wind\"].sel(time=\"2020-07-31T00:00:00\"),\n",
+    "    x=\"lon\", y=\"lat\",\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee03b214-8009-4d44-83e2-0783892050a1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/04-example-datasets/02-OpenIFS.ipynb b/04-example-datasets/02-OpenIFS.ipynb
index 513ef82..36cc2ad 100644
--- a/04-example-datasets/02-OpenIFS.ipynb
+++ b/04-example-datasets/02-OpenIFS.ipynb
@@ -1,267 +1,6433 @@
 {
-  "metadata": {
-    "kernelspec": {
-      "name": "python",
-      "display_name": "Python (Pyodide)",
-      "language": "python"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "python",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
+   "metadata": {},
+   "source": [
+    "# OpenIFS 43r3 data"
+   ]
   },
-  "nbformat_minor": 5,
-  "nbformat": 4,
-  "cells": [
-    {
-      "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
-      "cell_type": "markdown",
-      "source": "# OpenIFS 43r3 data",
-      "metadata": {}
-    },
-    {
-      "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
-      "cell_type": "markdown",
-      "source": "> [!NOTE]\n> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n\n> [!CAUTION]\n> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep.",
-      "metadata": {}
-    },
-    {
-      "id": "463bf2c0-ac3f-4786-9687-362393802dce",
-      "cell_type": "markdown",
-      "source": "This notebook is a comprehensive guide to accessing NetDCF files for an OpenIFS simulation. It includes details about the available parameters, levels, and steps, along with instructions for using the `load_OpenIFS_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point output from the OpenIFS 43r3 model.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco1279), corresponding to a horizontal resolution of approximately 9 km.\nThe 5-day forecast was run with a time step length of 600 seconds using a HighResMIP based output configuration.\n\n\nThere is a single NetCDF file for the surface level fields and separate files for each pressure level field.\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in Pa):\n```python\n\n[100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 25000, 20000, 15000, 10000, 7000, 5000, 3000, 2000, 1000, 500, 100]\n\n```\nThe available time steps start at 2009-10-15 18:00:00 UTC, increasing in 6-hour steps to 2009-10-20 12:00:00 UTC.\n\n### Surface Level Parameters\n\nThe following parameters are available as surface level fields:\n```python\n[\"10u\", \"10v\", \"2d\", \"2t\", \"cp\", \"e\", \"es\", \"ewss\", \"msl\", \"nsss\", \"ro\", \"rsn\", \"sd\", \"sf\", \"skt\", \"slhf\", \"smlt\", \"sp\", \"sro\", \"sshf\", \"ssr\", \"ssrc\", \"ssrd\", \"stl1\", \"stl2\", \"stl3\", \"stl4\", \"str\", \"strc\", \"strd\", \"swvl1\", \"swvl2\", \"swvl3\", \"swvl4\", \"tcc\", \"tciw\", \"tclw\", \"tcwv\", \"tisr\", \"tp\", \"tsn\", \"tsr\", \"tsrc\", \"ttr\", \"ttrc\"]\n```\n\nHere, the available time steps start at 2009-10-15 15:00:00 UTC, increasing in 3-hour steps to 2009-10-20 12:00:00 UTC.",
-      "metadata": {}
-    },
-    {
-      "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
-      "cell_type": "markdown",
-      "source": "## Using the `load_OpenIFS_data` Function\n\nTo load the NetCDF files, you can use the following function:\n\n```python\nload_OpenIFS_data(leveltype=None, param=None)\n```\n\n### Parameters\n\n- **`leveltype`**: Defines the type of data to load. Choose one of the following:\n  - `\"pl\"`: Pressure level fields.\n  - `\"sfc\"`: Surface level fields.\n- **`param`**: Specifies the parameter of the pressure level data. Options include: `\"q\"`, `\"r\"`, `\"t\"`, `\"u\"`, `\"v\"`, `\"vo\"`, `\"w\"`, `\"v\"` and `\"z\"`.\n",
-      "metadata": {}
-    },
-    {
-      "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
-      "cell_type": "code",
-      "source": "# URL of S3 bucket\nBASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\"",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [],
-      "execution_count": 1
-    },
-    {
-      "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
-      "cell_type": "markdown",
-      "source": "First, the package `zarr` (modern dataset format that is specifically designed for chunked access) and its dependencies need to be imported for the remote access.",
-      "metadata": {}
-    },
-    {
-      "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
-      "cell_type": "code",
-      "source": "import aiohttp\nimport dask\nimport fsspec\nimport zarr",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Loading Jinja2, MarkupSafe, aiohttp, aiosignal, asciitree, async-timeout, attrs, click, cloudpickle, dask, frozenlist, fsspec, importlib_metadata, locket, msgpack, multidict, numcodecs, numpy, partd, propcache, pyyaml, toolz, yarl, zarr, zipp\n[pyodide]: Loaded Jinja2, MarkupSafe, aiohttp, aiosignal, asciitree, async-timeout, attrs, click, cloudpickle, dask, frozenlist, fsspec, importlib_metadata, locket, msgpack, multidict, numcodecs, numpy, partd, propcache, pyyaml, toolz, yarl, zarr, zipp\n[pyodide]: Memory usage has grown to 71.9MiB (from 49.9MiB) for this notebook\n",
-          "output_type": "stream"
-        }
-      ],
-      "execution_count": 2
-    },
-    {
-      "id": "5885a858-f6a6-429b-ae83-69e3f965f032",
-      "cell_type": "markdown",
-      "source": "The `load_OpenIFS_data` function simplifies remote access to GRIB datasets stored on the S3 bucket. It validates input parameters, constructs the correct URL for the requested data, and loads it efficiently as an xarray dataset using the corresponding `.ref` file.\n",
-      "metadata": {}
-    },
+  {
+   "cell_type": "markdown",
+   "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
+   "metadata": {},
+   "source": [
+    "> [!NOTE]\n",
+    "> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n",
+    "\n",
+    "> [!CAUTION]\n",
+    "> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "463bf2c0-ac3f-4786-9687-362393802dce",
+   "metadata": {},
+   "source": [
+    "This notebook is a comprehensive guide to accessing NetDCF files for an OpenIFS simulation. It includes details about the available parameters, levels, and steps, along with instructions for using the `load_OpenIFS_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Experiment Details and Available Data Fields\n",
+    "\n",
+    "The dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point output from the OpenIFS 43r3 model.\n",
+    "The simulation was conducted on a reduced Gaussian octahedral grid (Tco1279), corresponding to a horizontal resolution of approximately 9 km.\n",
+    "The 5-day forecast was run with a time step length of 600 seconds using a HighResMIP based output configuration. \n",
+    "The total volume of the available dataset is about 258 GB.\n",
+    "\n",
+    "\n",
+    "There is a single NetCDF file for the surface level fields and separate files for each pressure level field.\n",
+    "\n",
+    "### Pressure Level Parameters\n",
+    "\n",
+    "The following parameters are available as pressure level fields:\n",
+    "```python\n",
+    "[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n",
+    "```\n",
+    "\n",
+    "These parameters are stored at the following pressure levels (in Pa):\n",
+    "```python\n",
+    "\n",
+    "[100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 25000, 20000, 15000, 10000, 7000, 5000, 3000, 2000, 1000, 500, 100]\n",
+    "\n",
+    "```\n",
+    "The available time steps start at 2009-10-15 18:00:00 UTC, increasing in 6-hour steps to 2009-10-20 12:00:00 UTC.\n",
+    "\n",
+    "### Surface Level Parameters\n",
+    "\n",
+    "The following parameters are available as surface level fields:\n",
+    "```python\n",
+    "[\"10u\", \"10v\", \"2d\", \"2t\", \"cp\", \"e\", \"es\", \"ewss\", \"msl\", \"nsss\", \"ro\", \"rsn\", \"sd\", \"sf\", \"skt\", \"slhf\", \"smlt\", \"sp\", \"sro\", \"sshf\", \"ssr\", \"ssrc\", \"ssrd\", \"stl1\", \"stl2\", \"stl3\", \"stl4\", \"str\", \"strc\", \"strd\", \"swvl1\", \"swvl2\", \"swvl3\", \"swvl4\", \"tcc\", \"tciw\", \"tclw\", \"tcwv\", \"tisr\", \"tp\", \"tsn\", \"tsr\", \"tsrc\", \"ttr\", \"ttrc\"]\n",
+    "```\n",
+    "\n",
+    "Here, the available time steps start at 2009-10-15 15:00:00 UTC, increasing in 3-hour steps to 2009-10-20 12:00:00 UTC."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
+   "metadata": {},
+   "source": [
+    "## Using the `load_OpenIFS_data` Function\n",
+    "\n",
+    "To load the NetCDF files, you can use the following function:\n",
+    "\n",
+    "```python\n",
+    "load_OpenIFS_data(leveltype=None, param=None)\n",
+    "```\n",
+    "\n",
+    "### Parameters\n",
+    "\n",
+    "- **`leveltype`**: Defines the type of data to load. Choose one of the following:\n",
+    "  - `\"pl\"`: Pressure level fields.\n",
+    "  - `\"sfc\"`: Surface level fields.\n",
+    "- **`param`**: Specifies the parameter of the pressure level data. Options include: `\"q\"`, `\"r\"`, `\"t\"`, `\"u\"`, `\"v\"`, `\"vo\"`, `\"w\"`, `\"v\"` and `\"z\"`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# URL of S3 bucket\n",
+    "BASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
+   "metadata": {},
+   "source": [
+    "First, the package `zarr` (modern dataset format that is specifically designed for chunked access) and its dependencies need to be imported for the remote access."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "3a68e663-c6c8-49c2-9678-929d87f772b4",
-      "cell_type": "code",
-      "source": "import json\n\nimport requests\nimport xarray as xr\n\n\ndef load_OpenIFS_data(leveltype=None, param=None):\n    if leveltype not in {\"pl\", \"sfc\"}:\n        raise ValueError(f\"Invalid leveltype: {leveltype}\")\n\n    if leveltype == \"pl\" and not param:\n        raise ValueError(f\"Param is required for leveltype '{leveltype}'. Available params: q, r, t, u, v, vo, w, z\")\n\n    if leveltype == \"sfc\" and param:\n        print(\"Warning: Specifying 'param' is unnecessary for sfc level and will be ignored.\")\n\n    if leveltype == \"sfc\":\n        url = f\"{BASE_URL}/OpenIFS/HighResMIP_3h_reduced_sfc.nc\"\n    elif leveltype == \"pl\":\n        if param not in {\"q\", \"r\", \"t\", \"u\", \"v\", \"vo\", \"w\", \"z\"}:\n            raise ValueError(f\"Incorrect param: '{param}'. Available: q, r, t, u, v, vo, w, z\")\n\n        url = f\"{BASE_URL}/OpenIFS/HighResMIP_6h_reduced_pl_{param}.nc\"\n        \n    ref = requests.get(f\"{url}.ref\").json()\n\n    print(f\"Loading dataset {url}\")\n\n    return xr.open_dataset(\n        \"reference://\", \n        engine=\"zarr\", \n        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n        consolidated=False, \n        chunks=dict(),\n    )",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Loading pandas, python-dateutil, pytz, tzdata, xarray\n[pyodide]: Loaded pandas, python-dateutil, pytz, tzdata, xarray\n[pyodide]: Memory usage has grown to 124.2MiB (from 71.9MiB) for this notebook\n",
-          "output_type": "stream"
-        }
-      ],
-      "execution_count": 3
-    },
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Python(8725) MallocStackLogging: can't turn off malloc stack logging because it was not enabled.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import aiohttp\n",
+    "import dask\n",
+    "import fsspec\n",
+    "import zarr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5885a858-f6a6-429b-ae83-69e3f965f032",
+   "metadata": {},
+   "source": [
+    "The `load_OpenIFS_data` function simplifies remote access to NetCDF datasets stored on the S3 bucket. It validates input parameters, constructs the correct URL for the requested data, and loads it efficiently as an xarray dataset using the corresponding `.ref` file.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3a68e663-c6c8-49c2-9678-929d87f772b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "import requests\n",
+    "import xarray as xr\n",
+    "\n",
+    "\n",
+    "def load_OpenIFS_data(leveltype=None, param=None):\n",
+    "    if leveltype not in {\"pl\", \"sfc\"}:\n",
+    "        raise ValueError(f\"Invalid leveltype: '{leveltype}'. Available leveltypes: pl, sfc\")\n",
+    "\n",
+    "    if leveltype == \"pl\" and not param:\n",
+    "        raise ValueError(f\"Param is required for leveltype '{leveltype}'. Available params: q, r, t, u, v, vo, w, z\")\n",
+    "\n",
+    "    if leveltype == \"sfc\" and param:\n",
+    "        print(\"Warning: Specifying 'param' is unnecessary for sfc level and will be ignored.\")\n",
+    "\n",
+    "    if leveltype == \"sfc\":\n",
+    "        url = f\"{BASE_URL}/OpenIFS/HighResMIP_3h_reduced_sfc.nc\"\n",
+    "    elif leveltype == \"pl\":\n",
+    "        if param not in {\"q\", \"r\", \"t\", \"u\", \"v\", \"vo\", \"w\", \"z\"}:\n",
+    "            raise ValueError(f\"Incorrect param: '{param}'. Available: q, r, t, u, v, vo, w, z\")\n",
+    "\n",
+    "        url = f\"{BASE_URL}/OpenIFS/HighResMIP_6h_reduced_pl_{param}.nc\"\n",
+    "        \n",
+    "    ref = requests.get(f\"{url}.ref\").json()\n",
+    "\n",
+    "    print(f\"Loading dataset {url}\")\n",
+    "\n",
+    "    return xr.open_dataset(\n",
+    "        \"reference://\", \n",
+    "        engine=\"zarr\", \n",
+    "        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n",
+    "        consolidated=False, \n",
+    "        chunks=dict(),\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10b46f55-ec2a-45ea-87c6-46173ce58696",
+   "metadata": {},
+   "source": [
+    "## Example Usage\n",
+    "\n",
+    "To load NetCDF file for pressure level parameters, you can use the following example. This will load the **specific humidity (q)** data. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "37c31fe0-661a-4b71-afb0-61f7fb804f5f",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "10b46f55-ec2a-45ea-87c6-46173ce58696",
-      "cell_type": "markdown",
-      "source": "## Example Usage\n\nTo load NetCDF file for pressure level parameters, you can use the following example. This will load the **specific humidity (q)** data. \n",
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/HighResMIP_6h_reduced_pl_q.nc\n"
+     ]
     },
     {
-      "id": "37c31fe0-661a-4b71-afb0-61f7fb804f5f",
-      "cell_type": "code",
-      "source": "ds_pl_q = load_OpenIFS_data(leveltype=\"pl\", param=\"q\")\nds_pl_q",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/HighResMIP_6h_reduced_pl_q.nc\n",
-          "output_type": "stream"
-        },
-        {
-          "execution_count": 4,
-          "output_type": "execute_result",
-          "data": {
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 20GB\nDimensions:              (cell: 6599680, nvertex: 4, pressure_levels: 19,\n                          time_counter: 20, axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n  * pressure_levels      (pressure_levels) float32 76B 1e+05 9.25e+04 ... 100.0\n  * time_counter         (time_counter) datetime64[ns] 160B 2009-10-15T18:00:...\n    time_instant         (time_counter) datetime64[ns] 160B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables:\n    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    q                    (time_counter, pressure_levels, cell) float64 20GB dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;\n    time_counter_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n    time_instant_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_6h_reduced_pl_q\n    timeStamp:    2024-Apr-05 11:05:21 GMT\n    title:        Created by xios\n    uuid:         25d5f0a2-ceae-47e1-936f-5b1804228ca6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3bbbbf2b-102e-4ae6-bcd9-14b54a51a842' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3bbbbf2b-102e-4ae6-bcd9-14b54a51a842' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span class='xr-has-index'>pressure_levels</span>: 19</li><li><span class='xr-has-index'>time_counter</span>: 20</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9eae4465-38df-47ea-b9e3-2541ffb96e96' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9eae4465-38df-47ea-b9e3-2541ffb96e96' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-ca116bc8-6934-49e8-9b2f-f168a118237e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca116bc8-6934-49e8-9b2f-f168a118237e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d0d5643-b584-4fe7-a9e2-a3350c4b3359' class='xr-var-data-in' type='checkbox'><label for='data-7d0d5643-b584-4fe7-a9e2-a3350c4b3359' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-4fcdeef2-58b2-48bd-8228-8c6705972039' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fcdeef2-58b2-48bd-8228-8c6705972039' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af3e8dda-ff2c-46a4-8b1a-e58116ffdbfa' class='xr-var-data-in' type='checkbox'><label for='data-af3e8dda-ff2c-46a4-8b1a-e58116ffdbfa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure_levels</span></div><div class='xr-var-dims'>(pressure_levels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.25e+04 ... 500.0 100.0</div><input id='attrs-910beac7-67fa-4d1f-9892-712905008181' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-910beac7-67fa-4d1f-9892-712905008181' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7845d2b1-0039-4a6c-b151-f3836c67045f' class='xr-var-data-in' type='checkbox'><label for='data-7845d2b1-0039-4a6c-b151-f3836c67045f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertical pressure levels</dd><dt><span>name :</span></dt><dd>pressure_levels</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  92500.,  85000.,  70000.,  60000.,  50000.,  40000.,  30000.,\n        25000.,  20000.,  15000.,  10000.,   7000.,   5000.,   3000.,   2000.,\n         1000.,    500.,    100.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T18:00:00 ... 2009-10-...</div><input id='attrs-aa268933-63c9-46e2-b93e-be463eb417ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa268933-63c9-46e2-b93e-be463eb417ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5bda3b7-e57a-444a-acbe-fbb9d11a5d51' class='xr-var-data-in' type='checkbox'><label for='data-b5bda3b7-e57a-444a-acbe-fbb9d11a5d51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T18:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n       &#x27;2009-10-16T06:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n       &#x27;2009-10-16T18:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n       &#x27;2009-10-17T06:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n       &#x27;2009-10-17T18:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n       &#x27;2009-10-18T06:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n       &#x27;2009-10-18T18:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n       &#x27;2009-10-19T06:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n       &#x27;2009-10-19T18:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n       &#x27;2009-10-20T06:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-28ce60a1-80be-44cf-a59c-262a4284229d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28ce60a1-80be-44cf-a59c-262a4284229d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28d46195-984d-4dcf-8fc4-93437407fb6d' class='xr-var-data-in' type='checkbox'><label for='data-28d46195-984d-4dcf-8fc4-93437407fb6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 160 B </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"84\" style=\"stroke:rgb(0,0,0);stroke-width:1\" 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/>\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"34\" />\n  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"34\" />\n  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"34\" />\n  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"34\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,34.50101617379934 0.0,34.50101617379934\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"54.501016\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >20</text>\n  <text x=\"140.000000\" y=\"17.250508\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,17.250508)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-4cd7286c-6b69-4e69-b8d7-a3c4ee596db0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4cd7286c-6b69-4e69-b8d7-a3c4ee596db0' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-7f4aa29a-8632-4ce4-a699-665d122cc943' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f4aa29a-8632-4ce4-a699-665d122cc943' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67626ac9-67e9-4126-92cd-18034be9e7a2' class='xr-var-data-in' type='checkbox'><label for='data-67626ac9-67e9-4126-92cd-18034be9e7a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-9ace5892-d542-4283-a1b7-8b9a8b661e5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ace5892-d542-4283-a1b7-8b9a8b661e5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a3322a7-2b28-4748-a36b-2bff6da79ab0' class='xr-var-data-in' type='checkbox'><label for='data-3a3322a7-2b28-4748-a36b-2bff6da79ab0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time_counter, pressure_levels, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;</div><input id='attrs-1248dc64-338c-48f6-ad9d-9a2b45c9daf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1248dc64-338c-48f6-ad9d-9a2b45c9daf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c626fc3-94d9-4076-aed9-d1540274da4a' class='xr-var-data-in' type='checkbox'><label for='data-3c626fc3-94d9-4076-aed9-d1540274da4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>6 h</dd><dt><span>interval_write :</span></dt><dd>6 h</dd><dt><span>long_name :</span></dt><dd>specific humidity</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg*kg-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.69 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 19, 6599680) </td>\n                        <td> (1, 1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 6080 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"1\" x2=\"24\" y2=\"16\" />\n  <line x1=\"10\" y1=\"2\" x2=\"24\" 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class='xr-var-data-in' type='checkbox'><label for='data-d1b73df7-67d1-45d1-bb90-c9bf5aa7c697' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"38\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" 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id='data-1f36ef9f-d755-479e-ba0b-60493c10e36c' class='xr-var-data-in' type='checkbox'><label for='data-1f36ef9f-d755-479e-ba0b-60493c10e36c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"38\" 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0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"19.298432\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"58.596863\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,58.596863,60.000000)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b84975b4-f549-4a33-abde-874af83c987e' class='xr-section-summary-in' type='checkbox'  ><label for='section-b84975b4-f549-4a33-abde-874af83c987e' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>pressure_levels</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-53f51be9-5afc-4744-8537-4d68c8c69a44' class='xr-index-data-in' type='checkbox'/><label for='index-53f51be9-5afc-4744-8537-4d68c8c69a44' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000.0,  92500.0,  85000.0,  70000.0,  60000.0,  50000.0,  40000.0,\n        30000.0,  25000.0,  20000.0,  15000.0,  10000.0,   7000.0,   5000.0,\n         3000.0,   2000.0,   1000.0,    500.0,    100.0],\n      dtype=&#x27;float32&#x27;, name=&#x27;pressure_levels&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3a371be3-1425-4641-b024-4f002fc0099f' class='xr-index-data-in' type='checkbox'/><label for='index-3a371be3-1425-4641-b024-4f002fc0099f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 18:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n               &#x27;2009-10-16 06:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n               &#x27;2009-10-16 18:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n               &#x27;2009-10-17 06:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n               &#x27;2009-10-17 18:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n               &#x27;2009-10-18 06:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n               &#x27;2009-10-18 18:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n               &#x27;2009-10-19 06:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n               &#x27;2009-10-19 18:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n               &#x27;2009-10-20 06:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9570fa57-07e3-42a0-991c-4db59d470b1b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9570fa57-07e3-42a0-991c-4db59d470b1b' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_6h_reduced_pl_q</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 11:05:21 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>25d5f0a2-ceae-47e1-936f-5b1804228ca6</dd></dl></div></li></ul></div></div>"
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+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:              (cell: 6599680, nvertex: 4, pressure_levels: 19,\n",
+       "                          time_counter: 20, axis_nbounds: 2)\n",
+       "Coordinates:\n",
+       "    lat                  (cell) float32 dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n",
+       "    lon                  (cell) float32 dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n",
+       "  * pressure_levels      (pressure_levels) float32 1e+05 9.25e+04 ... 100.0\n",
+       "  * time_counter         (time_counter) datetime64[ns] 2009-10-15T18:00:00 .....\n",
+       "    time_instant         (time_counter) datetime64[ns] dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "Dimensions without coordinates: cell, nvertex, axis_nbounds\n",
+       "Data variables:\n",
+       "    bounds_lat           (cell, nvertex) float32 dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n",
+       "    bounds_lon           (cell, nvertex) float32 dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n",
+       "    q                    (time_counter, pressure_levels, cell) float64 dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;\n",
+       "    time_counter_bounds  (time_counter, axis_nbounds) datetime64[ns] dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
+       "    time_instant_bounds  (time_counter, axis_nbounds) datetime64[ns] dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    description:  Created by xios\n",
+       "    name:         HighResMIP_6h_reduced_pl_q\n",
+       "    timeStamp:    2024-Apr-05 11:05:21 GMT\n",
+       "    title:        Created by xios\n",
+       "    uuid:         25d5f0a2-ceae-47e1-936f-5b1804228ca6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a3dbf7a5-1290-49dc-b5ce-804d9d5b8257' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3dbf7a5-1290-49dc-b5ce-804d9d5b8257' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span class='xr-has-index'>pressure_levels</span>: 19</li><li><span class='xr-has-index'>time_counter</span>: 20</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-003d3ffc-41af-412d-8282-c28e0d909707' class='xr-section-summary-in' type='checkbox'  checked><label for='section-003d3ffc-41af-412d-8282-c28e0d909707' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-f1221d5e-ac6c-4eff-985d-d30200fa229c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1221d5e-ac6c-4eff-985d-d30200fa229c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0a3d300-b280-433e-b54b-6fb006da5b8e' class='xr-var-data-in' type='checkbox'><label for='data-f0a3d300-b280-433e-b54b-6fb006da5b8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 25.18 MiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (6599680,) </td>\n",
+       "                        <td> (824960,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-b86c525f-f0df-4417-b2b2-058519d411c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b86c525f-f0df-4417-b2b2-058519d411c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de4a86da-e826-4399-b62a-c189d3dbbf12' class='xr-var-data-in' type='checkbox'><label for='data-de4a86da-e826-4399-b62a-c189d3dbbf12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 25.18 MiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (6599680,) </td>\n",
+       "                        <td> (824960,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure_levels</span></div><div class='xr-var-dims'>(pressure_levels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.25e+04 ... 500.0 100.0</div><input id='attrs-0160eb34-72d4-48df-83c9-b36189647df8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0160eb34-72d4-48df-83c9-b36189647df8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23095e8a-6380-4c29-9211-f5c58b14c8b8' class='xr-var-data-in' type='checkbox'><label for='data-23095e8a-6380-4c29-9211-f5c58b14c8b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertical pressure levels</dd><dt><span>name :</span></dt><dd>pressure_levels</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  92500.,  85000.,  70000.,  60000.,  50000.,  40000.,  30000.,\n",
+       "        25000.,  20000.,  15000.,  10000.,   7000.,   5000.,   3000.,   2000.,\n",
+       "         1000.,    500.,    100.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T18:00:00 ... 2009-10-...</div><input id='attrs-1adb0af2-a504-4725-ae83-c800b2a8d104' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1adb0af2-a504-4725-ae83-c800b2a8d104' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffe681b4-1de9-4c0a-88b3-6a8bbbafd935' class='xr-var-data-in' type='checkbox'><label for='data-ffe681b4-1de9-4c0a-88b3-6a8bbbafd935' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T18:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-16T06:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-16T18:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-17T06:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-17T18:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-18T06:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-18T18:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-19T06:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-19T18:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-20T06:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-c043edfb-6c49-4c4b-bce5-f896e8f35868' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c043edfb-6c49-4c4b-bce5-f896e8f35868' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c6b2b62-b00b-42c3-843b-22d6db116289' class='xr-var-data-in' type='checkbox'><label for='data-7c6b2b62-b00b-42c3-843b-22d6db116289' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 160 B </td>\n",
+       "                        <td> 8 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (20,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"84\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e514a4c0-ebeb-4fc2-8bc1-de8fc78d5f66' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e514a4c0-ebeb-4fc2-8bc1-de8fc78d5f66' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-3f2ad75b-ea3d-4959-ac54-3c151da308ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f2ad75b-ea3d-4959-ac54-3c151da308ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b229b163-4ec3-4cf5-99d8-cd9294f471c7' class='xr-var-data-in' type='checkbox'><label for='data-b229b163-4ec3-4cf5-99d8-cd9294f471c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 100.70 MiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (6599680, 4) </td>\n",
+       "                        <td> (206240, 4) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-b9eabdda-1011-449c-ab70-f076032d9bfe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9eabdda-1011-449c-ab70-f076032d9bfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d4f087e-e876-48bd-b1cb-2985a701b9d6' class='xr-var-data-in' type='checkbox'><label for='data-5d4f087e-e876-48bd-b1cb-2985a701b9d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 100.70 MiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (6599680, 4) </td>\n",
+       "                        <td> (206240, 4) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time_counter, pressure_levels, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;</div><input id='attrs-9e82060b-0645-4bb8-ac93-07bc148ab1fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e82060b-0645-4bb8-ac93-07bc148ab1fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49469b49-46fe-4bbc-80d1-91428c2ace84' class='xr-var-data-in' type='checkbox'><label for='data-49469b49-46fe-4bbc-80d1-91428c2ace84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>6 h</dd><dt><span>interval_write :</span></dt><dd>6 h</dd><dt><span>long_name :</span></dt><dd>specific humidity</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg*kg-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (20, 19, 6599680) </td>\n",
+       "                        <td> (1, 1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 6080 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-094d23e1-c351-494f-a2a0-7c6e9071c4be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-094d23e1-c351-494f-a2a0-7c6e9071c4be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae728cd4-3bfd-449d-b7a1-634c95dd3cd5' class='xr-var-data-in' type='checkbox'><label for='data-ae728cd4-3bfd-449d-b7a1-634c95dd3cd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 320 B </td>\n",
+       "                        <td> 16 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (20, 2) </td>\n",
+       "                        <td> (1, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-98ead1c0-fcc5-45b0-acdc-42d8ea4f5400' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98ead1c0-fcc5-45b0-acdc-42d8ea4f5400' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d33fe9a-fa8a-4d1c-9f9b-0c97aec637ab' class='xr-var-data-in' type='checkbox'><label for='data-4d33fe9a-fa8a-4d1c-9f9b-0c97aec637ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 320 B </td>\n",
+       "                        <td> 16 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (20, 2) </td>\n",
+       "                        <td> (1, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n",
+       "  <line x1=\"0\" y1=\"36\" x2=\"38\" y2=\"36\" />\n",
+       "  <line x1=\"0\" y1=\"42\" x2=\"38\" y2=\"42\" />\n",
+       "  <line x1=\"0\" y1=\"48\" x2=\"38\" y2=\"48\" />\n",
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+       "  <line x1=\"0\" y1=\"60\" x2=\"38\" y2=\"60\" />\n",
+       "  <line x1=\"0\" y1=\"66\" x2=\"38\" y2=\"66\" />\n",
+       "  <line x1=\"0\" y1=\"72\" x2=\"38\" y2=\"72\" />\n",
+       "  <line x1=\"0\" y1=\"78\" x2=\"38\" y2=\"78\" />\n",
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+       "  <line x1=\"0\" y1=\"90\" x2=\"38\" y2=\"90\" />\n",
+       "  <line x1=\"0\" y1=\"96\" x2=\"38\" y2=\"96\" />\n",
+       "  <line x1=\"0\" y1=\"102\" x2=\"38\" y2=\"102\" />\n",
+       "  <line x1=\"0\" y1=\"108\" x2=\"38\" y2=\"108\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 38.596863036086,0.0 38.596863036086,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"19.298432\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"58.596863\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,58.596863,60.000000)\">20</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6bd8db64-1956-4212-9907-0a3a480e4aac' class='xr-section-summary-in' type='checkbox'  ><label for='section-6bd8db64-1956-4212-9907-0a3a480e4aac' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>pressure_levels</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ab522574-e230-47c6-a2e4-a67c685a8dbf' class='xr-index-data-in' type='checkbox'/><label for='index-ab522574-e230-47c6-a2e4-a67c685a8dbf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000.0,  92500.0,  85000.0,  70000.0,  60000.0,  50000.0,  40000.0,\n",
+       "        30000.0,  25000.0,  20000.0,  15000.0,  10000.0,   7000.0,   5000.0,\n",
+       "         3000.0,   2000.0,   1000.0,    500.0,    100.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;pressure_levels&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2542cba3-3c5c-4e6d-8ed5-424804fd58f1' class='xr-index-data-in' type='checkbox'/><label for='index-2542cba3-3c5c-4e6d-8ed5-424804fd58f1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 18:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n",
+       "               &#x27;2009-10-16 06:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n",
+       "               &#x27;2009-10-16 18:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n",
+       "               &#x27;2009-10-17 06:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n",
+       "               &#x27;2009-10-17 18:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n",
+       "               &#x27;2009-10-18 06:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n",
+       "               &#x27;2009-10-18 18:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n",
+       "               &#x27;2009-10-19 06:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n",
+       "               &#x27;2009-10-19 18:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n",
+       "               &#x27;2009-10-20 06:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-72a56cd9-6dcd-49b5-9968-18e0aea4efb5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-72a56cd9-6dcd-49b5-9968-18e0aea4efb5' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_6h_reduced_pl_q</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 11:05:21 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>25d5f0a2-ceae-47e1-936f-5b1804228ca6</dd></dl></div></li></ul></div></div>"
       ],
-      "execution_count": 4
-    },
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:              (cell: 6599680, nvertex: 4, pressure_levels: 19,\n",
+       "                          time_counter: 20, axis_nbounds: 2)\n",
+       "Coordinates:\n",
+       "    lat                  (cell) float32 dask.array<chunksize=(824960,), meta=np.ndarray>\n",
+       "    lon                  (cell) float32 dask.array<chunksize=(824960,), meta=np.ndarray>\n",
+       "  * pressure_levels      (pressure_levels) float32 1e+05 9.25e+04 ... 100.0\n",
+       "  * time_counter         (time_counter) datetime64[ns] 2009-10-15T18:00:00 .....\n",
+       "    time_instant         (time_counter) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "Dimensions without coordinates: cell, nvertex, axis_nbounds\n",
+       "Data variables:\n",
+       "    bounds_lat           (cell, nvertex) float32 dask.array<chunksize=(206240, 4), meta=np.ndarray>\n",
+       "    bounds_lon           (cell, nvertex) float32 dask.array<chunksize=(206240, 4), meta=np.ndarray>\n",
+       "    q                    (time_counter, pressure_levels, cell) float64 dask.array<chunksize=(1, 1, 412480), meta=np.ndarray>\n",
+       "    time_counter_bounds  (time_counter, axis_nbounds) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
+       "    time_instant_bounds  (time_counter, axis_nbounds) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    description:  Created by xios\n",
+       "    name:         HighResMIP_6h_reduced_pl_q\n",
+       "    timeStamp:    2024-Apr-05 11:05:21 GMT\n",
+       "    title:        Created by xios\n",
+       "    uuid:         25d5f0a2-ceae-47e1-936f-5b1804228ca6"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_pl_q = load_OpenIFS_data(leveltype=\"pl\", param=\"q\")\n",
+    "ds_pl_q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33418b16-9735-4eb9-b540-942325d552df",
+   "metadata": {},
+   "source": [
+    "The `earthkit` package is used for plotting the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "169008a5-cfa4-4078-8bad-32510c9a91e7",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "33418b16-9735-4eb9-b540-942325d552df",
-      "cell_type": "markdown",
-      "source": "The `earthkit` package is used for plotting the data.",
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyodide]: Loading Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cftime, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n",
+      "[pyodide]: Loaded Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cftime, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n"
+     ]
     },
     {
-      "id": "169008a5-cfa4-4078-8bad-32510c9a91e7",
-      "cell_type": "code",
-      "source": "import earthkit.plots\nimport earthkit.plots.quickmap",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Loading Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cftime, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n[pyodide]: Loaded Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cftime, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n",
-          "output_type": "stream"
-        },
-        {
-          "name": "stderr",
-          "text": "/lib/python3.12/site-packages/earthkit/regrid/utils/caching.py:27: UserWarning: only soft file lock is available\n  from filelock import FileLock\n",
-          "output_type": "stream"
-        },
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 309.4MiB (from 124.2MiB) for this notebook\n",
-          "output_type": "stream"
-        }
-      ],
-      "execution_count": 5
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lib/python3.12/site-packages/earthkit/regrid/utils/caching.py:27: UserWarning: only soft file lock is available\n",
+      "  from filelock import FileLock\n"
+     ]
     },
     {
-      "id": "6ee55166-f151-474c-b36c-1e07bc516906",
-      "cell_type": "markdown",
-      "source": "The data can be visualized as demonstrated below. For example, the specific humidity at **pressure level 1e5Pa** on **2009-10-20 at 12:00:00 UTC** is plotted. `[::10]` applies slicing to the data being plotted (syntax: `[start:stop:step]`) in order to reduce computational load by selecting only every 10th data point.\n",
-      "metadata": {}
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyodide]: Memory usage has grown to 309.4MiB (from 124.2MiB) for this notebook\n"
+     ]
+    }
+   ],
+   "source": [
+    "import earthkit.plots\n",
+    "import earthkit.plots.quickmap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ee55166-f151-474c-b36c-1e07bc516906",
+   "metadata": {},
+   "source": [
+    "The data can be visualized as demonstrated below. For example, the specific humidity at **pressure level 1e5Pa** on **2009-10-20 at 12:00:00 UTC** is plotted. `[::10]` applies slicing to the data being plotted (syntax: `[start:stop:step]`) in order to reduce computational load by selecting only every 10th data point.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2e12e256",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "2e12e256",
-      "cell_type": "code",
-      "source": "earthkit.plots.quickmap.plot(\n    ds_pl_q[\"q\"].rename(dict(time_counter=\"time\")).sel(\n        time=\"2009-10-20T12:00:00\", pressure_levels=1e5,\n    )[::10], x=\"lon\", y=\"lat\",\n);",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stderr",
-          "text": "/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip\n  warnings.warn(f'Downloading: {url}', DownloadWarning)\n",
-          "output_type": "stream"
-        },
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 735.2MiB (from 309.4MiB) for this notebook\n",
-          "output_type": "stream"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 700x600 with 2 Axes>",
-            "image/png": 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"
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 6
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip\n",
+      "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
+     ]
     },
     {
-      "id": "f2d66bcd-cb4b-4a03-970e-3ce49d68380e",
-      "cell_type": "markdown",
-      "source": "The surface level data contains all available parameters and can can be loaded as follows.",
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[pyodide]: Memory usage has grown to 735.2MiB (from 309.4MiB) for this notebook\n"
+     ]
     },
     {
-      "id": "5a574d92-3c0a-429b-97e8-d024d78c5daf",
-      "cell_type": "code",
-      "source": "ds_sfc = load_OpenIFS_data(leveltype=\"sfc\")\nds_sfc",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/HighResMIP_3h_reduced_sfc.nc\n",
-          "output_type": "stream"
-        },
-        {
-          "execution_count": 7,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "<xarray.Dataset> Size: 95GB\nDimensions:              (time_counter: 40, cell: 6599680, nvertex: 4,\n                          axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array<chunksize=(824960,), meta=np.ndarray>\n    lon                  (cell) float32 26MB dask.array<chunksize=(824960,), meta=np.ndarray>\n  * time_counter         (time_counter) datetime64[ns] 320B 2009-10-15T15:00:...\n    time_instant         (time_counter) datetime64[ns] 320B dask.array<chunksize=(1,), meta=np.ndarray>\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables: (12/49)\n    10u                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    10v                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    2d                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    2t                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    bounds_lat           (cell, nvertex) float32 106MB dask.array<chunksize=(206240, 4), meta=np.ndarray>\n    bounds_lon           (cell, nvertex) float32 106MB dask.array<chunksize=(206240, 4), meta=np.ndarray>\n    ...                   ...\n    tp                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    tsn                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    tsr                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    tsrc                 (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    ttr                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    ttrc                 (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_3h_reduced_sfc\n    timeStamp:    2024-Apr-05 10:55:20 GMT\n    title:        Created by xios\n    uuid:         dbac5691-165e-4465-bd75-e403aa400071",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 95GB\nDimensions:              (time_counter: 40, cell: 6599680, nvertex: 4,\n                          axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n  * time_counter         (time_counter) datetime64[ns] 320B 2009-10-15T15:00:...\n    time_instant         (time_counter) datetime64[ns] 320B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables: (12/49)\n    10u                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    10v                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    2d                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    2t                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    ...                   ...\n    tp                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsn                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    ttr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    ttrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_3h_reduced_sfc\n    timeStamp:    2024-Apr-05 10:55:20 GMT\n    title:        Created by xios\n    uuid:         dbac5691-165e-4465-bd75-e403aa400071</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e2b79126-156b-4971-aa40-1f7fb1c848b8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e2b79126-156b-4971-aa40-1f7fb1c848b8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time_counter</span>: 40</li><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-098082a9-5e61-4791-88ea-dc6794b684a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-098082a9-5e61-4791-88ea-dc6794b684a8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-2d8bf04b-ffea-4d8a-8b11-77dc8ecfcb9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d8bf04b-ffea-4d8a-8b11-77dc8ecfcb9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-debdaf7f-90ca-499b-9309-f71a161d7022' class='xr-var-data-in' type='checkbox'><label for='data-debdaf7f-90ca-499b-9309-f71a161d7022' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" 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text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-f645be90-f42a-4992-b922-4dfc016b7ba6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f645be90-f42a-4992-b922-4dfc016b7ba6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f12bb7ad-e881-4a93-a896-960aceb4e6d0' class='xr-var-data-in' type='checkbox'><label for='data-f12bb7ad-e881-4a93-a896-960aceb4e6d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T15:00:00 ... 2009-10-...</div><input id='attrs-2605a8bc-a9b5-49a2-a50a-926088d0ba57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2605a8bc-a9b5-49a2-a50a-926088d0ba57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cb07a8e-782f-4d1a-b021-8a5b3aa86454' class='xr-var-data-in' type='checkbox'><label for='data-5cb07a8e-782f-4d1a-b021-8a5b3aa86454' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T15:00:00.000000000&#x27;, &#x27;2009-10-15T18:00:00.000000000&#x27;,\n       &#x27;2009-10-15T21:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n       &#x27;2009-10-16T03:00:00.000000000&#x27;, &#x27;2009-10-16T06:00:00.000000000&#x27;,\n       &#x27;2009-10-16T09:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n       &#x27;2009-10-16T15:00:00.000000000&#x27;, &#x27;2009-10-16T18:00:00.000000000&#x27;,\n       &#x27;2009-10-16T21:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n       &#x27;2009-10-17T03:00:00.000000000&#x27;, &#x27;2009-10-17T06:00:00.000000000&#x27;,\n       &#x27;2009-10-17T09:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n       &#x27;2009-10-17T15:00:00.000000000&#x27;, &#x27;2009-10-17T18:00:00.000000000&#x27;,\n       &#x27;2009-10-17T21:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n       &#x27;2009-10-18T03:00:00.000000000&#x27;, &#x27;2009-10-18T06:00:00.000000000&#x27;,\n       &#x27;2009-10-18T09:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n       &#x27;2009-10-18T15:00:00.000000000&#x27;, &#x27;2009-10-18T18:00:00.000000000&#x27;,\n       &#x27;2009-10-18T21:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n       &#x27;2009-10-19T03:00:00.000000000&#x27;, &#x27;2009-10-19T06:00:00.000000000&#x27;,\n       &#x27;2009-10-19T09:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n       &#x27;2009-10-19T15:00:00.000000000&#x27;, &#x27;2009-10-19T18:00:00.000000000&#x27;,\n       &#x27;2009-10-19T21:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n       &#x27;2009-10-20T03:00:00.000000000&#x27;, &#x27;2009-10-20T06:00:00.000000000&#x27;,\n       &#x27;2009-10-20T09:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-912895ab-5744-4a6a-92ce-c960664dd168' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-912895ab-5744-4a6a-92ce-c960664dd168' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-624716ce-dd69-4ca3-857f-94fed0763e5a' class='xr-var-data-in' type='checkbox'><label for='data-624716ce-dd69-4ca3-857f-94fed0763e5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"80\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"30\" style=\"stroke-width:2\" />\n  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"30\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"30\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"30\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"30\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"30\" />\n  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"30\" />\n  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"30\" />\n  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"30\" />\n  <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"30\" />\n  <line 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      </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae47d7b9-fe7f-45ea-a255-1fb225804b0c' class='xr-section-summary-in' type='checkbox'  ><label for='section-ae47d7b9-fe7f-45ea-a255-1fb225804b0c' class='xr-section-summary' >Data variables: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-86f8230c-4e78-4330-9cf7-d1c269ac871a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86f8230c-4e78-4330-9cf7-d1c269ac871a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9fd55a1-7ad6-4dd5-911d-038a5daa527b' class='xr-var-data-in' type='checkbox'><label for='data-e9fd55a1-7ad6-4dd5-911d-038a5daa527b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>u-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n 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x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-94e2738e-e816-4a6c-b8a0-f111ae62bddc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94e2738e-e816-4a6c-b8a0-f111ae62bddc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65fc61ac-006b-4bcf-8f0e-d2b827625b2d' class='xr-var-data-in' type='checkbox'><label for='data-65fc61ac-006b-4bcf-8f0e-d2b827625b2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>v-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-173a46ff-f5fc-4ad0-8922-8c5f7f0593b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-173a46ff-f5fc-4ad0-8922-8c5f7f0593b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d6abb52-89bf-4e89-81c4-f4d187ccce79' class='xr-var-data-in' type='checkbox'><label for='data-4d6abb52-89bf-4e89-81c4-f4d187ccce79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m dewpoint temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-6afa68b0-3d0f-4481-9cbf-13d1989cbb7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6afa68b0-3d0f-4481-9cbf-13d1989cbb7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58e41a57-4519-43fb-ac55-312d07b1ef66' class='xr-var-data-in' type='checkbox'><label for='data-58e41a57-4519-43fb-ac55-312d07b1ef66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m air temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-5a488474-8be9-4473-9bae-bc3c450aec3a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a488474-8be9-4473-9bae-bc3c450aec3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17b40d48-180e-4c40-afd3-63d050b649bb' class='xr-var-data-in' type='checkbox'><label for='data-17b40d48-180e-4c40-afd3-63d050b649bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-d7ce6ada-3279-4dab-b793-f8c208faf313' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7ce6ada-3279-4dab-b793-f8c208faf313' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d3f1fbf-992f-4443-b778-66a8ed0a3389' class='xr-var-data-in' type='checkbox'><label for='data-5d3f1fbf-992f-4443-b778-66a8ed0a3389' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-d13022c1-ec9f-4eff-9e24-f3e8d348a1ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d13022c1-ec9f-4eff-9e24-f3e8d348a1ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5e42c02-f2a6-499f-b623-1b1b625e4d9a' class='xr-var-data-in' type='checkbox'><label for='data-d5e42c02-f2a6-499f-b623-1b1b625e4d9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>convective precipitation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-542fa6ce-dadb-4395-858e-020f2e95fec0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-542fa6ce-dadb-4395-858e-020f2e95fec0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bffde147-ec0a-46ec-a692-412d56a8aab2' class='xr-var-data-in' type='checkbox'><label for='data-bffde147-ec0a-46ec-a692-412d56a8aab2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4f277d3e-36d3-4e1b-b4c1-e1544f1066d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f277d3e-36d3-4e1b-b4c1-e1544f1066d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-849ad489-f6a6-4355-98f2-aac2c418b194' class='xr-var-data-in' type='checkbox'><label for='data-849ad489-f6a6-4355-98f2-aac2c418b194' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a055dd72-e844-49a1-ab61-31dc9160bdcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a055dd72-e844-49a1-ab61-31dc9160bdcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc8a62ff-d14f-4862-8b9d-2c981a19a622' class='xr-var-data-in' type='checkbox'><label for='data-bc8a62ff-d14f-4862-8b9d-2c981a19a622' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>eastward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_eastward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-97e2311a-0f15-4536-a164-28e73f1bf0c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97e2311a-0f15-4536-a164-28e73f1bf0c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b98a218d-dd83-4b26-8299-743a35eff53a' class='xr-var-data-in' type='checkbox'><label for='data-b98a218d-dd83-4b26-8299-743a35eff53a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_vel</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-1b3b4226-8a8b-4ec4-afac-da8b0fff17e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b3b4226-8a8b-4ec4-afac-da8b0fff17e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae67002d-65d6-4556-8fb6-abb3eba3bf83' class='xr-var-data-in' type='checkbox'><label for='data-ae67002d-65d6-4556-8fb6-abb3eba3bf83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>northward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_northward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-9a8b7df4-858d-4430-8783-e579725bbd17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a8b7df4-858d-4430-8783-e579725bbd17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2660eafb-fa12-4236-9e6a-9bc8b84031d0' class='xr-var-data-in' type='checkbox'><label for='data-2660eafb-fa12-4236-9e6a-9bc8b84031d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a8469d2c-af14-4797-aa2c-4d2ec27cf11d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8469d2c-af14-4797-aa2c-4d2ec27cf11d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf5fe558-954f-487d-9cef-60352c5c8328' class='xr-var-data-in' type='checkbox'><label for='data-cf5fe558-954f-487d-9cef-60352c5c8328' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow density</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_density</dd><dt><span>units :</span></dt><dd>kg m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-b513bbc5-24c4-490c-8e0e-3ef29adec6c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b513bbc5-24c4-490c-8e0e-3ef29adec6c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dca2ce8-8caf-45fc-9db4-ea43d17b5069' class='xr-var-data-in' type='checkbox'><label for='data-1dca2ce8-8caf-45fc-9db4-ea43d17b5069' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow depth</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-efb1c85c-045b-43a5-a9e6-6a1fd5fb5a49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efb1c85c-045b-43a5-a9e6-6a1fd5fb5a49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bce49b71-51bb-4c4d-8076-7599e74adf39' class='xr-var-data-in' type='checkbox'><label for='data-bce49b71-51bb-4c4d-8076-7599e74adf39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snowfall</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n 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x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-357373c0-db53-4a20-9472-e518d0b918fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-357373c0-db53-4a20-9472-e518d0b918fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f20d7b6d-39bc-489f-9622-695ce64371e9' class='xr-var-data-in' type='checkbox'><label for='data-f20d7b6d-39bc-489f-9622-695ce64371e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>skin temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>sea_surface_skin_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-036c2139-54b7-46b7-b2f1-bb26b9674c79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-036c2139-54b7-46b7-b2f1-bb26b9674c79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2460536-7c1d-4124-9f24-30d5dcd7e572' class='xr-var-data-in' type='checkbox'><label for='data-c2460536-7c1d-4124-9f24-30d5dcd7e572' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_latent_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5f118b58-321e-42bd-89e3-8e928e618777' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f118b58-321e-42bd-89e3-8e928e618777' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc12d01d-ad98-4bcc-ab79-80b466a3aa20' class='xr-var-data-in' type='checkbox'><label for='data-bc12d01d-ad98-4bcc-ab79-80b466a3aa20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow melt</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_snow_melt_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-d5222ae1-a84f-44f4-a1f1-37d39ac7aa8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5222ae1-a84f-44f4-a1f1-37d39ac7aa8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9713551d-a8dc-4288-a804-8123404eacf9' class='xr-var-data-in' type='checkbox'><label for='data-9713551d-a8dc-4288-a804-8123404eacf9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0f87fb97-0929-48c4-a087-f0d7a0b73315' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f87fb97-0929-48c4-a087-f0d7a0b73315' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-637e4153-85d0-47a7-a5ee-f7faf7f12001' class='xr-var-data-in' type='checkbox'><label for='data-637e4153-85d0-47a7-a5ee-f7faf7f12001' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  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<text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5ec7b9b6-7c51-428c-8dab-2ba5fb47eddd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ec7b9b6-7c51-428c-8dab-2ba5fb47eddd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0e28e5f-2c0e-42c9-a79c-a88bc2330b58' class='xr-var-data-in' type='checkbox'><label for='data-e0e28e5f-2c0e-42c9-a79c-a88bc2330b58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_sensible_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a329139c-4039-4c34-aca2-4cfa52f3f602' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a329139c-4039-4c34-aca2-4cfa52f3f602' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-685080c6-40e0-4f03-895c-c81b472f4403' class='xr-var-data-in' type='checkbox'><label for='data-685080c6-40e0-4f03-895c-c81b472f4403' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-393369dd-1a1a-47b8-bf5b-e1991d5014b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-393369dd-1a1a-47b8-bf5b-e1991d5014b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ea6b0db-43c0-488a-84ee-ebe6af913b7a' class='xr-var-data-in' type='checkbox'><label for='data-5ea6b0db-43c0-488a-84ee-ebe6af913b7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-03abaf99-7b5e-4350-b9b1-c33464341a2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03abaf99-7b5e-4350-b9b1-c33464341a2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92b03756-1170-404f-8b98-d4a5d6a1f66a' class='xr-var-data-in' type='checkbox'><label for='data-92b03756-1170-404f-8b98-d4a5d6a1f66a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-522bed44-ae6e-4e42-a63a-6f18911c62aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-522bed44-ae6e-4e42-a63a-6f18911c62aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d33449b-ba1e-4853-97ed-6fe2bf8a1b5f' class='xr-var-data-in' type='checkbox'><label for='data-8d33449b-ba1e-4853-97ed-6fe2bf8a1b5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_1</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-7b2cc906-e816-4625-992d-f710d604f9bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b2cc906-e816-4625-992d-f710d604f9bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-171f56f9-3918-4072-b132-7171861678c4' class='xr-var-data-in' type='checkbox'><label for='data-171f56f9-3918-4072-b132-7171861678c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_2</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  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font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-629ff378-180f-49b0-a51e-980ade2154bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-629ff378-180f-49b0-a51e-980ade2154bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77e47166-7cba-4a8d-ad36-dc7853f23f3b' class='xr-var-data-in' type='checkbox'><label for='data-77e47166-7cba-4a8d-ad36-dc7853f23f3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_3</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" 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0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-597d8e20-55c0-4d3a-9d5d-025127da52ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-597d8e20-55c0-4d3a-9d5d-025127da52ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6034c26-77c9-4742-8ba2-f08c34b66ab3' class='xr-var-data-in' type='checkbox'><label for='data-e6034c26-77c9-4742-8ba2-f08c34b66ab3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 4</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_4</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f20a0022-1038-4c28-b357-3267d6f21240' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f20a0022-1038-4c28-b357-3267d6f21240' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d088dee2-8b8e-4fa3-8e90-82de946ab2f5' class='xr-var-data-in' type='checkbox'><label for='data-d088dee2-8b8e-4fa3-8e90-82de946ab2f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-8ce69777-d36a-4b95-abb4-bbbcb9507b90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ce69777-d36a-4b95-abb4-bbbcb9507b90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-647b9523-736a-4602-9cc8-5a562415c4ab' class='xr-var-data-in' type='checkbox'><label for='data-647b9523-736a-4602-9cc8-5a562415c4ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5ce3fc6e-048d-4d32-84bd-65cdafd67ba7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ce3fc6e-048d-4d32-84bd-65cdafd67ba7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c871eec-14e5-4440-b5ed-991490c5ec84' class='xr-var-data-in' type='checkbox'><label for='data-6c871eec-14e5-4440-b5ed-991490c5ec84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-64c47e28-84ed-4513-8e6e-9a7a75489ff6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64c47e28-84ed-4513-8e6e-9a7a75489ff6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db9e10e4-d127-431a-87dc-83bc62e0b715' class='xr-var-data-in' type='checkbox'><label for='data-db9e10e4-d127-431a-87dc-83bc62e0b715' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_1</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5f6b2a79-d3ed-488f-9e7e-6df6e5cba7cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f6b2a79-d3ed-488f-9e7e-6df6e5cba7cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e667dda-eaee-4c42-bc3c-0d85b260ce63' class='xr-var-data-in' type='checkbox'><label for='data-0e667dda-eaee-4c42-bc3c-0d85b260ce63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_2</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-3babbe4f-9617-47f6-8358-88e88431720b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3babbe4f-9617-47f6-8358-88e88431720b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f4ad066-5d56-488f-bf34-9222b85ecd37' class='xr-var-data-in' type='checkbox'><label for='data-3f4ad066-5d56-488f-bf34-9222b85ecd37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_3</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c2875b65-6869-48d0-8b34-0a16cb479851' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2875b65-6869-48d0-8b34-0a16cb479851' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6d52773-2018-4214-ae0f-fa740e64c02c' class='xr-var-data-in' type='checkbox'><label for='data-e6d52773-2018-4214-ae0f-fa740e64c02c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 4</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_4</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-b3e10d08-c8b2-4b8f-848a-45ea43b6c229' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3e10d08-c8b2-4b8f-848a-45ea43b6c229' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f81c7393-febf-4945-82fa-81f97abb521c' class='xr-var-data-in' type='checkbox'><label for='data-f81c7393-febf-4945-82fa-81f97abb521c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-121d43f8-6826-40d5-b5d3-360a7b0e4686' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-121d43f8-6826-40d5-b5d3-360a7b0e4686' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51854346-65ad-45aa-9778-ee303a3189e8' class='xr-var-data-in' type='checkbox'><label for='data-51854346-65ad-45aa-9778-ee303a3189e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column ice water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_ice_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f69300b8-cda3-4aef-9ab2-16718377d77f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f69300b8-cda3-4aef-9ab2-16718377d77f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cf5fa5a-87bd-4385-bec3-1d66d9900a9f' class='xr-var-data-in' type='checkbox'><label for='data-5cf5fa5a-87bd-4385-bec3-1d66d9900a9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column liquid water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_liquid_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-faf5ce68-8c5a-4a5a-bac9-275fa8ba239f' class='xr-var-data-in' type='checkbox'><label for='data-faf5ce68-8c5a-4a5a-bac9-275fa8ba239f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column water vapor</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_water_vapor</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-6f4c0dfd-85e8-4655-823f-1cfd47b8d69f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f4c0dfd-85e8-4655-823f-1cfd47b8d69f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12ebda5e-736c-4862-ab2e-72b2d9a0be81' class='xr-var-data-in' type='checkbox'><label for='data-12ebda5e-736c-4862-ab2e-72b2d9a0be81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"34\" y2=\"6\" />\n  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y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 34.50101617379934,0.0 34.50101617379934,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"17.250508\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"54.501016\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,54.501016,60.000000)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-e803d08a-b8c3-4e5d-bb57-cde7c7549d3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e803d08a-b8c3-4e5d-bb57-cde7c7549d3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79ec794c-0c2f-47e5-b49c-b32901433ea8' class='xr-var-data-in' type='checkbox'><label for='data-79ec794c-0c2f-47e5-b49c-b32901433ea8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"34\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"34\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"34\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"34\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"34\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"34\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"34\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"34\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"34\" y2=\"54\" />\n  <line x1=\"0\" y1=\"63\" x2=\"34\" y2=\"63\" />\n  <line x1=\"0\" y1=\"69\" x2=\"34\" y2=\"69\" />\n  <line x1=\"0\" y1=\"75\" x2=\"34\" y2=\"75\" />\n  <line x1=\"0\" y1=\"81\" x2=\"34\" y2=\"81\" />\n  <line x1=\"0\" y1=\"87\" x2=\"34\" y2=\"87\" />\n  <line x1=\"0\" y1=\"93\" x2=\"34\" y2=\"93\" />\n  <line x1=\"0\" y1=\"99\" x2=\"34\" y2=\"99\" />\n  <line x1=\"0\" y1=\"105\" x2=\"34\" y2=\"105\" />\n  <line x1=\"0\" y1=\"111\" x2=\"34\" y2=\"111\" />\n  <line x1=\"0\" y1=\"120\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 34.50101617379934,0.0 34.50101617379934,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"17.250508\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"54.501016\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,54.501016,60.000000)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f418e7e4-f321-4c2a-b0eb-514c6a49b7fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f418e7e4-f321-4c2a-b0eb-514c6a49b7fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b60e65c2-68e1-4791-a02c-922d2f8543e5' class='xr-var-data-in' type='checkbox'><label for='data-b60e65c2-68e1-4791-a02c-922d2f8543e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>toa incident solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_incoming_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c24b7fb7-4750-4040-a595-76cc4e616919' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c24b7fb7-4750-4040-a595-76cc4e616919' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe8a9e16-65b3-4088-a9f8-d20de5fcd6f5' class='xr-var-data-in' type='checkbox'><label for='data-fe8a9e16-65b3-4088-a9f8-d20de5fcd6f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total precipitation (rain+snow)</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-987805cf-8107-411d-9214-40db160b7f08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-987805cf-8107-411d-9214-40db160b7f08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-700c5e9f-130b-49a0-bf8a-81a05868df83' class='xr-var-data-in' type='checkbox'><label for='data-700c5e9f-130b-49a0-bf8a-81a05868df83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>temperature of snow layer</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-314da8c8-a820-4134-a4ce-a8d5a69d48e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-314da8c8-a820-4134-a4ce-a8d5a69d48e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a71d23b-8f92-44f7-bb50-6034e0444667' class='xr-var-data-in' type='checkbox'><label for='data-0a71d23b-8f92-44f7-bb50-6034e0444667' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  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 <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-b89e6a0a-0180-41c9-8014-c5066972cd8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b89e6a0a-0180-41c9-8014-c5066972cd8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e834dc13-177f-4ffc-8999-723874f05499' class='xr-var-data-in' type='checkbox'><label for='data-e834dc13-177f-4ffc-8999-723874f05499' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f4e0620e-d378-4fec-81c5-e247e87da331' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4e0620e-d378-4fec-81c5-e247e87da331' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa56b1cf-25ed-4c0e-9e18-73e3156974fd' class='xr-var-data-in' type='checkbox'><label for='data-aa56b1cf-25ed-4c0e-9e18-73e3156974fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" 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x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-9430030c-d550-4cb5-87ae-f7c4d2dedf47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9430030c-d550-4cb5-87ae-f7c4d2dedf47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cb2a74f-bfc2-477d-9074-beba1bce0c8e' class='xr-var-data-in' type='checkbox'><label for='data-2cb2a74f-bfc2-477d-9074-beba1bce0c8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" 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for='section-44f71832-e339-4a28-b939-e5a4e4f2fd38' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db550f99-28c4-49e4-bd64-e9d2b71406b0' class='xr-index-data-in' type='checkbox'/><label for='index-db550f99-28c4-49e4-bd64-e9d2b71406b0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 15:00:00&#x27;, &#x27;2009-10-15 18:00:00&#x27;,\n               &#x27;2009-10-15 21:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n               &#x27;2009-10-16 03:00:00&#x27;, &#x27;2009-10-16 06:00:00&#x27;,\n               &#x27;2009-10-16 09:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n               &#x27;2009-10-16 15:00:00&#x27;, &#x27;2009-10-16 18:00:00&#x27;,\n               &#x27;2009-10-16 21:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n               &#x27;2009-10-17 03:00:00&#x27;, &#x27;2009-10-17 06:00:00&#x27;,\n               &#x27;2009-10-17 09:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n               &#x27;2009-10-17 15:00:00&#x27;, &#x27;2009-10-17 18:00:00&#x27;,\n               &#x27;2009-10-17 21:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n               &#x27;2009-10-18 03:00:00&#x27;, &#x27;2009-10-18 06:00:00&#x27;,\n               &#x27;2009-10-18 09:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n               &#x27;2009-10-18 15:00:00&#x27;, &#x27;2009-10-18 18:00:00&#x27;,\n               &#x27;2009-10-18 21:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n               &#x27;2009-10-19 03:00:00&#x27;, &#x27;2009-10-19 06:00:00&#x27;,\n               &#x27;2009-10-19 09:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n               &#x27;2009-10-19 15:00:00&#x27;, &#x27;2009-10-19 18:00:00&#x27;,\n               &#x27;2009-10-19 21:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n               &#x27;2009-10-20 03:00:00&#x27;, &#x27;2009-10-20 06:00:00&#x27;,\n               &#x27;2009-10-20 09:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-136a4085-e291-49ba-a426-e5f7f809b5d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-136a4085-e291-49ba-a426-e5f7f809b5d0' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_3h_reduced_sfc</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 10:55:20 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>dbac5691-165e-4465-bd75-e403aa400071</dd></dl></div></li></ul></div></div>"
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 7
-    },
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "earthkit.plots.quickmap.plot(\n",
+    "    ds_pl_q[\"q\"].rename(dict(time_counter=\"time\")).sel(\n",
+    "        time=\"2009-10-20T12:00:00\", pressure_levels=1e5,\n",
+    "    )[::10], x=\"lon\", y=\"lat\",\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2d66bcd-cb4b-4a03-970e-3ce49d68380e",
+   "metadata": {},
+   "source": [
+    "The surface level data contains all available parameters and can can be loaded as follows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5a574d92-3c0a-429b-97e8-d024d78c5daf",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "32f314be-e6dc-4a81-b12c-c740fcc043d9",
-      "cell_type": "markdown",
-      "source": "The data can be visualized as demonstrated below. For example, the 2 metre temperature on **2009-10-20 at 12:00:00 UTC** is plotted.",
-      "metadata": {}
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/HighResMIP_3h_reduced_sfc.nc\n"
+     ]
     },
     {
-      "id": "36e11d63-baec-4897-a5d1-26195cf5545f",
-      "cell_type": "code",
-      "source": "earthkit.plots.quickmap.plot(\n    ds_sfc[\"2t\"].rename(dict(time_counter=\"time\")).sel(\n        time=\"2009-10-20T12:00:00\",\n    )[::10], x=\"lon\", y=\"lat\",\n);",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 700x600 with 2 Axes>",
-            "image/png": 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-          "metadata": {}
-        }
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+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 95GB\n",
+       "Dimensions:              (time_counter: 40, cell: 6599680, nvertex: 4,\n",
+       "                          axis_nbounds: 2)\n",
+       "Coordinates:\n",
+       "    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n",
+       "    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n",
+       "  * time_counter         (time_counter) datetime64[ns] 320B 2009-10-15T15:00:...\n",
+       "    time_instant         (time_counter) datetime64[ns] 320B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "Dimensions without coordinates: cell, nvertex, axis_nbounds\n",
+       "Data variables: (12/49)\n",
+       "    10u                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
+       "    10v                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
+       "    2d                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
+       "    2t                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
+       "    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n",
+       "    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n",
+       "    ...                   ...\n",
+       "    tp                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
+       "    tsn                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
+       "    tsr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
+       "    tsrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
+       "    ttr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
+       "    ttrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    description:  Created by xios\n",
+       "    name:         HighResMIP_3h_reduced_sfc\n",
+       "    timeStamp:    2024-Apr-05 10:55:20 GMT\n",
+       "    title:        Created by xios\n",
+       "    uuid:         dbac5691-165e-4465-bd75-e403aa400071</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e2b79126-156b-4971-aa40-1f7fb1c848b8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e2b79126-156b-4971-aa40-1f7fb1c848b8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time_counter</span>: 40</li><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-098082a9-5e61-4791-88ea-dc6794b684a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-098082a9-5e61-4791-88ea-dc6794b684a8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-2d8bf04b-ffea-4d8a-8b11-77dc8ecfcb9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d8bf04b-ffea-4d8a-8b11-77dc8ecfcb9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-debdaf7f-90ca-499b-9309-f71a161d7022' class='xr-var-data-in' type='checkbox'><label for='data-debdaf7f-90ca-499b-9309-f71a161d7022' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 25.18 MiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (6599680,) </td>\n",
+       "                        <td> (824960,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-f645be90-f42a-4992-b922-4dfc016b7ba6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f645be90-f42a-4992-b922-4dfc016b7ba6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f12bb7ad-e881-4a93-a896-960aceb4e6d0' class='xr-var-data-in' type='checkbox'><label for='data-f12bb7ad-e881-4a93-a896-960aceb4e6d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 25.18 MiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (6599680,) </td>\n",
+       "                        <td> (824960,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T15:00:00 ... 2009-10-...</div><input id='attrs-2605a8bc-a9b5-49a2-a50a-926088d0ba57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2605a8bc-a9b5-49a2-a50a-926088d0ba57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cb07a8e-782f-4d1a-b021-8a5b3aa86454' class='xr-var-data-in' type='checkbox'><label for='data-5cb07a8e-782f-4d1a-b021-8a5b3aa86454' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T15:00:00.000000000&#x27;, &#x27;2009-10-15T18:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-15T21:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-16T03:00:00.000000000&#x27;, &#x27;2009-10-16T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-16T09:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-16T15:00:00.000000000&#x27;, &#x27;2009-10-16T18:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-16T21:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-17T03:00:00.000000000&#x27;, &#x27;2009-10-17T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-17T09:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-17T15:00:00.000000000&#x27;, &#x27;2009-10-17T18:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-17T21:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-18T03:00:00.000000000&#x27;, &#x27;2009-10-18T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-18T09:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-18T15:00:00.000000000&#x27;, &#x27;2009-10-18T18:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-18T21:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-19T03:00:00.000000000&#x27;, &#x27;2009-10-19T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-19T09:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-19T15:00:00.000000000&#x27;, &#x27;2009-10-19T18:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-19T21:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-20T03:00:00.000000000&#x27;, &#x27;2009-10-20T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2009-10-20T09:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-912895ab-5744-4a6a-92ce-c960664dd168' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-912895ab-5744-4a6a-92ce-c960664dd168' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-624716ce-dd69-4ca3-857f-94fed0763e5a' class='xr-var-data-in' type='checkbox'><label for='data-624716ce-dd69-4ca3-857f-94fed0763e5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 320 B </td>\n",
+       "                        <td> 8 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"80\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"30\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"30\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"30\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,30.311399683662877 0.0,30.311399683662877\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"50.311400\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >40</text>\n",
+       "  <text x=\"140.000000\" y=\"15.155700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,15.155700)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae47d7b9-fe7f-45ea-a255-1fb225804b0c' class='xr-section-summary-in' type='checkbox'  ><label for='section-ae47d7b9-fe7f-45ea-a255-1fb225804b0c' class='xr-section-summary' >Data variables: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-86f8230c-4e78-4330-9cf7-d1c269ac871a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86f8230c-4e78-4330-9cf7-d1c269ac871a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9fd55a1-7ad6-4dd5-911d-038a5daa527b' class='xr-var-data-in' type='checkbox'><label for='data-e9fd55a1-7ad6-4dd5-911d-038a5daa527b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>u-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
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+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
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+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
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+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
+       "  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
+       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
+       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
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+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-94e2738e-e816-4a6c-b8a0-f111ae62bddc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94e2738e-e816-4a6c-b8a0-f111ae62bddc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65fc61ac-006b-4bcf-8f0e-d2b827625b2d' class='xr-var-data-in' type='checkbox'><label for='data-65fc61ac-006b-4bcf-8f0e-d2b827625b2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>v-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-173a46ff-f5fc-4ad0-8922-8c5f7f0593b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-173a46ff-f5fc-4ad0-8922-8c5f7f0593b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d6abb52-89bf-4e89-81c4-f4d187ccce79' class='xr-var-data-in' type='checkbox'><label for='data-4d6abb52-89bf-4e89-81c4-f4d187ccce79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m dewpoint temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-6afa68b0-3d0f-4481-9cbf-13d1989cbb7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6afa68b0-3d0f-4481-9cbf-13d1989cbb7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58e41a57-4519-43fb-ac55-312d07b1ef66' class='xr-var-data-in' type='checkbox'><label for='data-58e41a57-4519-43fb-ac55-312d07b1ef66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m air temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-5a488474-8be9-4473-9bae-bc3c450aec3a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a488474-8be9-4473-9bae-bc3c450aec3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17b40d48-180e-4c40-afd3-63d050b649bb' class='xr-var-data-in' type='checkbox'><label for='data-17b40d48-180e-4c40-afd3-63d050b649bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 100.70 MiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (6599680, 4) </td>\n",
+       "                        <td> (206240, 4) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-d7ce6ada-3279-4dab-b793-f8c208faf313' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7ce6ada-3279-4dab-b793-f8c208faf313' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d3f1fbf-992f-4443-b778-66a8ed0a3389' class='xr-var-data-in' type='checkbox'><label for='data-5d3f1fbf-992f-4443-b778-66a8ed0a3389' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 100.70 MiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (6599680, 4) </td>\n",
+       "                        <td> (206240, 4) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-d13022c1-ec9f-4eff-9e24-f3e8d348a1ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d13022c1-ec9f-4eff-9e24-f3e8d348a1ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5e42c02-f2a6-499f-b623-1b1b625e4d9a' class='xr-var-data-in' type='checkbox'><label for='data-d5e42c02-f2a6-499f-b623-1b1b625e4d9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>convective precipitation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-542fa6ce-dadb-4395-858e-020f2e95fec0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-542fa6ce-dadb-4395-858e-020f2e95fec0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bffde147-ec0a-46ec-a692-412d56a8aab2' class='xr-var-data-in' type='checkbox'><label for='data-bffde147-ec0a-46ec-a692-412d56a8aab2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4f277d3e-36d3-4e1b-b4c1-e1544f1066d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f277d3e-36d3-4e1b-b4c1-e1544f1066d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-849ad489-f6a6-4355-98f2-aac2c418b194' class='xr-var-data-in' type='checkbox'><label for='data-849ad489-f6a6-4355-98f2-aac2c418b194' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a055dd72-e844-49a1-ab61-31dc9160bdcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a055dd72-e844-49a1-ab61-31dc9160bdcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc8a62ff-d14f-4862-8b9d-2c981a19a622' class='xr-var-data-in' type='checkbox'><label for='data-bc8a62ff-d14f-4862-8b9d-2c981a19a622' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>eastward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_eastward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-97e2311a-0f15-4536-a164-28e73f1bf0c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97e2311a-0f15-4536-a164-28e73f1bf0c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b98a218d-dd83-4b26-8299-743a35eff53a' class='xr-var-data-in' type='checkbox'><label for='data-b98a218d-dd83-4b26-8299-743a35eff53a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_vel</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-1b3b4226-8a8b-4ec4-afac-da8b0fff17e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b3b4226-8a8b-4ec4-afac-da8b0fff17e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae67002d-65d6-4556-8fb6-abb3eba3bf83' class='xr-var-data-in' type='checkbox'><label for='data-ae67002d-65d6-4556-8fb6-abb3eba3bf83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>northward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_northward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-9a8b7df4-858d-4430-8783-e579725bbd17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a8b7df4-858d-4430-8783-e579725bbd17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2660eafb-fa12-4236-9e6a-9bc8b84031d0' class='xr-var-data-in' type='checkbox'><label for='data-2660eafb-fa12-4236-9e6a-9bc8b84031d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a8469d2c-af14-4797-aa2c-4d2ec27cf11d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8469d2c-af14-4797-aa2c-4d2ec27cf11d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf5fe558-954f-487d-9cef-60352c5c8328' class='xr-var-data-in' type='checkbox'><label for='data-cf5fe558-954f-487d-9cef-60352c5c8328' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow density</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_density</dd><dt><span>units :</span></dt><dd>kg m-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-b513bbc5-24c4-490c-8e0e-3ef29adec6c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b513bbc5-24c4-490c-8e0e-3ef29adec6c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dca2ce8-8caf-45fc-9db4-ea43d17b5069' class='xr-var-data-in' type='checkbox'><label for='data-1dca2ce8-8caf-45fc-9db4-ea43d17b5069' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow depth</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-efb1c85c-045b-43a5-a9e6-6a1fd5fb5a49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efb1c85c-045b-43a5-a9e6-6a1fd5fb5a49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bce49b71-51bb-4c4d-8076-7599e74adf39' class='xr-var-data-in' type='checkbox'><label for='data-bce49b71-51bb-4c4d-8076-7599e74adf39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snowfall</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-357373c0-db53-4a20-9472-e518d0b918fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-357373c0-db53-4a20-9472-e518d0b918fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f20d7b6d-39bc-489f-9622-695ce64371e9' class='xr-var-data-in' type='checkbox'><label for='data-f20d7b6d-39bc-489f-9622-695ce64371e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>skin temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>sea_surface_skin_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-036c2139-54b7-46b7-b2f1-bb26b9674c79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-036c2139-54b7-46b7-b2f1-bb26b9674c79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2460536-7c1d-4124-9f24-30d5dcd7e572' class='xr-var-data-in' type='checkbox'><label for='data-c2460536-7c1d-4124-9f24-30d5dcd7e572' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_latent_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5f118b58-321e-42bd-89e3-8e928e618777' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f118b58-321e-42bd-89e3-8e928e618777' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc12d01d-ad98-4bcc-ab79-80b466a3aa20' class='xr-var-data-in' type='checkbox'><label for='data-bc12d01d-ad98-4bcc-ab79-80b466a3aa20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow melt</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_snow_melt_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-d5222ae1-a84f-44f4-a1f1-37d39ac7aa8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5222ae1-a84f-44f4-a1f1-37d39ac7aa8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9713551d-a8dc-4288-a804-8123404eacf9' class='xr-var-data-in' type='checkbox'><label for='data-9713551d-a8dc-4288-a804-8123404eacf9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0f87fb97-0929-48c4-a087-f0d7a0b73315' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f87fb97-0929-48c4-a087-f0d7a0b73315' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-637e4153-85d0-47a7-a5ee-f7faf7f12001' class='xr-var-data-in' type='checkbox'><label for='data-637e4153-85d0-47a7-a5ee-f7faf7f12001' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5ec7b9b6-7c51-428c-8dab-2ba5fb47eddd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ec7b9b6-7c51-428c-8dab-2ba5fb47eddd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0e28e5f-2c0e-42c9-a79c-a88bc2330b58' class='xr-var-data-in' type='checkbox'><label for='data-e0e28e5f-2c0e-42c9-a79c-a88bc2330b58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_sensible_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a329139c-4039-4c34-aca2-4cfa52f3f602' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a329139c-4039-4c34-aca2-4cfa52f3f602' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-685080c6-40e0-4f03-895c-c81b472f4403' class='xr-var-data-in' type='checkbox'><label for='data-685080c6-40e0-4f03-895c-c81b472f4403' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-393369dd-1a1a-47b8-bf5b-e1991d5014b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-393369dd-1a1a-47b8-bf5b-e1991d5014b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ea6b0db-43c0-488a-84ee-ebe6af913b7a' class='xr-var-data-in' type='checkbox'><label for='data-5ea6b0db-43c0-488a-84ee-ebe6af913b7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-03abaf99-7b5e-4350-b9b1-c33464341a2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03abaf99-7b5e-4350-b9b1-c33464341a2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92b03756-1170-404f-8b98-d4a5d6a1f66a' class='xr-var-data-in' type='checkbox'><label for='data-92b03756-1170-404f-8b98-d4a5d6a1f66a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "    <tr>\n",
+       "        <td>\n",
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+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-7b2cc906-e816-4625-992d-f710d604f9bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b2cc906-e816-4625-992d-f710d604f9bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-171f56f9-3918-4072-b132-7171861678c4' class='xr-var-data-in' type='checkbox'><label for='data-171f56f9-3918-4072-b132-7171861678c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_2</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-629ff378-180f-49b0-a51e-980ade2154bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-629ff378-180f-49b0-a51e-980ade2154bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77e47166-7cba-4a8d-ad36-dc7853f23f3b' class='xr-var-data-in' type='checkbox'><label for='data-77e47166-7cba-4a8d-ad36-dc7853f23f3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_3</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "    <tr>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f20a0022-1038-4c28-b357-3267d6f21240' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f20a0022-1038-4c28-b357-3267d6f21240' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d088dee2-8b8e-4fa3-8e90-82de946ab2f5' class='xr-var-data-in' type='checkbox'><label for='data-d088dee2-8b8e-4fa3-8e90-82de946ab2f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-8ce69777-d36a-4b95-abb4-bbbcb9507b90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ce69777-d36a-4b95-abb4-bbbcb9507b90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-647b9523-736a-4602-9cc8-5a562415c4ab' class='xr-var-data-in' type='checkbox'><label for='data-647b9523-736a-4602-9cc8-5a562415c4ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5ce3fc6e-048d-4d32-84bd-65cdafd67ba7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ce3fc6e-048d-4d32-84bd-65cdafd67ba7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c871eec-14e5-4440-b5ed-991490c5ec84' class='xr-var-data-in' type='checkbox'><label for='data-6c871eec-14e5-4440-b5ed-991490c5ec84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-64c47e28-84ed-4513-8e6e-9a7a75489ff6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64c47e28-84ed-4513-8e6e-9a7a75489ff6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db9e10e4-d127-431a-87dc-83bc62e0b715' class='xr-var-data-in' type='checkbox'><label for='data-db9e10e4-d127-431a-87dc-83bc62e0b715' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_1</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5f6b2a79-d3ed-488f-9e7e-6df6e5cba7cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f6b2a79-d3ed-488f-9e7e-6df6e5cba7cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e667dda-eaee-4c42-bc3c-0d85b260ce63' class='xr-var-data-in' type='checkbox'><label for='data-0e667dda-eaee-4c42-bc3c-0d85b260ce63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_2</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-3babbe4f-9617-47f6-8358-88e88431720b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3babbe4f-9617-47f6-8358-88e88431720b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f4ad066-5d56-488f-bf34-9222b85ecd37' class='xr-var-data-in' type='checkbox'><label for='data-3f4ad066-5d56-488f-bf34-9222b85ecd37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_3</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c2875b65-6869-48d0-8b34-0a16cb479851' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2875b65-6869-48d0-8b34-0a16cb479851' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6d52773-2018-4214-ae0f-fa740e64c02c' class='xr-var-data-in' type='checkbox'><label for='data-e6d52773-2018-4214-ae0f-fa740e64c02c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 4</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_4</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-b3e10d08-c8b2-4b8f-848a-45ea43b6c229' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3e10d08-c8b2-4b8f-848a-45ea43b6c229' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f81c7393-febf-4945-82fa-81f97abb521c' class='xr-var-data-in' type='checkbox'><label for='data-f81c7393-febf-4945-82fa-81f97abb521c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "        </td>\n",
+       "        <td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-121d43f8-6826-40d5-b5d3-360a7b0e4686' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-121d43f8-6826-40d5-b5d3-360a7b0e4686' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51854346-65ad-45aa-9778-ee303a3189e8' class='xr-var-data-in' type='checkbox'><label for='data-51854346-65ad-45aa-9778-ee303a3189e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column ice water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_ice_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f69300b8-cda3-4aef-9ab2-16718377d77f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f69300b8-cda3-4aef-9ab2-16718377d77f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cf5fa5a-87bd-4385-bec3-1d66d9900a9f' class='xr-var-data-in' type='checkbox'><label for='data-5cf5fa5a-87bd-4385-bec3-1d66d9900a9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column liquid water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_liquid_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcwv</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-06f6d7d3-64db-4987-be4f-8ac84d5775d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06f6d7d3-64db-4987-be4f-8ac84d5775d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faf5ce68-8c5a-4a5a-bac9-275fa8ba239f' class='xr-var-data-in' type='checkbox'><label for='data-faf5ce68-8c5a-4a5a-bac9-275fa8ba239f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column water vapor</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_water_vapor</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-6f4c0dfd-85e8-4655-823f-1cfd47b8d69f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f4c0dfd-85e8-4655-823f-1cfd47b8d69f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12ebda5e-736c-4862-ab2e-72b2d9a0be81' class='xr-var-data-in' type='checkbox'><label for='data-12ebda5e-736c-4862-ab2e-72b2d9a0be81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 640 B </td>\n",
+       "                        <td> 16 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 2) </td>\n",
+       "                        <td> (1, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-e803d08a-b8c3-4e5d-bb57-cde7c7549d3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e803d08a-b8c3-4e5d-bb57-cde7c7549d3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79ec794c-0c2f-47e5-b49c-b32901433ea8' class='xr-var-data-in' type='checkbox'><label for='data-79ec794c-0c2f-47e5-b49c-b32901433ea8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 640 B </td>\n",
+       "                        <td> 16 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 2) </td>\n",
+       "                        <td> (1, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f418e7e4-f321-4c2a-b0eb-514c6a49b7fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f418e7e4-f321-4c2a-b0eb-514c6a49b7fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b60e65c2-68e1-4791-a02c-922d2f8543e5' class='xr-var-data-in' type='checkbox'><label for='data-b60e65c2-68e1-4791-a02c-922d2f8543e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>toa incident solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_incoming_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c24b7fb7-4750-4040-a595-76cc4e616919' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c24b7fb7-4750-4040-a595-76cc4e616919' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe8a9e16-65b3-4088-a9f8-d20de5fcd6f5' class='xr-var-data-in' type='checkbox'><label for='data-fe8a9e16-65b3-4088-a9f8-d20de5fcd6f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total precipitation (rain+snow)</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-987805cf-8107-411d-9214-40db160b7f08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-987805cf-8107-411d-9214-40db160b7f08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-700c5e9f-130b-49a0-bf8a-81a05868df83' class='xr-var-data-in' type='checkbox'><label for='data-700c5e9f-130b-49a0-bf8a-81a05868df83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>temperature of snow layer</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-314da8c8-a820-4134-a4ce-a8d5a69d48e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-314da8c8-a820-4134-a4ce-a8d5a69d48e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a71d23b-8f92-44f7-bb50-6034e0444667' class='xr-var-data-in' type='checkbox'><label for='data-0a71d23b-8f92-44f7-bb50-6034e0444667' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-b89e6a0a-0180-41c9-8014-c5066972cd8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b89e6a0a-0180-41c9-8014-c5066972cd8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e834dc13-177f-4ffc-8999-723874f05499' class='xr-var-data-in' type='checkbox'><label for='data-e834dc13-177f-4ffc-8999-723874f05499' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f4e0620e-d378-4fec-81c5-e247e87da331' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4e0620e-d378-4fec-81c5-e247e87da331' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa56b1cf-25ed-4c0e-9e18-73e3156974fd' class='xr-var-data-in' type='checkbox'><label for='data-aa56b1cf-25ed-4c0e-9e18-73e3156974fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-9430030c-d550-4cb5-87ae-f7c4d2dedf47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9430030c-d550-4cb5-87ae-f7c4d2dedf47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cb2a74f-bfc2-477d-9074-beba1bce0c8e' class='xr-var-data-in' type='checkbox'><label for='data-2cb2a74f-bfc2-477d-9074-beba1bce0c8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.97 GiB </td>\n",
+       "                        <td> 3.15 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (40, 6599680) </td>\n",
+       "                        <td> (1, 412480) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-44f71832-e339-4a28-b939-e5a4e4f2fd38' class='xr-section-summary-in' type='checkbox'  ><label for='section-44f71832-e339-4a28-b939-e5a4e4f2fd38' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db550f99-28c4-49e4-bd64-e9d2b71406b0' class='xr-index-data-in' type='checkbox'/><label for='index-db550f99-28c4-49e4-bd64-e9d2b71406b0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 15:00:00&#x27;, &#x27;2009-10-15 18:00:00&#x27;,\n",
+       "               &#x27;2009-10-15 21:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n",
+       "               &#x27;2009-10-16 03:00:00&#x27;, &#x27;2009-10-16 06:00:00&#x27;,\n",
+       "               &#x27;2009-10-16 09:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n",
+       "               &#x27;2009-10-16 15:00:00&#x27;, &#x27;2009-10-16 18:00:00&#x27;,\n",
+       "               &#x27;2009-10-16 21:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n",
+       "               &#x27;2009-10-17 03:00:00&#x27;, &#x27;2009-10-17 06:00:00&#x27;,\n",
+       "               &#x27;2009-10-17 09:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n",
+       "               &#x27;2009-10-17 15:00:00&#x27;, &#x27;2009-10-17 18:00:00&#x27;,\n",
+       "               &#x27;2009-10-17 21:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n",
+       "               &#x27;2009-10-18 03:00:00&#x27;, &#x27;2009-10-18 06:00:00&#x27;,\n",
+       "               &#x27;2009-10-18 09:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n",
+       "               &#x27;2009-10-18 15:00:00&#x27;, &#x27;2009-10-18 18:00:00&#x27;,\n",
+       "               &#x27;2009-10-18 21:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n",
+       "               &#x27;2009-10-19 03:00:00&#x27;, &#x27;2009-10-19 06:00:00&#x27;,\n",
+       "               &#x27;2009-10-19 09:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n",
+       "               &#x27;2009-10-19 15:00:00&#x27;, &#x27;2009-10-19 18:00:00&#x27;,\n",
+       "               &#x27;2009-10-19 21:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n",
+       "               &#x27;2009-10-20 03:00:00&#x27;, &#x27;2009-10-20 06:00:00&#x27;,\n",
+       "               &#x27;2009-10-20 09:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-136a4085-e291-49ba-a426-e5f7f809b5d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-136a4085-e291-49ba-a426-e5f7f809b5d0' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_3h_reduced_sfc</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 10:55:20 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>dbac5691-165e-4465-bd75-e403aa400071</dd></dl></div></li></ul></div></div>"
       ],
-      "execution_count": 8
-    },
+      "text/plain": [
+       "<xarray.Dataset> Size: 95GB\n",
+       "Dimensions:              (time_counter: 40, cell: 6599680, nvertex: 4,\n",
+       "                          axis_nbounds: 2)\n",
+       "Coordinates:\n",
+       "    lat                  (cell) float32 26MB dask.array<chunksize=(824960,), meta=np.ndarray>\n",
+       "    lon                  (cell) float32 26MB dask.array<chunksize=(824960,), meta=np.ndarray>\n",
+       "  * time_counter         (time_counter) datetime64[ns] 320B 2009-10-15T15:00:...\n",
+       "    time_instant         (time_counter) datetime64[ns] 320B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "Dimensions without coordinates: cell, nvertex, axis_nbounds\n",
+       "Data variables: (12/49)\n",
+       "    10u                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
+       "    10v                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
+       "    2d                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
+       "    2t                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
+       "    bounds_lat           (cell, nvertex) float32 106MB dask.array<chunksize=(206240, 4), meta=np.ndarray>\n",
+       "    bounds_lon           (cell, nvertex) float32 106MB dask.array<chunksize=(206240, 4), meta=np.ndarray>\n",
+       "    ...                   ...\n",
+       "    tp                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
+       "    tsn                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
+       "    tsr                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
+       "    tsrc                 (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
+       "    ttr                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
+       "    ttrc                 (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.6\n",
+       "    description:  Created by xios\n",
+       "    name:         HighResMIP_3h_reduced_sfc\n",
+       "    timeStamp:    2024-Apr-05 10:55:20 GMT\n",
+       "    title:        Created by xios\n",
+       "    uuid:         dbac5691-165e-4465-bd75-e403aa400071"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_sfc = load_OpenIFS_data(leveltype=\"sfc\")\n",
+    "ds_sfc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32f314be-e6dc-4a81-b12c-c740fcc043d9",
+   "metadata": {},
+   "source": [
+    "The data can be visualized as demonstrated below. For example, the 2 metre temperature on **2009-10-20 at 12:00:00 UTC** is plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "36e11d63-baec-4897-a5d1-26195cf5545f",
+   "metadata": {},
+   "outputs": [
     {
-      "id": "e909c9f3-42f8-46d6-8d04-8f0a15106545",
-      "cell_type": "code",
-      "source": "",
-      "metadata": {
-        "trusted": true
-      },
-      "outputs": [],
-      "execution_count": null
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ]
-}
\ No newline at end of file
+   ],
+   "source": [
+    "earthkit.plots.quickmap.plot(\n",
+    "    ds_sfc[\"2t\"].rename(dict(time_counter=\"time\")).sel(\n",
+    "        time=\"2009-10-20T12:00:00\",\n",
+    "    )[::10], x=\"lon\", y=\"lat\",\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e909c9f3-42f8-46d6-8d04-8f0a15106545",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/04-example-datasets/03-NextGEMS.ipynb b/04-example-datasets/03-NextGEMS.ipynb
new file mode 100644
index 0000000..5bbc8e2
--- /dev/null
+++ b/04-example-datasets/03-NextGEMS.ipynb
@@ -0,0 +1,1307 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
+   "metadata": {},
+   "source": [
+    "# NextGEMS data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
+   "metadata": {},
+   "source": [
+    "> [!NOTE]\n",
+    "> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n",
+    "\n",
+    "> [!CAUTION]\n",
+    "> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "463bf2c0-ac3f-4786-9687-362393802dce",
+   "metadata": {},
+   "source": [
+    "This notebook is a comprehensive guide to accessing Zarr directory for an ICON simulation within the 4th model cycle phase of the NextGEMS project. It includes details about the available parameters and steps, along with instructions for using the `load_NextGEMS_data` function to access these data from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/EW3_ICON_ngc4008_90d_Compression_test.zarr/). The Zarr data is not downloaded locally; instead, it is accessed remotely using `zarr`.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Experiment Details and Available Data Fields\n",
+    "\n",
+    "The dataset comprises output data from an ICON run within the 4th model cycle phase of the NextGEMS project (NGC4008). This simulation spans a total of 30 years and uses an ssp370-like scenario run from 2020.\n",
+    "The simulation was performed using a horizontal grid spacing of approximately 100 km (R2B8/8). \n",
+    "However, only a subset of the data is provided: three months of data for four selected variables at three model levels.\n",
+    "A global, coupled simulation covering a time period of 1000 years, starting in 1300, was run. 48-hourly output data was generated throughout the simulation.\n",
+    "The data is stored on a HEALPix grid with lossless compression, and the total size of the available dataset is approximately 9 GB.\n",
+    "\n",
+    "The dataset is contained in a Zarr directory.\n",
+    "\n",
+    "The following 2D parameters are available:\n",
+    "```python\n",
+    "[\"hus\", \"pr\" \"ua\", \"va\"]\n",
+    "```\n",
+    "The following model levels are available:\n",
+    "```python\n",
+    "[89, 65, 55]\n",
+    "```\n",
+    "\n",
+    "\n",
+    "The available time steps begin at 2020-01-02 00:00:00 UTC, increasing in 2-day steps until 2020-03-31 00:00:00 UTC.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
+   "metadata": {},
+   "source": [
+    "## Using the `load_NextGEMS_data` Function\n",
+    "\n",
+    "To load the Zarr directory, you can use the following function:\n",
+    "\n",
+    "```python\n",
+    "load_NextGEMS_data()\n",
+    "```\n",
+    "\n",
+    "This function is straightforward and does not require any parameters.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# URL of S3 bucket\n",
+    "BASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
+   "metadata": {},
+   "source": [
+    "First, the package `zarr` (modern dataset format that is specifically designed for chunked access) and its dependencies need to be imported for the remote access."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import fsspec\n",
+    "import zarr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7b8524c-1fc2-4fd1-94dc-5c9233adae6b",
+   "metadata": {},
+   "source": [
+    "The `load_NextGEMS_data` function simplifies remote access to Zarr datasets stored on the S3 bucket. It loads the data efficiently as an xarray dataset.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "0b393415-87ca-4dc7-8c72-4cdd101d6621",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "import xarray as xr\n",
+    "\n",
+    "\n",
+    "def load_NextGEMS_data():\n",
+    "\n",
+    "    url = f\"{BASE_URL}/EW3_ICON_ngc4008_90d_Compression_test.zarr\"\n",
+    "\n",
+    "    print(f\"Loading dataset {url}\")\n",
+    "\n",
+    "    return xr.open_dataset( url, engine=\"zarr\", chunks=dict(),)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62adb1fd-1fd1-4c59-a0c3-66710f4a31d1",
+   "metadata": {},
+   "source": [
+    "## Example Usage\n",
+    "\n",
+    "To load the Zarr directory, you can use the following example. This will load the whole dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "510bad61-a98c-495b-a9fa-365887b9f1b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/EW3_ICON_ngc4008_90d_Compression_test.zarr\n"
+     ]
+    },
+    {
+     "data": {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:     (time: 90, level_full: 3, cell: 3145728)\n",
+       "Coordinates:\n",
+       "  * level_full  (level_full) float64 89.0 65.0 55.0\n",
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+       "    pr          (time, cell) float32 dask.array&lt;chunksize=(3, 196608), meta=np.ndarray&gt;\n",
+       "    ua          (time, level_full, cell) float32 dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;\n",
+       "    va          (time, level_full, cell) float32 dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0fa66641-78df-42f3-b664-a84c5cd05bc2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0fa66641-78df-42f3-b664-a84c5cd05bc2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 90</li><li><span class='xr-has-index'>level_full</span>: 3</li><li><span>cell</span>: 3145728</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-12f8e8a4-d3f4-4b67-9675-51297b97c835' class='xr-section-summary-in' type='checkbox'  checked><label for='section-12f8e8a4-d3f4-4b67-9675-51297b97c835' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level_full</span></div><div class='xr-var-dims'>(level_full)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.0 65.0 55.0</div><input id='attrs-a83b4f42-5cda-4a2f-9901-47affcded87e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a83b4f42-5cda-4a2f-9901-47affcded87e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4ef6f6f-7fe6-4f89-b0b3-284aaad5baad' class='xr-var-data-in' type='checkbox'><label for='data-a4ef6f6f-7fe6-4f89-b0b3-284aaad5baad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>generalized_height</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array([89., 65., 55.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-02 ... 2020-03-31</div><input id='attrs-9eede9bb-a6f1-4dfe-a886-0541c6240bac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9eede9bb-a6f1-4dfe-a886-0541c6240bac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfd65b85-bf30-4cef-85a7-45204ee91014' class='xr-var-data-in' type='checkbox'><label for='data-cfd65b85-bf30-4cef-85a7-45204ee91014' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-02T00:00:00.000000000&#x27;, &#x27;2020-01-03T00:00:00.000000000&#x27;,\n",
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+       "       &#x27;2020-03-20T00:00:00.000000000&#x27;, &#x27;2020-03-21T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-22T00:00:00.000000000&#x27;, &#x27;2020-03-23T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-24T00:00:00.000000000&#x27;, &#x27;2020-03-25T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-26T00:00:00.000000000&#x27;, &#x27;2020-03-27T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-28T00:00:00.000000000&#x27;, &#x27;2020-03-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-30T00:00:00.000000000&#x27;, &#x27;2020-03-31T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa73597f-2de8-4fc0-9804-a75986ef72ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa73597f-2de8-4fc0-9804-a75986ef72ac' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>hus</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-4e6755ef-f930-4c1e-9fb7-89dabecd22cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e6755ef-f930-4c1e-9fb7-89dabecd22cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21903b11-166f-4c53-b3e3-d55736ddc878' class='xr-var-data-in' type='checkbox'><label for='data-21903b11-166f-4c53-b3e3-d55736ddc878' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.16 GiB </td>\n",
+       "                        <td> 2.25 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (90, 3, 3145728) </td>\n",
+       "                        <td> (3, 1, 196608) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1440 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 196608), meta=np.ndarray&gt;</div><input id='attrs-00281b6b-d8f9-43d1-98e3-a0320d7ec5c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00281b6b-d8f9-43d1-98e3-a0320d7ec5c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89e0fa4f-a00d-4761-8464-4649c0cb7da2' class='xr-var-data-in' type='checkbox'><label for='data-89e0fa4f-a00d-4761-8464-4649c0cb7da2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>precipitation flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>vgrid :</span></dt><dd>surface</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.05 GiB </td>\n",
+       "                        <td> 2.25 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (90, 3145728) </td>\n",
+       "                        <td> (3, 196608) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 480 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ua</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-a53a7e6a-4ae8-4420-9434-e60132a6fbb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a53a7e6a-4ae8-4420-9434-e60132a6fbb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-786b4b4c-e873-4ef0-9752-b6ee0fb271a1' class='xr-var-data-in' type='checkbox'><label for='data-786b4b4c-e873-4ef0-9752-b6ee0fb271a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Zonal wind</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.16 GiB </td>\n",
+       "                        <td> 2.25 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (90, 3, 3145728) </td>\n",
+       "                        <td> (3, 1, 196608) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1440 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>va</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-4eac3111-d49b-406a-a9e9-9a3dcb9b9844' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4eac3111-d49b-406a-a9e9-9a3dcb9b9844' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23d64f36-22d2-4bea-ab2e-4c2a8beb1f32' class='xr-var-data-in' type='checkbox'><label for='data-23d64f36-22d2-4bea-ab2e-4c2a8beb1f32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Meridional wind</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.16 GiB </td>\n",
+       "                        <td> 2.25 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (90, 3, 3145728) </td>\n",
+       "                        <td> (3, 1, 196608) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1440 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "               &#x27;2020-01-06&#x27;, &#x27;2020-01-07&#x27;, &#x27;2020-01-08&#x27;, &#x27;2020-01-09&#x27;,\n",
+       "               &#x27;2020-01-10&#x27;, &#x27;2020-01-11&#x27;, &#x27;2020-01-12&#x27;, &#x27;2020-01-13&#x27;,\n",
+       "               &#x27;2020-01-14&#x27;, &#x27;2020-01-15&#x27;, &#x27;2020-01-16&#x27;, &#x27;2020-01-17&#x27;,\n",
+       "               &#x27;2020-01-18&#x27;, &#x27;2020-01-19&#x27;, &#x27;2020-01-20&#x27;, &#x27;2020-01-21&#x27;,\n",
+       "               &#x27;2020-01-22&#x27;, &#x27;2020-01-23&#x27;, &#x27;2020-01-24&#x27;, &#x27;2020-01-25&#x27;,\n",
+       "               &#x27;2020-01-26&#x27;, &#x27;2020-01-27&#x27;, &#x27;2020-01-28&#x27;, &#x27;2020-01-29&#x27;,\n",
+       "               &#x27;2020-01-30&#x27;, &#x27;2020-01-31&#x27;, &#x27;2020-02-01&#x27;, &#x27;2020-02-02&#x27;,\n",
+       "               &#x27;2020-02-03&#x27;, &#x27;2020-02-04&#x27;, &#x27;2020-02-05&#x27;, &#x27;2020-02-06&#x27;,\n",
+       "               &#x27;2020-02-07&#x27;, &#x27;2020-02-08&#x27;, &#x27;2020-02-09&#x27;, &#x27;2020-02-10&#x27;,\n",
+       "               &#x27;2020-02-11&#x27;, &#x27;2020-02-12&#x27;, &#x27;2020-02-13&#x27;, &#x27;2020-02-14&#x27;,\n",
+       "               &#x27;2020-02-15&#x27;, &#x27;2020-02-16&#x27;, &#x27;2020-02-17&#x27;, &#x27;2020-02-18&#x27;,\n",
+       "               &#x27;2020-02-19&#x27;, &#x27;2020-02-20&#x27;, &#x27;2020-02-21&#x27;, &#x27;2020-02-22&#x27;,\n",
+       "               &#x27;2020-02-23&#x27;, &#x27;2020-02-24&#x27;, &#x27;2020-02-25&#x27;, &#x27;2020-02-26&#x27;,\n",
+       "               &#x27;2020-02-27&#x27;, &#x27;2020-02-28&#x27;, &#x27;2020-02-29&#x27;, &#x27;2020-03-01&#x27;,\n",
+       "               &#x27;2020-03-02&#x27;, &#x27;2020-03-03&#x27;, &#x27;2020-03-04&#x27;, &#x27;2020-03-05&#x27;,\n",
+       "               &#x27;2020-03-06&#x27;, &#x27;2020-03-07&#x27;, &#x27;2020-03-08&#x27;, &#x27;2020-03-09&#x27;,\n",
+       "               &#x27;2020-03-10&#x27;, &#x27;2020-03-11&#x27;, &#x27;2020-03-12&#x27;, &#x27;2020-03-13&#x27;,\n",
+       "               &#x27;2020-03-14&#x27;, &#x27;2020-03-15&#x27;, &#x27;2020-03-16&#x27;, &#x27;2020-03-17&#x27;,\n",
+       "               &#x27;2020-03-18&#x27;, &#x27;2020-03-19&#x27;, &#x27;2020-03-20&#x27;, &#x27;2020-03-21&#x27;,\n",
+       "               &#x27;2020-03-22&#x27;, &#x27;2020-03-23&#x27;, &#x27;2020-03-24&#x27;, &#x27;2020-03-25&#x27;,\n",
+       "               &#x27;2020-03-26&#x27;, &#x27;2020-03-27&#x27;, &#x27;2020-03-28&#x27;, &#x27;2020-03-29&#x27;,\n",
+       "               &#x27;2020-03-30&#x27;, &#x27;2020-03-31&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68410ad6-eb50-4ca5-b62a-c0d5ed5e6c13' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68410ad6-eb50-4ca5-b62a-c0d5ed5e6c13' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:     (time: 90, level_full: 3, cell: 3145728)\n",
+       "Coordinates:\n",
+       "  * level_full  (level_full) float64 89.0 65.0 55.0\n",
+       "  * time        (time) datetime64[ns] 2020-01-02 2020-01-03 ... 2020-03-31\n",
+       "Dimensions without coordinates: cell\n",
+       "Data variables:\n",
+       "    hus         (time, level_full, cell) float32 dask.array<chunksize=(3, 1, 196608), meta=np.ndarray>\n",
+       "    pr          (time, cell) float32 dask.array<chunksize=(3, 196608), meta=np.ndarray>\n",
+       "    ua          (time, level_full, cell) float32 dask.array<chunksize=(3, 1, 196608), meta=np.ndarray>\n",
+       "    va          (time, level_full, cell) float32 dask.array<chunksize=(3, 1, 196608), meta=np.ndarray>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = load_NextGEMS_data()\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4521020-47fd-47e9-8254-f83321c920c4",
+   "metadata": {},
+   "source": [
+    "Since tha data is stored on a HEALPix grid `earthkit` cannot be used to plot this data and the `pyicon` library is used here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "962a9a11-d103-4588-ade3-4373c847540c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyicon as pyic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99449502-9fb2-4097-89b0-d63634a0e250",
+   "metadata": {},
+   "source": [
+    "The data can be visualized as demonstrated below. For example, the specific humidity on **2020-02-01 at 00:00:00 UTC** at model level 89 is plotted.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "d792634e-3a22-4e7d-92f8-f81f66eabbde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ecm5945/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/pyicon/pyicon_tb.py:451: PerformanceWarning: Slicing with an out-of-order index is generating 332 times more chunks\n",
+      "  arr_interp = xr.DataArray(arr.data[pix].reshape(Lon.shape), dims=['lat', 'lon'], coords=dict(lat=lat, lon=lon))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<GeoAxesSubplot:title={'left':'level_full = 89.0 ','right':'2020-02-01T00:00:00'}>,\n",
+       " [<cartopy.mpl.geocollection.GeoQuadMesh at 0x33d4150c0>,\n",
+       "  <matplotlib.colorbar.Colorbar at 0x33d638f40>])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 751.967x480.314 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da_hus = ds.hus.isel(time=30, level_full=0)\n",
+    "da_hus.pyic.plot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7106bc6e-e25e-412c-8aff-5692134155c6",
+   "metadata": {},
+   "source": [
+    "Zonal and Meridional wind can be visualized in the same way.\n",
+    "\n",
+    "\n",
+    "The precipitation flux on **2020-03-31 at 00:00:00 UTC** is plotted as demonstrated below.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "b0371f7d-f7bf-4dd7-be17-830de8e24db9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ecm5945/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/pyicon/pyicon_tb.py:451: PerformanceWarning: Slicing with an out-of-order index is generating 332 times more chunks\n",
+      "  arr_interp = xr.DataArray(arr.data[pix].reshape(Lon.shape), dims=['lat', 'lon'], coords=dict(lat=lat, lon=lon))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<GeoAxesSubplot:title={'right':'2020-03-31T00:00:00'}>,\n",
+       " [<cartopy.mpl.geocollection.GeoQuadMesh at 0x33d41e6b0>,\n",
+       "  <matplotlib.colorbar.Colorbar at 0x3322c7100>])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 751.967x480.314 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "da_pr = ds.pr.isel(time=89)\n",
+    "da_pr.pyic.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f820694-e442-487d-981a-4fbd238a806e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/04-example-datasets/04-ICONXPP.ipynb b/04-example-datasets/04-ICONXPP.ipynb
new file mode 100644
index 0000000..d65a780
--- /dev/null
+++ b/04-example-datasets/04-ICONXPP.ipynb
@@ -0,0 +1,7465 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
+   "metadata": {},
+   "source": [
+    "# ICON-XPP data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
+   "metadata": {},
+   "source": [
+    "> [!NOTE]\n",
+    "> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n",
+    "\n",
+    "> [!CAUTION]\n",
+    "> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "463bf2c0-ac3f-4786-9687-362393802dce",
+   "metadata": {},
+   "source": [
+    "This notebook is a comprehensive guide to accessing NetDCF files for an ICON-eXtendedPrediction and Projection (XPP) simulation. It includes details about the available parameters and steps, along with instructions for using the `load_ICONXPP_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/ICON-XPP/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Experiment Details and Available Data Fields\n",
+    "\n",
+    "The dataset consists of output data from an ICON-XPP run, a current state-of-the-art climate-scale simulation.\n",
+    "The simulation was conducted using a horizontal grid spacings of about 80km (ICON grid R2B5).\n",
+    "A global, coupled simulation covering a time period of 1000 years, starting in 1300, was run. Monthly output data was generated throughout the simulation. \n",
+    "The total volume of the available dataset is about 147 GB.\n",
+    "\n",
+    "The dataset is organized into multiple NetCDF files, with each file containing five years of simulation output along with all available parameters.\n",
+    "\n",
+    "The following 2D parameters are available:\n",
+    "```python\n",
+    "[\"pres_msl\", \"pres_sfc\", \"t_s\", \"clct\", \"tot_prec_rate\", \"tqv\", \"tqv_dia\", \"tqc_dia\", \"tqi_dia\", \"umfl_s\", \"vmfl_s\", \"sp_10m\", \"t_2m\", \"sob_t\", \"sod_t\", \"sou_t\", \"thb_t\", \"sob_s\", \"sou_s\", \"thb_s\", \"shfl_s\", \"lhfl_s\", \"snow_con_rate\", \"snow_gsp_rate\", \"ice_gsp_rate\", \"qifl_s\", \"qhfl_s\", \"t_seasfc\", \"fr_land(ncells) ;\", \"fr_seaice\", \"condhf_ice\", \"meltpot_ice\", \"t_ice\", \"h_ice\", \"albvisdif\", \"albvisdir\", \"albnirdif\", \"albnirdir\"]\n",
+    "```\n",
+    "\n",
+    "The available time steps begin at 1300-02-01 00:00:00 UTC, increasing in 1-month steps until 2300-01-01 00:00:00 UTC.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
+   "metadata": {},
+   "source": [
+    "## Using the `load_ICONXPP_data` Function\n",
+    "\n",
+    "To load the NetCDF files, you can use the following function:\n",
+    "\n",
+    "```python\n",
+    "load_ICONXPP_data(year=None, month=None)\n",
+    "```\n",
+    "\n",
+    "### Parameters\n",
+    "\n",
+    "- **`year`**: An integer specifying the year for which data should be loaded. Available years: 1300 to 2300.\n",
+    "- **`month`**(optional): An integer specifying the month for which data should be loaded.\n",
+    "\n",
+    "The datasets start on February 1st for years ending in `00` or `05`. If you need data for January 1st of these years, be sure to specify the appropriate month to load the correct dataset.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# URL of S3 bucket\n",
+    "BASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
+   "metadata": {},
+   "source": [
+    "First, the package `zarr` (modern dataset format that is specifically designed for chunked access) and its dependencies need to be imported for the remote access."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import aiohttp\n",
+    "import dask\n",
+    "import fsspec\n",
+    "import zarr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7b8524c-1fc2-4fd1-94dc-5c9233adae6b",
+   "metadata": {},
+   "source": [
+    "The `load_ICONXPP_data` function simplifies remote access to NetCDF datasets stored on the S3 bucket. It validates input parameters, constructs the correct URL for the requested data, and loads it efficiently as an xarray dataset using the corresponding `.ref` file.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "0b393415-87ca-4dc7-8c72-4cdd101d6621",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "import requests\n",
+    "import xarray as xr\n",
+    "\n",
+    "\n",
+    "def load_ICONXPP_data(year=None, month=None):\n",
+    "    \n",
+    "    if not isinstance(year, int) or year < 1300 or year > 2300:\n",
+    "        raise ValueError(\"Please specify a valid year as an integer between 1300 and 2300.\")\n",
+    "\n",
+    "    if year == 2300:\n",
+    "        if not month:\n",
+    "            print(f\"Warning: Only data for January 1st is available for 2300.\")\n",
+    "        if month and month != 1:\n",
+    "            raise ValueError(f\"Only data for January 1st is available for 2300.\")\n",
+    "            \n",
+    "    if month:\n",
+    "        if not isinstance(month, int) or month < 1 or month > 12:\n",
+    "            raise ValueError(f\"Please specify a valid month as an integer between 1 and 12.\")\n",
+    "\n",
+    "    file_year = (year // 10) * 10\n",
+    "    if not year % 10 <= 4: \n",
+    "        file_year += 5\n",
+    "\n",
+    "\n",
+    "    if not month and year % 5 == 0:\n",
+    "        if year == 1300:\n",
+    "            print(f\"Warning: Data for January 1st of 1300 is not available as is is the simulation starting time.\")\n",
+    "        else:\n",
+    "            print(f\"Warning: Data for January 1st of '{year}' is stored in the previous file. Please specify the month to load the correct dataset.\")\n",
+    "    elif month == 1 and year % 5 == 0:\n",
+    "        if year == 1300:\n",
+    "            raise ValueError(f\"Data for January 1st of 1300 is not available as this is the simulation starting time.\")\n",
+    "        file_year -= 5\n",
+    "        \n",
+    "    if year == 2300:\n",
+    "        file_year = 2295\n",
+    "\n",
+    "    url = f\"{BASE_URL}/ICON-XPP/slo1802_atm_2d_ml_{file_year}0101T000000Z.nc\"\n",
+    "        \n",
+    "    ref = requests.get(f\"{url}.ref\").json()\n",
+    "\n",
+    "    print(f\"Loading dataset {url}\")\n",
+    "\n",
+    "    return xr.open_dataset(\n",
+    "        \"reference://\", \n",
+    "        engine=\"zarr\", \n",
+    "        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n",
+    "        consolidated=False, \n",
+    "        chunks=dict(),\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62adb1fd-1fd1-4c59-a0c3-66710f4a31d1",
+   "metadata": {},
+   "source": [
+    "## Example Usage\n",
+    "\n",
+    "To load NetCDF file for pressure level parameters, you can use the following example. This will load the data for years 2020 to 2025. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "510bad61-a98c-495b-a9fa-365887b9f1b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/ICON-XPP/slo1802_atm_2d_ml_20200101T000000Z.nc\n"
+     ]
+    },
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+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\n",
+       "Coordinates:\n",
+       "    clat           (ncells) float64 dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n",
+       "    clon           (ncells) float64 dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n",
+       "  * height         (height) float64 10.0\n",
+       "  * height_2       (height_2) float64 2.0\n",
+       "  * time           (time) datetime64[ns] 2020-02-01 2020-03-01 ... 2025-01-01\n",
+       "Dimensions without coordinates: ncells, vertices\n",
+       "Data variables: (12/40)\n",
+       "    albnirdif      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    albnirdir      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    albvisdif      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    albvisdir      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    clat_bnds      (ncells, vertices) float64 dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n",
+       "    clct           (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    ...             ...\n",
+       "    tqc_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    tqi_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    tqv            (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    tqv_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    umfl_s         (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    vmfl_s         (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n",
+       "    Conventions:          CF-1.6\n",
+       "    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n",
+       "    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n",
+       "    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n",
+       "    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n",
+       "    number_of_grid_used:  30\n",
+       "    references:           see MPIM/DWD publications\n",
+       "    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n",
+       "    title:                ICON simulation\n",
+       "    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a37c7394-7017-44ec-828c-e155050aeb23' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a37c7394-7017-44ec-828c-e155050aeb23' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7409a493-541f-4bc6-843c-d790f1774dd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7409a493-541f-4bc6-843c-d790f1774dd8' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-c49475fb-1310-4439-b30c-7bb3819495aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c49475fb-1310-4439-b30c-7bb3819495aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8018c7c-a394-445a-bb40-18bbf23f631a' class='xr-var-data-in' type='checkbox'><label for='data-f8018c7c-a394-445a-bb40-18bbf23f631a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 640.00 kiB </td>\n",
+       "                        <td> 640.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (81920,) </td>\n",
+       "                        <td> (81920,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-abff35d4-4a95-4a18-ac5e-5505c8fbc7e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abff35d4-4a95-4a18-ac5e-5505c8fbc7e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-173ff085-b81c-4b9d-b002-59969ced0ba1' class='xr-var-data-in' type='checkbox'><label for='data-173ff085-b81c-4b9d-b002-59969ced0ba1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 640.00 kiB </td>\n",
+       "                        <td> 640.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (81920,) </td>\n",
+       "                        <td> (81920,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-509db153-9b87-49fc-9c46-75d670c16e01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-509db153-9b87-49fc-9c46-75d670c16e01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbf020a2-cca3-48b9-aa6e-1c4d2daf0288' class='xr-var-data-in' type='checkbox'><label for='data-cbf020a2-cca3-48b9-aa6e-1c4d2daf0288' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-d79dbe36-58cc-48b0-bf74-5f1cf8eff22e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d79dbe36-58cc-48b0-bf74-5f1cf8eff22e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8cd0f1b-88f7-4866-99e5-0f3871abe655' class='xr-var-data-in' type='checkbox'><label for='data-d8cd0f1b-88f7-4866-99e5-0f3871abe655' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-01 ... 2025-01-01</div><input id='attrs-aa9ff3ff-de0c-42df-a8f6-5c3796cff6f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa9ff3ff-de0c-42df-a8f6-5c3796cff6f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4b83c01-a6a8-445c-9fde-1cc41d833778' class='xr-var-data-in' type='checkbox'><label for='data-c4b83c01-a6a8-445c-9fde-1cc41d833778' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;, &#x27;2020-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-01T00:00:00.000000000&#x27;, &#x27;2020-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-08-01T00:00:00.000000000&#x27;, &#x27;2020-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-10-01T00:00:00.000000000&#x27;, &#x27;2020-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-12-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2021-02-01T00:00:00.000000000&#x27;, &#x27;2021-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2021-04-01T00:00:00.000000000&#x27;, &#x27;2021-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2021-06-01T00:00:00.000000000&#x27;, &#x27;2021-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2021-08-01T00:00:00.000000000&#x27;, &#x27;2021-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2021-10-01T00:00:00.000000000&#x27;, &#x27;2021-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2021-12-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-02-01T00:00:00.000000000&#x27;, &#x27;2022-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-04-01T00:00:00.000000000&#x27;, &#x27;2022-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-06-01T00:00:00.000000000&#x27;, &#x27;2022-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-08-01T00:00:00.000000000&#x27;, &#x27;2022-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-10-01T00:00:00.000000000&#x27;, &#x27;2022-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-12-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-02-01T00:00:00.000000000&#x27;, &#x27;2023-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-04-01T00:00:00.000000000&#x27;, &#x27;2023-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-08-01T00:00:00.000000000&#x27;, &#x27;2023-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-10-01T00:00:00.000000000&#x27;, &#x27;2023-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-12-01T00:00:00.000000000&#x27;, &#x27;2024-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-02-01T00:00:00.000000000&#x27;, &#x27;2024-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-04-01T00:00:00.000000000&#x27;, &#x27;2024-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-06-01T00:00:00.000000000&#x27;, &#x27;2024-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-08-01T00:00:00.000000000&#x27;, &#x27;2024-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-10-01T00:00:00.000000000&#x27;, &#x27;2024-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2024-12-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b665a772-2655-4a90-8fdd-11fcabd812ff' class='xr-section-summary-in' type='checkbox'  ><label for='section-b665a772-2655-4a90-8fdd-11fcabd812ff' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b62d873a-3713-46c4-ac2b-d6798dea9ee4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b62d873a-3713-46c4-ac2b-d6798dea9ee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9a3b4b3-3da7-419e-bb5b-14dd7c91674f' class='xr-var-data-in' type='checkbox'><label for='data-a9a3b4b3-3da7-419e-bb5b-14dd7c91674f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8ae7d6a2-f3fe-4b7e-8e90-ae8b08884114' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ae7d6a2-f3fe-4b7e-8e90-ae8b08884114' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dd56738-9415-4f3e-b989-ac05a53a967c' class='xr-var-data-in' type='checkbox'><label for='data-5dd56738-9415-4f3e-b989-ac05a53a967c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-dcdbf2d1-043a-4535-a27e-efa609b69ba4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcdbf2d1-043a-4535-a27e-efa609b69ba4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c581252a-cfb6-401c-ab2b-6ff407dc5b6a' class='xr-var-data-in' type='checkbox'><label for='data-c581252a-cfb6-401c-ab2b-6ff407dc5b6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-863d189e-c7fc-4160-9063-75ca1c371402' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-863d189e-c7fc-4160-9063-75ca1c371402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f8c6622-3a83-4e16-945e-c9f9f3cfa166' class='xr-var-data-in' type='checkbox'><label for='data-5f8c6622-3a83-4e16-945e-c9f9f3cfa166' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-1a156868-12cc-4098-8c09-f7e770bb305e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a156868-12cc-4098-8c09-f7e770bb305e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3ac053a-3c11-49eb-8b29-ba8b771cda0d' class='xr-var-data-in' type='checkbox'><label for='data-b3ac053a-3c11-49eb-8b29-ba8b771cda0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.88 MiB </td>\n",
+       "                        <td> 1.88 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (81920, 3) </td>\n",
+       "                        <td> (81920, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a196810f-6500-4a1e-ae6e-4d0fb1bab414' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a196810f-6500-4a1e-ae6e-4d0fb1bab414' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f2cf741-b7a4-47ca-aaa7-0d05797074b4' class='xr-var-data-in' type='checkbox'><label for='data-4f2cf741-b7a4-47ca-aaa7-0d05797074b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-f34e36b3-6210-41a6-ba82-81f8bf6010f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f34e36b3-6210-41a6-ba82-81f8bf6010f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58c610a9-567e-4cc7-b6fe-b1c54a4d3927' class='xr-var-data-in' type='checkbox'><label for='data-58c610a9-567e-4cc7-b6fe-b1c54a4d3927' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.88 MiB </td>\n",
+       "                        <td> 1.88 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (81920, 3) </td>\n",
+       "                        <td> (81920, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-401d958f-5b8e-4059-9df7-616c8c9653c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-401d958f-5b8e-4059-9df7-616c8c9653c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23e0e799-1e93-410d-884e-b7c8a0fc0fd9' class='xr-var-data-in' type='checkbox'><label for='data-23e0e799-1e93-410d-884e-b7c8a0fc0fd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-45fb88d4-970b-4d2d-bebf-8176f808acb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45fb88d4-970b-4d2d-bebf-8176f808acb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f17088b-0f50-4244-8106-ce1d5c4a9f94' class='xr-var-data-in' type='checkbox'><label for='data-6f17088b-0f50-4244-8106-ce1d5c4a9f94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (81920,) </td>\n",
+       "                        <td> (81920,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-bcc956f1-ac10-4020-86f3-e031ebe05ac6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcc956f1-ac10-4020-86f3-e031ebe05ac6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcb59d15-7120-4c1c-9179-51d16096c82d' class='xr-var-data-in' type='checkbox'><label for='data-fcb59d15-7120-4c1c-9179-51d16096c82d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5006ebae-c24e-4cdb-92c4-4d5d0c4e1fe3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5006ebae-c24e-4cdb-92c4-4d5d0c4e1fe3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-272b46b9-4c36-48fd-9843-d6e878c3356c' class='xr-var-data-in' type='checkbox'><label for='data-272b46b9-4c36-48fd-9843-d6e878c3356c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2cbf5e0e-be97-48fe-8810-f5a2203effd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cbf5e0e-be97-48fe-8810-f5a2203effd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0191bd32-a66d-4ccc-a36e-ef0664f28178' class='xr-var-data-in' type='checkbox'><label for='data-0191bd32-a66d-4ccc-a36e-ef0664f28178' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
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+       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
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+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-0db33c46-74cd-43a5-955a-f317db4a2079' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0db33c46-74cd-43a5-955a-f317db4a2079' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-088199c8-2a01-4d4c-989a-bed7fe671a11' class='xr-var-data-in' type='checkbox'><label for='data-088199c8-2a01-4d4c-989a-bed7fe671a11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-01772518-ba80-4df0-8246-26d990710ac0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01772518-ba80-4df0-8246-26d990710ac0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dc9a5b3-228c-4b0a-ab7f-cec5af31348f' class='xr-var-data-in' type='checkbox'><label for='data-5dc9a5b3-228c-4b0a-ab7f-cec5af31348f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-27d10837-2932-4b6d-9a2d-361d1b8616c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27d10837-2932-4b6d-9a2d-361d1b8616c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ce21909-8908-470f-92d8-eb183798c555' class='xr-var-data-in' type='checkbox'><label for='data-7ce21909-8908-470f-92d8-eb183798c555' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
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+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ed6d7eab-3cf7-4aac-8e54-78682962e334' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed6d7eab-3cf7-4aac-8e54-78682962e334' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f21af715-4366-43b0-a895-063ef072f18b' class='xr-var-data-in' type='checkbox'><label for='data-f21af715-4366-43b0-a895-063ef072f18b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
+       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-47ca8f79-a6a9-4761-862e-81f9b796e885' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47ca8f79-a6a9-4761-862e-81f9b796e885' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e41cdea-3344-40dd-906f-759f97f405ab' class='xr-var-data-in' type='checkbox'><label for='data-7e41cdea-3344-40dd-906f-759f97f405ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d58bb3ef-2b72-452f-aef8-94c2f63e7182' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d58bb3ef-2b72-452f-aef8-94c2f63e7182' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc7e1c47-bf51-4fbe-be3b-5bd7b4c2671a' class='xr-var-data-in' type='checkbox'><label for='data-fc7e1c47-bf51-4fbe-be3b-5bd7b4c2671a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-29a2bcda-657f-44ed-8ff8-6bb7ee8588eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29a2bcda-657f-44ed-8ff8-6bb7ee8588eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-388deb59-e1cb-46c8-b91f-6df74de096ed' class='xr-var-data-in' type='checkbox'><label for='data-388deb59-e1cb-46c8-b91f-6df74de096ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-11811f08-be68-4a55-adb9-311fdac9c03f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11811f08-be68-4a55-adb9-311fdac9c03f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-484d9166-a6d4-4032-8245-dcbd07a37cca' class='xr-var-data-in' type='checkbox'><label for='data-484d9166-a6d4-4032-8245-dcbd07a37cca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-da07d793-d743-4918-a361-e49c0fe78396' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da07d793-d743-4918-a361-e49c0fe78396' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fc5aece-665a-4951-a239-398c47f5d7f8' class='xr-var-data-in' type='checkbox'><label for='data-9fc5aece-665a-4951-a239-398c47f5d7f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1a9b92d8-4704-4b31-b3be-b5f8dcb69ec6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a9b92d8-4704-4b31-b3be-b5f8dcb69ec6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cb820cb-15e1-4e3f-8121-c16b67897a70' class='xr-var-data-in' type='checkbox'><label for='data-3cb820cb-15e1-4e3f-8121-c16b67897a70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-432c10b8-1c24-4190-88ec-5066dc12f272' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-432c10b8-1c24-4190-88ec-5066dc12f272' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6288871-3f52-410f-ac36-2d0b3486c97e' class='xr-var-data-in' type='checkbox'><label for='data-e6288871-3f52-410f-ac36-2d0b3486c97e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-316d1e02-5ba1-4410-9da1-590a9d28144a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-316d1e02-5ba1-4410-9da1-590a9d28144a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b82bb90-e876-495c-96f0-dc5a821cce30' class='xr-var-data-in' type='checkbox'><label for='data-6b82bb90-e876-495c-96f0-dc5a821cce30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
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+       "\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f62f10f8-1c93-43e7-8be3-54c97a5bec4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f62f10f8-1c93-43e7-8be3-54c97a5bec4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52071f28-8c55-4410-b16e-9353e37ae7e4' class='xr-var-data-in' type='checkbox'><label for='data-52071f28-8c55-4410-b16e-9353e37ae7e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-14c82697-6def-4f21-95be-8ddf4a69b061' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14c82697-6def-4f21-95be-8ddf4a69b061' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff7c9e37-2f23-4e93-b780-dfcf3044bd69' class='xr-var-data-in' type='checkbox'><label for='data-ff7c9e37-2f23-4e93-b780-dfcf3044bd69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp_10m</span></div><div class='xr-var-dims'>(time, height, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1aac3c11-5b6e-47bd-a2ba-9bb308773569' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1aac3c11-5b6e-47bd-a2ba-9bb308773569' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d57dd4e-4ed5-4bfa-b3a8-bf3b861f8e2e' class='xr-var-data-in' type='checkbox'><label for='data-3d57dd4e-4ed5-4bfa-b3a8-bf3b861f8e2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>wind speed in 10m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.0</dd><dt><span>standard_name :</span></dt><dd>sp_10m</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 1, 81920) </td>\n",
+       "                        <td> (1, 1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8d928bd7-264c-49ea-adc4-19f0fe108408' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d928bd7-264c-49ea-adc4-19f0fe108408' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e306701-2fd9-4312-8707-faf05c537d88' class='xr-var-data-in' type='checkbox'><label for='data-3e306701-2fd9-4312-8707-faf05c537d88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d61f4415-54e0-4d4a-b2f6-a33cf476c917' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d61f4415-54e0-4d4a-b2f6-a33cf476c917' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2abc5415-0dea-423d-81f9-8f55007f29ec' class='xr-var-data-in' type='checkbox'><label for='data-2abc5415-0dea-423d-81f9-8f55007f29ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-21022fda-1530-4e39-b3e5-b0b8bdcd9291' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21022fda-1530-4e39-b3e5-b0b8bdcd9291' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-292a9aef-12bf-42f4-8731-2e1aa9e931cd' class='xr-var-data-in' type='checkbox'><label for='data-292a9aef-12bf-42f4-8731-2e1aa9e931cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
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+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2c5dc45a-69f9-4906-b98b-bfc232ee7d33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c5dc45a-69f9-4906-b98b-bfc232ee7d33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e249d6b2-e94f-4c16-bc91-a05c72bd3797' class='xr-var-data-in' type='checkbox'><label for='data-e249d6b2-e94f-4c16-bc91-a05c72bd3797' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
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+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1237eef7-8584-4850-9272-ad928cc5cab2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1237eef7-8584-4850-9272-ad928cc5cab2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44009e32-8852-462f-9971-7b4b6c33a680' class='xr-var-data-in' type='checkbox'><label for='data-44009e32-8852-462f-9971-7b4b6c33a680' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-068c62b2-5ca1-4882-b631-a2cf0ba69ace' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-068c62b2-5ca1-4882-b631-a2cf0ba69ace' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8aa078b-70a1-4849-986f-0de50fad507d' class='xr-var-data-in' type='checkbox'><label for='data-a8aa078b-70a1-4849-986f-0de50fad507d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
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+       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4317796d-d418-44ac-a86a-13558dd3a064' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4317796d-d418-44ac-a86a-13558dd3a064' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e7b6a5d-bbfb-4794-a4da-8c63f22383ac' class='xr-var-data-in' type='checkbox'><label for='data-2e7b6a5d-bbfb-4794-a4da-8c63f22383ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
+       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-29cb2849-f93a-4ba4-86c8-4263ab1c95f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29cb2849-f93a-4ba4-86c8-4263ab1c95f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-326489e1-c117-4a4c-a4d9-c0f31c1c4cac' class='xr-var-data-in' type='checkbox'><label for='data-326489e1-c117-4a4c-a4d9-c0f31c1c4cac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-444e2194-978c-48fc-b53b-d7d31fa02bea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-444e2194-978c-48fc-b53b-d7d31fa02bea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3dd18e8-3cde-414b-a326-f8aa924fb17e' class='xr-var-data-in' type='checkbox'><label for='data-f3dd18e8-3cde-414b-a326-f8aa924fb17e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4981f804-849f-46b8-9362-f4bcaddcabef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4981f804-849f-46b8-9362-f4bcaddcabef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8bc098d-f354-4bc8-a4ce-26baba7f97ac' class='xr-var-data-in' type='checkbox'><label for='data-e8bc098d-f354-4bc8-a4ce-26baba7f97ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "  <!-- Vertical lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ae098b48-fc41-4bb5-b2c7-11358abb8b5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae098b48-fc41-4bb5-b2c7-11358abb8b5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d0a6478-81d3-4fc8-adde-d30b5f0db687' class='xr-var-data-in' type='checkbox'><label for='data-8d0a6478-81d3-4fc8-adde-d30b5f0db687' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a145e69e-6a3e-4274-9309-6a9c72552923' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a145e69e-6a3e-4274-9309-6a9c72552923' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f6a7153-de7f-48db-be27-938973c2b1e7' class='xr-var-data-in' type='checkbox'><label for='data-4f6a7153-de7f-48db-be27-938973c2b1e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
+       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-55fd2e7f-f3f9-4aee-9fe3-278fbdd1a093' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55fd2e7f-f3f9-4aee-9fe3-278fbdd1a093' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25609a10-1e3c-4811-a291-a65da07c8d46' class='xr-var-data-in' type='checkbox'><label for='data-25609a10-1e3c-4811-a291-a65da07c8d46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
+       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-3bacf534-9bbe-4456-a5c7-b2052c3ef8a1' class='xr-section-summary-in' type='checkbox'  ><label for='section-3bacf534-9bbe-4456-a5c7-b2052c3ef8a1' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4b2141d1-92e6-4005-b302-334450fa13a3' class='xr-index-data-in' type='checkbox'/><label for='index-4b2141d1-92e6-4005-b302-334450fa13a3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4f369337-a1c0-47f9-8afe-f698b9cb5a05' class='xr-index-data-in' type='checkbox'/><label for='index-4f369337-a1c0-47f9-8afe-f698b9cb5a05' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a1d1b1e8-2477-4e11-86aa-8188119f69a0' class='xr-index-data-in' type='checkbox'/><label for='index-a1d1b1e8-2477-4e11-86aa-8188119f69a0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-02-01&#x27;, &#x27;2020-03-01&#x27;, &#x27;2020-04-01&#x27;, &#x27;2020-05-01&#x27;,\n",
+       "               &#x27;2020-06-01&#x27;, &#x27;2020-07-01&#x27;, &#x27;2020-08-01&#x27;, &#x27;2020-09-01&#x27;,\n",
+       "               &#x27;2020-10-01&#x27;, &#x27;2020-11-01&#x27;, &#x27;2020-12-01&#x27;, &#x27;2021-01-01&#x27;,\n",
+       "               &#x27;2021-02-01&#x27;, &#x27;2021-03-01&#x27;, &#x27;2021-04-01&#x27;, &#x27;2021-05-01&#x27;,\n",
+       "               &#x27;2021-06-01&#x27;, &#x27;2021-07-01&#x27;, &#x27;2021-08-01&#x27;, &#x27;2021-09-01&#x27;,\n",
+       "               &#x27;2021-10-01&#x27;, &#x27;2021-11-01&#x27;, &#x27;2021-12-01&#x27;, &#x27;2022-01-01&#x27;,\n",
+       "               &#x27;2022-02-01&#x27;, &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;,\n",
+       "               &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;,\n",
+       "               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n",
+       "               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;, &#x27;2023-05-01&#x27;,\n",
+       "               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;, &#x27;2023-08-01&#x27;, &#x27;2023-09-01&#x27;,\n",
+       "               &#x27;2023-10-01&#x27;, &#x27;2023-11-01&#x27;, &#x27;2023-12-01&#x27;, &#x27;2024-01-01&#x27;,\n",
+       "               &#x27;2024-02-01&#x27;, &#x27;2024-03-01&#x27;, &#x27;2024-04-01&#x27;, &#x27;2024-05-01&#x27;,\n",
+       "               &#x27;2024-06-01&#x27;, &#x27;2024-07-01&#x27;, &#x27;2024-08-01&#x27;, &#x27;2024-09-01&#x27;,\n",
+       "               &#x27;2024-10-01&#x27;, &#x27;2024-11-01&#x27;, &#x27;2024-12-01&#x27;, &#x27;2025-01-01&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-86d0ea22-ec28-4617-86b8-3819f504d7a2' class='xr-section-summary-in' type='checkbox'  ><label for='section-86d0ea22-ec28-4617-86b8-3819f504d7a2' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 044638</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\n",
+       "Coordinates:\n",
+       "    clat           (ncells) float64 dask.array<chunksize=(81920,), meta=np.ndarray>\n",
+       "    clon           (ncells) float64 dask.array<chunksize=(81920,), meta=np.ndarray>\n",
+       "  * height         (height) float64 10.0\n",
+       "  * height_2       (height_2) float64 2.0\n",
+       "  * time           (time) datetime64[ns] 2020-02-01 2020-03-01 ... 2025-01-01\n",
+       "Dimensions without coordinates: ncells, vertices\n",
+       "Data variables: (12/40)\n",
+       "    albnirdif      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    albnirdir      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    albvisdif      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    albvisdir      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    clat_bnds      (ncells, vertices) float64 dask.array<chunksize=(81920, 3), meta=np.ndarray>\n",
+       "    clct           (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    ...             ...\n",
+       "    tqc_dia        (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    tqi_dia        (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    tqv            (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    tqv_dia        (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    umfl_s         (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    vmfl_s         (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n",
+       "    Conventions:          CF-1.6\n",
+       "    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n",
+       "    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n",
+       "    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n",
+       "    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n",
+       "    number_of_grid_used:  30\n",
+       "    references:           see MPIM/DWD publications\n",
+       "    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n",
+       "    title:                ICON simulation\n",
+       "    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_2020 = load_ICONXPP_data(year=2024)\n",
+    "ds_2020"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4521020-47fd-47e9-8254-f83321c920c4",
+   "metadata": {},
+   "source": [
+    "The `earthkit` package is used for plotting the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "962a9a11-d103-4588-ade3-4373c847540c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import earthkit.plots\n",
+    "import earthkit.plots.quickmap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99449502-9fb2-4097-89b0-d63634a0e250",
+   "metadata": {},
+   "source": [
+    "The data can be visualized as demonstrated below. For example, the 2 metre temperature on **2024-12-01 at 00:00:00 UTC** is plotted. `[::10]` applies slicing to the data being plotted (syntax: `[start:stop:step]`) in order to reduce computational load by selecting only every 10th data point.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d792634e-3a22-4e7d-92f8-f81f66eabbde",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<earthkit.plots.components.maps.Map at 0x17b1d9e70>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "earthkit.plots.quickmap.plot(\n",
+    "    ds_2020[\"t_2m\"].sel(time=np.datetime64(\"2024-12-01 00:00:00\"))[::10], \n",
+    "    x=\"clon\", y=\"clat\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ee0a8b1-6ecf-4306-99b0-93f8e65b164b",
+   "metadata": {},
+   "source": [
+    "If interested in data on **2020-01-01 at 00:00:00 UTC** the previous NetCDF file needs to be loaded. This will automatically be done by specifying the month as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "30d45387-9af5-4a4b-b4bd-d026f04e644d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/ICON-XPP/slo1802_atm_2d_ml_20150101T000000Z.nc\n"
+     ]
+    },
+    {
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+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\n",
+       "Coordinates:\n",
+       "    clat           (ncells) float64 dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n",
+       "    clon           (ncells) float64 dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n",
+       "  * height         (height) float64 10.0\n",
+       "  * height_2       (height_2) float64 2.0\n",
+       "  * time           (time) datetime64[ns] 2015-02-01 2015-03-01 ... 2020-01-01\n",
+       "Dimensions without coordinates: ncells, vertices\n",
+       "Data variables: (12/40)\n",
+       "    albnirdif      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    albnirdir      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    albvisdif      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    albvisdir      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    clat_bnds      (ncells, vertices) float64 dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n",
+       "    clct           (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    ...             ...\n",
+       "    tqc_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    tqi_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    tqv            (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    tqv_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    umfl_s         (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "    vmfl_s         (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n",
+       "    Conventions:          CF-1.6\n",
+       "    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n",
+       "    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n",
+       "    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n",
+       "    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n",
+       "    number_of_grid_used:  30\n",
+       "    references:           see MPIM/DWD publications\n",
+       "    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n",
+       "    title:                ICON simulation\n",
+       "    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-baf985aa-89da-44df-9907-8f8af73af912' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-baf985aa-89da-44df-9907-8f8af73af912' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-77321b0d-10dd-4974-a72a-9b26edfde0bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-77321b0d-10dd-4974-a72a-9b26edfde0bf' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-4009552f-0132-4ece-b003-d7373f88136b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4009552f-0132-4ece-b003-d7373f88136b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fae88e02-8bba-4f5a-85f9-b0cf90ec0faa' class='xr-var-data-in' type='checkbox'><label for='data-fae88e02-8bba-4f5a-85f9-b0cf90ec0faa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 640.00 kiB </td>\n",
+       "                        <td> 640.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (81920,) </td>\n",
+       "                        <td> (81920,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-8a6da4de-86fa-4ea0-81e3-1c13f42833cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a6da4de-86fa-4ea0-81e3-1c13f42833cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a9d9265-a8f7-45c9-863d-7803d043f581' class='xr-var-data-in' type='checkbox'><label for='data-6a9d9265-a8f7-45c9-863d-7803d043f581' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 640.00 kiB </td>\n",
+       "                        <td> 640.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (81920,) </td>\n",
+       "                        <td> (81920,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-6c66adc7-3e3a-4816-ba4b-5a08a4c0a4db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c66adc7-3e3a-4816-ba4b-5a08a4c0a4db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7af8a6e-cf35-4ced-943f-805285a858a2' class='xr-var-data-in' type='checkbox'><label for='data-a7af8a6e-cf35-4ced-943f-805285a858a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-01e6a118-275e-4829-8657-5678341665f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01e6a118-275e-4829-8657-5678341665f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3549940-875d-4526-a10b-2e836b6fc84f' class='xr-var-data-in' type='checkbox'><label for='data-a3549940-875d-4526-a10b-2e836b6fc84f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-02-01 ... 2020-01-01</div><input id='attrs-ea859484-57e9-4353-b285-725e1253ec91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea859484-57e9-4353-b285-725e1253ec91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1889b282-f6d1-4bb4-b810-e7ecda75eaa0' class='xr-var-data-in' type='checkbox'><label for='data-1889b282-f6d1-4bb4-b810-e7ecda75eaa0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2016-12-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2017-02-01T00:00:00.000000000&#x27;, &#x27;2017-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2017-04-01T00:00:00.000000000&#x27;, &#x27;2017-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2017-06-01T00:00:00.000000000&#x27;, &#x27;2017-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2017-08-01T00:00:00.000000000&#x27;, &#x27;2017-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2017-10-01T00:00:00.000000000&#x27;, &#x27;2017-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2017-12-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-02-01T00:00:00.000000000&#x27;, &#x27;2018-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-04-01T00:00:00.000000000&#x27;, &#x27;2018-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-06-01T00:00:00.000000000&#x27;, &#x27;2018-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-08-01T00:00:00.000000000&#x27;, &#x27;2018-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-10-01T00:00:00.000000000&#x27;, &#x27;2018-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2018-12-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2019-04-01T00:00:00.000000000&#x27;, &#x27;2019-05-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2019-06-01T00:00:00.000000000&#x27;, &#x27;2019-07-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2019-08-01T00:00:00.000000000&#x27;, &#x27;2019-09-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2019-10-01T00:00:00.000000000&#x27;, &#x27;2019-11-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2019-12-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3154bbcc-a59e-4593-88b6-28d78b540ce5' class='xr-section-summary-in' type='checkbox'  ><label for='section-3154bbcc-a59e-4593-88b6-28d78b540ce5' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5a71daad-992f-4770-8a64-145a55740898' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a71daad-992f-4770-8a64-145a55740898' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be42fa0a-3369-4b1a-abcc-3877d9204925' class='xr-var-data-in' type='checkbox'><label for='data-be42fa0a-3369-4b1a-abcc-3877d9204925' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-e5aaed40-335a-4c73-8359-849f9bc9f291' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5aaed40-335a-4c73-8359-849f9bc9f291' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb4baeb9-243f-4179-b271-827b220f54b4' class='xr-var-data-in' type='checkbox'><label for='data-cb4baeb9-243f-4179-b271-827b220f54b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4a47e8ef-621c-4796-ba52-fbd1b430045c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a47e8ef-621c-4796-ba52-fbd1b430045c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d9a7ba4-f285-4b0f-a438-46501155138f' class='xr-var-data-in' type='checkbox'><label for='data-7d9a7ba4-f285-4b0f-a438-46501155138f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
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+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
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+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
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+       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
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+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d2eb229f-aafd-405d-a4a3-7139a8eabc5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2eb229f-aafd-405d-a4a3-7139a8eabc5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b86d05b-fef9-4fbc-b240-b7738f5330d8' class='xr-var-data-in' type='checkbox'><label for='data-6b86d05b-fef9-4fbc-b240-b7738f5330d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
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+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-6e3650b7-2868-407f-8812-d94ac183b565' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e3650b7-2868-407f-8812-d94ac183b565' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f5907ae-ecd6-45e5-a7cc-2e43874624a6' class='xr-var-data-in' type='checkbox'><label for='data-8f5907ae-ecd6-45e5-a7cc-2e43874624a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.88 MiB </td>\n",
+       "                        <td> 1.88 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (81920, 3) </td>\n",
+       "                        <td> (81920, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
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+       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-e534ce09-86f3-4987-879f-d7d1d0fc740d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e534ce09-86f3-4987-879f-d7d1d0fc740d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bbb69e9-43f0-4255-81b7-d11d8d3baa99' class='xr-var-data-in' type='checkbox'><label for='data-5bbb69e9-43f0-4255-81b7-d11d8d3baa99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-158e3e50-6bea-4afd-8d9a-2111f89dd9b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-158e3e50-6bea-4afd-8d9a-2111f89dd9b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-110c1106-d9af-477a-9170-3195269d18b2' class='xr-var-data-in' type='checkbox'><label for='data-110c1106-d9af-477a-9170-3195269d18b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.88 MiB </td>\n",
+       "                        <td> 1.88 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (81920, 3) </td>\n",
+       "                        <td> (81920, 3) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-44a11c54-cc42-414b-9556-e18d9c05cca0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44a11c54-cc42-414b-9556-e18d9c05cca0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ed0f065-29e4-47bd-8109-6994f33208c0' class='xr-var-data-in' type='checkbox'><label for='data-3ed0f065-29e4-47bd-8109-6994f33208c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-ec20f9d4-dbb5-4bdb-8a38-05ad8514248a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec20f9d4-dbb5-4bdb-8a38-05ad8514248a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38adcc3a-cbbc-4a11-b2f4-188ee83c82eb' class='xr-var-data-in' type='checkbox'><label for='data-38adcc3a-cbbc-4a11-b2f4-188ee83c82eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (81920,) </td>\n",
+       "                        <td> (81920,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-20a125fa-afb6-4e52-91ed-2b58e89482dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20a125fa-afb6-4e52-91ed-2b58e89482dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a2e54c8-25ec-47c2-8f72-eb45ae1cc639' class='xr-var-data-in' type='checkbox'><label for='data-7a2e54c8-25ec-47c2-8f72-eb45ae1cc639' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f83de878-99c0-4b5c-8557-f327b015e0d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f83de878-99c0-4b5c-8557-f327b015e0d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7e14d01-28d6-4b13-8146-199937972672' class='xr-var-data-in' type='checkbox'><label for='data-e7e14d01-28d6-4b13-8146-199937972672' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-54204515-0717-45df-bc23-dd725321caec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54204515-0717-45df-bc23-dd725321caec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3abb627-f43e-473e-aa18-f3319f16eede' class='xr-var-data-in' type='checkbox'><label for='data-e3abb627-f43e-473e-aa18-f3319f16eede' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
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+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3b683c65-fcea-4308-93e6-fb62a0d8f0c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b683c65-fcea-4308-93e6-fb62a0d8f0c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0505990-7f9a-4c5c-a55c-8c44990dd07a' class='xr-var-data-in' type='checkbox'><label for='data-b0505990-7f9a-4c5c-a55c-8c44990dd07a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-6afa9071-6891-417f-8932-8c6fc95919f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6afa9071-6891-417f-8932-8c6fc95919f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22de8f57-ca6a-4c03-a961-9bf90349ce49' class='xr-var-data-in' type='checkbox'><label for='data-22de8f57-ca6a-4c03-a961-9bf90349ce49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
+       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-58af160f-719f-4860-869f-6ed8d0069bd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58af160f-719f-4860-869f-6ed8d0069bd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b513caa2-e465-4527-b7a5-4bff96308294' class='xr-var-data-in' type='checkbox'><label for='data-b513caa2-e465-4527-b7a5-4bff96308294' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-99e224f5-5025-4bc4-9c34-3423c860b882' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99e224f5-5025-4bc4-9c34-3423c860b882' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b38b3e46-4d4b-46df-a701-506ac41cfb0a' class='xr-var-data-in' type='checkbox'><label for='data-b38b3e46-4d4b-46df-a701-506ac41cfb0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a37781b2-d2da-4302-b5b6-6215bf962e26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a37781b2-d2da-4302-b5b6-6215bf962e26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faebc55e-8741-4d02-93da-74ff7bea0340' class='xr-var-data-in' type='checkbox'><label for='data-faebc55e-8741-4d02-93da-74ff7bea0340' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-7a0cd087-6ac2-4a67-a19d-3d7df27c8b7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a0cd087-6ac2-4a67-a19d-3d7df27c8b7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f439afc-bb33-4dc8-a736-f351d1059291' class='xr-var-data-in' type='checkbox'><label for='data-4f439afc-bb33-4dc8-a736-f351d1059291' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-dc099be8-1ff9-4ab1-96f3-f59d3693a818' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc099be8-1ff9-4ab1-96f3-f59d3693a818' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b95b6960-0baa-4bb2-a9e2-b488cc9c49c4' class='xr-var-data-in' type='checkbox'><label for='data-b95b6960-0baa-4bb2-a9e2-b488cc9c49c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ca39c122-9f76-40a9-9294-ef5dfaf55fd5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca39c122-9f76-40a9-9294-ef5dfaf55fd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dacb22f-7960-4434-94f5-99ffe3204fdc' class='xr-var-data-in' type='checkbox'><label for='data-1dacb22f-7960-4434-94f5-99ffe3204fdc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-99b8d6bb-1d3a-4217-984a-81b1d7dd1d9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99b8d6bb-1d3a-4217-984a-81b1d7dd1d9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-149abd9c-0260-4c4f-bb88-d14fb97a3825' class='xr-var-data-in' type='checkbox'><label for='data-149abd9c-0260-4c4f-bb88-d14fb97a3825' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-bed9aab2-11c5-45b6-9414-4cca3e5394c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bed9aab2-11c5-45b6-9414-4cca3e5394c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5760cd25-17a2-4628-a0eb-97f288924838' class='xr-var-data-in' type='checkbox'><label for='data-5760cd25-17a2-4628-a0eb-97f288924838' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-0a6111fa-3d3b-43f2-be3f-ade8dd3a0aea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a6111fa-3d3b-43f2-be3f-ade8dd3a0aea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44215e69-d125-494d-a1aa-c43eab0fce56' class='xr-var-data-in' type='checkbox'><label for='data-44215e69-d125-494d-a1aa-c43eab0fce56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-143250f1-72b8-48bb-82ac-3809d83947fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-143250f1-72b8-48bb-82ac-3809d83947fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e126cd8e-d008-4178-9a6c-0a165df0ee41' class='xr-var-data-in' type='checkbox'><label for='data-e126cd8e-d008-4178-9a6c-0a165df0ee41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f522d01b-2487-40eb-b090-335dacb0855d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f522d01b-2487-40eb-b090-335dacb0855d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-270f7d32-b1f3-4d5c-a8dd-32abb7909009' class='xr-var-data-in' type='checkbox'><label for='data-270f7d32-b1f3-4d5c-a8dd-32abb7909009' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c0d1ff4c-51cb-4786-849e-8b7b424b5a78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0d1ff4c-51cb-4786-849e-8b7b424b5a78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd037c37-81e9-4bf0-8460-aead6463ca40' class='xr-var-data-in' type='checkbox'><label for='data-cd037c37-81e9-4bf0-8460-aead6463ca40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
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+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp_10m</span></div><div class='xr-var-dims'>(time, height, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4abd552f-2c5a-4a90-a27c-e9d4c3820608' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4abd552f-2c5a-4a90-a27c-e9d4c3820608' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5cd26ef-c1d4-43a3-bc2f-977f7c65c338' class='xr-var-data-in' type='checkbox'><label for='data-f5cd26ef-c1d4-43a3-bc2f-977f7c65c338' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>wind speed in 10m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.0</dd><dt><span>standard_name :</span></dt><dd>sp_10m</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 1, 81920) </td>\n",
+       "                        <td> (1, 1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8d0b5aa9-4a35-4775-8661-ba3e34cd388a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d0b5aa9-4a35-4775-8661-ba3e34cd388a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bea07493-320c-4bde-9c06-088019113091' class='xr-var-data-in' type='checkbox'><label for='data-bea07493-320c-4bde-9c06-088019113091' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <td> </td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-343d7728-7be5-4834-88aa-0093656234ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-343d7728-7be5-4834-88aa-0093656234ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18bec288-304d-43bb-a476-b358fe4b7295' class='xr-var-data-in' type='checkbox'><label for='data-18bec288-304d-43bb-a476-b358fe4b7295' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <td> </td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
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+       "                    \n",
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+       "                        <th> Dask graph </th>\n",
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+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-292acec5-7f89-4625-af1a-ce5896cb2327' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-292acec5-7f89-4625-af1a-ce5896cb2327' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49904d6f-61c1-4738-8e70-7da39b46774e' class='xr-var-data-in' type='checkbox'><label for='data-49904d6f-61c1-4738-8e70-7da39b46774e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-79221507-2bac-4f26-9e25-f2d4049f35cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79221507-2bac-4f26-9e25-f2d4049f35cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-846d89d2-7276-4b9d-a488-52684e257345' class='xr-var-data-in' type='checkbox'><label for='data-846d89d2-7276-4b9d-a488-52684e257345' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-deecbb0c-5722-4bd0-95dc-3d5a796b8e57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-deecbb0c-5722-4bd0-95dc-3d5a796b8e57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4804befe-e1ae-4d3a-8c95-9395649883ec' class='xr-var-data-in' type='checkbox'><label for='data-4804befe-e1ae-4d3a-8c95-9395649883ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f8a2cf4c-51d4-42b6-bab7-08c82ec5ab46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8a2cf4c-51d4-42b6-bab7-08c82ec5ab46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8df38a7b-b549-4c90-ac98-ede599419609' class='xr-var-data-in' type='checkbox'><label for='data-8df38a7b-b549-4c90-ac98-ede599419609' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-313d5a39-7f7f-4b8f-b3ef-f58fc9637b52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-313d5a39-7f7f-4b8f-b3ef-f58fc9637b52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ceecaf5-c5e9-48d8-adee-2e90f3d230d3' class='xr-var-data-in' type='checkbox'><label for='data-8ceecaf5-c5e9-48d8-adee-2e90f3d230d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-616356f9-fd2e-4e16-a4cc-8ce1062ecc2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-616356f9-fd2e-4e16-a4cc-8ce1062ecc2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14402d26-223f-4dcb-9df4-2a0b85980048' class='xr-var-data-in' type='checkbox'><label for='data-14402d26-223f-4dcb-9df4-2a0b85980048' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
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+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
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+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3a9f2ef0-2730-431f-be46-eb2b106ad6c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a9f2ef0-2730-431f-be46-eb2b106ad6c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e5865b8-8b29-466d-845d-e2250a9b4527' class='xr-var-data-in' type='checkbox'><label for='data-3e5865b8-8b29-466d-845d-e2250a9b4527' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
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+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a7a0d57f-40e8-4766-ad3d-c5cdd65427fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7a0d57f-40e8-4766-ad3d-c5cdd65427fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5be4ea3-1c0d-4f4b-84e0-63a4c034eb54' class='xr-var-data-in' type='checkbox'><label for='data-d5be4ea3-1c0d-4f4b-84e0-63a4c034eb54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-45b3c401-0bd1-4f77-91e6-8d9dcf20fe4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45b3c401-0bd1-4f77-91e6-8d9dcf20fe4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3abeca91-c1f7-4537-98a4-727fc04b9edc' class='xr-var-data-in' type='checkbox'><label for='data-3abeca91-c1f7-4537-98a4-727fc04b9edc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
+       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-06db749b-ed47-4bd9-bf2f-4dc234ffd219' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06db749b-ed47-4bd9-bf2f-4dc234ffd219' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3119589-bc62-4a4c-9112-9fb6b2092d06' class='xr-var-data-in' type='checkbox'><label for='data-c3119589-bc62-4a4c-9112-9fb6b2092d06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
+       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ac793310-edf1-4fcf-95ec-07811f950e76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac793310-edf1-4fcf-95ec-07811f950e76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-659c0864-1b1f-4bb2-ba2c-71a9da0c0d75' class='xr-var-data-in' type='checkbox'><label for='data-659c0864-1b1f-4bb2-ba2c-71a9da0c0d75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.75 MiB </td>\n",
+       "                        <td> 320.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (60, 81920) </td>\n",
+       "                        <td> (1, 81920) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
+       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
+       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
+       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
+       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
+       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
+       "  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n",
+       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
+       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
+       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
+       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
+       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
+       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
+       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
+       "  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n",
+       "  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
+       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
+       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-261b963c-2849-4a97-a8b0-229e827c581f' class='xr-section-summary-in' type='checkbox'  ><label for='section-261b963c-2849-4a97-a8b0-229e827c581f' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-589a14c9-74a9-4c4d-9976-ae294fe9c5c9' class='xr-index-data-in' type='checkbox'/><label for='index-589a14c9-74a9-4c4d-9976-ae294fe9c5c9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-31686d0e-22e6-4daa-a1b0-df8c8c09be91' class='xr-index-data-in' type='checkbox'/><label for='index-31686d0e-22e6-4daa-a1b0-df8c8c09be91' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bd25388f-1a54-4331-aa23-29974054e37d' class='xr-index-data-in' type='checkbox'/><label for='index-bd25388f-1a54-4331-aa23-29974054e37d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2015-02-01&#x27;, &#x27;2015-03-01&#x27;, &#x27;2015-04-01&#x27;, &#x27;2015-05-01&#x27;,\n",
+       "               &#x27;2015-06-01&#x27;, &#x27;2015-07-01&#x27;, &#x27;2015-08-01&#x27;, &#x27;2015-09-01&#x27;,\n",
+       "               &#x27;2015-10-01&#x27;, &#x27;2015-11-01&#x27;, &#x27;2015-12-01&#x27;, &#x27;2016-01-01&#x27;,\n",
+       "               &#x27;2016-02-01&#x27;, &#x27;2016-03-01&#x27;, &#x27;2016-04-01&#x27;, &#x27;2016-05-01&#x27;,\n",
+       "               &#x27;2016-06-01&#x27;, &#x27;2016-07-01&#x27;, &#x27;2016-08-01&#x27;, &#x27;2016-09-01&#x27;,\n",
+       "               &#x27;2016-10-01&#x27;, &#x27;2016-11-01&#x27;, &#x27;2016-12-01&#x27;, &#x27;2017-01-01&#x27;,\n",
+       "               &#x27;2017-02-01&#x27;, &#x27;2017-03-01&#x27;, &#x27;2017-04-01&#x27;, &#x27;2017-05-01&#x27;,\n",
+       "               &#x27;2017-06-01&#x27;, &#x27;2017-07-01&#x27;, &#x27;2017-08-01&#x27;, &#x27;2017-09-01&#x27;,\n",
+       "               &#x27;2017-10-01&#x27;, &#x27;2017-11-01&#x27;, &#x27;2017-12-01&#x27;, &#x27;2018-01-01&#x27;,\n",
+       "               &#x27;2018-02-01&#x27;, &#x27;2018-03-01&#x27;, &#x27;2018-04-01&#x27;, &#x27;2018-05-01&#x27;,\n",
+       "               &#x27;2018-06-01&#x27;, &#x27;2018-07-01&#x27;, &#x27;2018-08-01&#x27;, &#x27;2018-09-01&#x27;,\n",
+       "               &#x27;2018-10-01&#x27;, &#x27;2018-11-01&#x27;, &#x27;2018-12-01&#x27;, &#x27;2019-01-01&#x27;,\n",
+       "               &#x27;2019-02-01&#x27;, &#x27;2019-03-01&#x27;, &#x27;2019-04-01&#x27;, &#x27;2019-05-01&#x27;,\n",
+       "               &#x27;2019-06-01&#x27;, &#x27;2019-07-01&#x27;, &#x27;2019-08-01&#x27;, &#x27;2019-09-01&#x27;,\n",
+       "               &#x27;2019-10-01&#x27;, &#x27;2019-11-01&#x27;, &#x27;2019-12-01&#x27;, &#x27;2020-01-01&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c18235e-39dd-4391-b9d8-eef52baf19f3' class='xr-section-summary-in' type='checkbox'  ><label for='section-9c18235e-39dd-4391-b9d8-eef52baf19f3' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 020510</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\n",
+       "Coordinates:\n",
+       "    clat           (ncells) float64 dask.array<chunksize=(81920,), meta=np.ndarray>\n",
+       "    clon           (ncells) float64 dask.array<chunksize=(81920,), meta=np.ndarray>\n",
+       "  * height         (height) float64 10.0\n",
+       "  * height_2       (height_2) float64 2.0\n",
+       "  * time           (time) datetime64[ns] 2015-02-01 2015-03-01 ... 2020-01-01\n",
+       "Dimensions without coordinates: ncells, vertices\n",
+       "Data variables: (12/40)\n",
+       "    albnirdif      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    albnirdir      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    albvisdif      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    albvisdir      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    clat_bnds      (ncells, vertices) float64 dask.array<chunksize=(81920, 3), meta=np.ndarray>\n",
+       "    clct           (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    ...             ...\n",
+       "    tqc_dia        (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    tqi_dia        (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    tqv            (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    tqv_dia        (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    umfl_s         (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "    vmfl_s         (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n",
+       "    Conventions:          CF-1.6\n",
+       "    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n",
+       "    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n",
+       "    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n",
+       "    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n",
+       "    number_of_grid_used:  30\n",
+       "    references:           see MPIM/DWD publications\n",
+       "    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n",
+       "    title:                ICON simulation\n",
+       "    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_2015 = load_ICONXPP_data(year=2020, month=1)\n",
+    "ds_2015"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8110a212-0814-4b9c-8a32-9d6379c6a6c0",
+   "metadata": {},
+   "source": [
+    "The data can be visualized as demonstrated below. For example, the total precipitation rate on **2020-01-01 at 00:00:00 UTC** is plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "6750630a-8b1b-4a35-8cb7-8323a55dbf1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<earthkit.plots.components.maps.Map at 0x320140250>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "earthkit.plots.quickmap.plot(\n",
+    "    ds_2015[\"tot_prec_rate\"].sel(time=np.datetime64(\"2020-01-01 00:00:00\"))[::10], \n",
+    "    x=\"clon\", y=\"clat\"\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3153d77-e5e3-49b0-94ba-9048a71889e8",
+   "metadata": {},
+   "source": [
+    "### Please note: due to an earthkit-plots bug related to np.datetime64 it is currently only possible to plot data ranging from 1678 to 2262."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f820694-e442-487d-981a-4fbd238a806e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 86e64dbc2e91270274af550f2dc72b1e19c7f472 Mon Sep 17 00:00:00 2001
From: Sara Faghih-Naini <sara.faghihnaini@ecmwf.int>
Date: Mon, 9 Dec 2024 11:29:37 +0100
Subject: [PATCH 2/8] Run notebooks in online lab

---
 04-example-datasets/01-hplp.ipynb    | 3468 ++------------
 04-example-datasets/02-OpenIFS.ipynb | 6652 +-------------------------
 2 files changed, 678 insertions(+), 9442 deletions(-)

diff --git a/04-example-datasets/01-hplp.ipynb b/04-example-datasets/01-hplp.ipynb
index 982127c..e1e8abc 100644
--- a/04-example-datasets/01-hplp.ipynb
+++ b/04-example-datasets/01-hplp.ipynb
@@ -1,3072 +1,476 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
-   "metadata": {},
-   "source": [
-    "# IFS hplp experiment data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
-   "metadata": {},
-   "source": [
-    "> [!NOTE]\n",
-    "> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n",
-    "\n",
-    "> [!CAUTION]\n",
-    "> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "463bf2c0-ac3f-4786-9687-362393802dce",
-   "metadata": {},
-   "source": [
-    "This notebook is a comprehensive guide to accessing GRIB files for the [hplp experiment](https://apps.ecmwf.int/ifs-experiments/rd/hplp/). It includes details about the available parameters, levels, and steps, along with instructions for using the `load_hplp_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/). The GRIB files are not downloaded locally; instead, they are accessed remotely using `gribscan`, `fsspec`, and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `gribscan`.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## Experiment Details and Available Data Fields\n",
-    "\n",
-    "The dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point single-time atmospheric and wave model (long window 4Dvar) outputs from the IFS.\n",
-    "The simulation was conducted on a reduced Gaussian octahedral grid (Tco399), corresponding to a horizontal resolution of approximately 28 km, and with 137 vertical model levels. The 10-day weather forecast is provided in 12-hour intervals, beginning from 2020-07-21 at 00:00:00 UTC.\n",
-    "The total volume of the available dataset is about 187 GB.\n",
-    "\n",
-    "The GRIB files are organized into different types of data fields based on the levels at which they are stored.\n",
-    "\n",
-    "### Pressure Level Parameters\n",
-    "\n",
-    "The following parameters are available as pressure level fields:\n",
-    "```python\n",
-    "[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n",
-    "```\n",
-    "\n",
-    "These parameters are stored at the following pressure levels (in hPa):\n",
-    "```python\n",
-    "[1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000]\n",
-    "```\n",
-    "\n",
-    "The corresponding IDs and more information are given in the table below:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c1d220c-d8b8-4e7c-90f4-0625b4cbc15b",
-   "metadata": {},
-   "source": [
-    "|   paramId | gridName   | gridType   | typeOfLevel   | shortName   | packingType      |\n",
-    "|----------:|:-----------|:-----------|:--------------|:------------|:-----------------|\n",
-    "|       129 | not_found  | sh         | isobaricInhPa | z           | spectral_complex |\n",
-    "|       130 | not_found  | sh         | isobaricInhPa | t           | spectral_complex |\n",
-    "|       133 | O400       | reduced_gg | isobaricInhPa | q           | spectral_complex |\n",
-    "|       135 | not_found  | sh         | isobaricInhPa | w           | spectral_complex |\n",
-    "|       138 | not_found  | sh         | isobaricInhPa | vo          | spectral_complex |\n",
-    "|       155 | not_found  | sh         | isobaricInhPa | d           | spectral_complex |\n",
-    "|       131 | not_found  | sh         | isobaricInhPa | u           | spectral_complex |\n",
-    "|       132 | not_found  | sh         | isobaricInhPa | v           | spectral_complex |\n",
-    "|       157 | not_found  | sh         | isobaricInhPa | r           | spectral_complex |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "789d34ac-2fdb-4d92-8c0c-c62b363a57a0",
-   "metadata": {},
-   "source": [
-    "### Model Level Parameters\n",
-    "\n",
-    "The following parameters are available as model level fields:\n",
-    "```python\n",
-    "[\"crwc\", \"cswc\", \"z\", \"t\", \"q\", \"w\", \"vo\", \"lnsp\", \"d\", \"u\", \"v\", \"o3\", \"clwc\", \"ciwc\", \"cc\"]\n",
-    "```\n",
-    "These parameters are stored at the following levels:\n",
-    "```python\n",
-    "[list(range(1, 138))] # Levels 1 to 137\n",
-    "```\n",
-    "For more information about the model levels please see [here](https://confluence.ecmwf.int/display/UDOC/Model+level+definitions) and [here](https://confluence.ecmwf.int/display/UDOC/L137+model+level+definitions).\n",
-    "\n",
-    "The corresponding IDs and more information are given in the table below:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d96a88c4-7954-42b5-989b-c433d4c87f4e",
-   "metadata": {},
-   "source": [
-    "|   paramId | gridName   | gridType   | typeOfLevel   | shortName   | packingType   |\n",
-    "|----------:|:-----------|:-----------|:--------------|:------------|:--------------|\n",
-    "|        75 | O400       | reduced_gg | hybrid        | crwc        | grid_ieee     |\n",
-    "|        76 | O400       | reduced_gg | hybrid        | cswc        | grid_ieee     |\n",
-    "|       129 | not_found  | sh         | hybrid        | z           | grid_ieee     |\n",
-    "|       130 | not_found  | sh         | hybrid        | t           | grid_ieee     |\n",
-    "|       133 | O400       | reduced_gg | hybrid        | q           | grid_ieee     |\n",
-    "|       135 | not_found  | sh         | hybrid        | w           | grid_ieee     |\n",
-    "|       138 | not_found  | sh         | hybrid        | vo          | grid_ieee     |\n",
-    "|       152 | not_found  | sh         | hybrid        | lnsp        | grid_ieee     |\n",
-    "|       155 | not_found  | sh         | hybrid        | d           | spectral_ieee |\n",
-    "|       131 | not_found  | sh         | hybrid        | u           | spectral_ieee |\n",
-    "|       132 | not_found  | sh         | sh            | v           | spectral_ieee |\n",
-    "|       203 | O400       | reduced_gg | sh            | o3          | spectral_ieee |\n",
-    "|       246 | O400       | reduced_gg | sh            | clwc        | spectral_ieee |\n",
-    "|       247 | O400       | reduced_gg | sh            | ciwc        | spectral_ieee |\n",
-    "|       248 | O400       | reduced_gg | sh            | cc          | spectral_ieee |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8539c000-1d96-439e-bc4d-2076c6f3f442",
-   "metadata": {},
-   "source": [
-    "### Surface Level Parameters\n",
-    "\n",
-    "The following parameters are available as surface level fields:\n",
-    "```python\n",
-    "['cin', 'sro', 'ssro', 'vis', 'i10fg', 'ci', 'asn', 'rsn', 'sst', 'istl1', 'istl2', 'istl3', 'istl4', 'swvl1', 'swvl2', 'swvl3', 'swvl4', 'es', 'smlt', 'dsrp', '10fg', 'lspf', 'uvb', 'par', 'cape', 'tclw', 'tciw', 'tcrw', 'tcsw', 'z', 'ocu', 'ocv', 'tcw', 'tcwv', 'stl1', 'sd', 'lsp', 'cp', 'sf', 'bld', 'sshf', 'slhf', 'chnk', 'msl', 'blh', 'tcc', '10u', '10v', '2t', '2d', 'ssrd', 'stl2', 'lsm', 'strd', 'ssr', 'str', 'tsr', 'ttr', 'ewss', 'nsss', 'e', 'stl3', 'lcc', 'mcc', 'hcc', 'sund', 'lgws', 'mgws', 'gwd', 'src', 'mx2t', 'mn2t', 'ro', 'tco3', 'tsrc', 'ttrc', 'ssrc', 'strc',  'tisr', 'vimd', 'fzra', 'ilspf', 'crr', 'lsrr', 'csfr', 'lssfr', 'tp', 'iews', 'inss', 'ishf', 'ie', 'skt', 'stl4', 'tsn', 'fal', 'fsr', 'flsr', 'pev', 'ptype', 'kx', 'totalx']\n",
-    "```\n",
-    "\n",
-    "The corresponding IDs and more information are given in the (hidden) table below:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "557c2ea2-1790-4af2-921d-46110b5502d3",
-   "metadata": {},
-   "source": [
-    "|   paramId | gridName   | gridType   | typeOfLevel         | shortName   | packingType   |\n",
-    "|----------:|:-----------|:-----------|:--------------------|:------------|:--------------|\n",
-    "|    228001 | O400       | reduced_gg | surface             | cin         | grid_ieee     |\n",
-    "|         8 | O400       | reduced_gg | surface             | sro         | grid_ieee     |\n",
-    "|         9 | O400       | reduced_gg | surface             | ssro        | grid_ieee     |\n",
-    "|      3020 | O400       | reduced_gg | surface             | vis         | grid_ieee     |\n",
-    "|    228029 | O400       | reduced_gg | surface             | i10fg       | grid_ieee     |\n",
-    "|        31 | O400       | reduced_gg | surface             | ci          | grid_ieee     |\n",
-    "|        32 | O400       | reduced_gg | surface             | asn         | grid_ieee     |\n",
-    "|        33 | O400       | reduced_gg | surface             | rsn         | grid_ieee     |\n",
-    "|        34 | O400       | reduced_gg | surface             | sst         | grid_ieee     |\n",
-    "|        35 | O400       | reduced_gg | surface             | istl1       | grid_ieee     |\n",
-    "|        36 | O400       | reduced_gg | surface             | istl2       | grid_ieee     |\n",
-    "|        37 | O400       | reduced_gg | surface             | istl3       | grid_ieee     |\n",
-    "|        38 | O400       | reduced_gg | surface             | istl4       | grid_ieee     |\n",
-    "|        39 | O400       | reduced_gg | surface             | swvl1       | grid_ieee     |\n",
-    "|        40 | O400       | reduced_gg | surface             | swvl2       | grid_ieee     |\n",
-    "|        41 | O400       | reduced_gg | surface             | swvl3       | grid_ieee     |\n",
-    "|        42 | O400       | reduced_gg | surface             | swvl4       | grid_ieee     |\n",
-    "|        44 | O400       | reduced_gg | surface             | es          | grid_ieee     |\n",
-    "|        45 | O400       | reduced_gg | surface             | smlt        | grid_ieee     |\n",
-    "|        47 | O400       | reduced_gg | surface             | dsrp        | grid_ieee     |\n",
-    "|        49 | O400       | reduced_gg | surface             | 10fg        | grid_ieee     |\n",
-    "|        50 | O400       | reduced_gg | surface             | lspf        | grid_ieee     |\n",
-    "|        57 | O400       | reduced_gg | surface             | uvb         | grid_ieee     |\n",
-    "|        58 | O400       | reduced_gg | surface             | par         | grid_ieee     |\n",
-    "|        59 | O400       | reduced_gg | surface             | cape        | grid_ieee     |\n",
-    "|        78 | O400       | reduced_gg | surface             | tclw        | grid_ieee     |\n",
-    "|        79 | O400       | reduced_gg | surface             | tciw        | grid_ieee     |\n",
-    "|    228089 | O400       | reduced_gg | surface             | tcrw        | grid_ieee     |\n",
-    "|    228090 | O400       | reduced_gg | surface             | tcsw        | grid_ieee     |\n",
-    "|       129 | O400       | reduced_gg | surface             | z           | grid_ieee     |\n",
-    "|    151131 | O400       | reduced_gg | surface             | ocu         | grid_ieee     |\n",
-    "|    151132 | O400       | reduced_gg | surface             | ocv         | grid_ieee     |\n",
-    "|       136 | O400       | reduced_gg | surface             | tcw         | grid_ieee     |\n",
-    "|       137 | O400       | reduced_gg | surface             | tcwv        | grid_ieee     |\n",
-    "|       139 | O400       | reduced_gg | surface             | stl1        | grid_ieee     |\n",
-    "|       141 | O400       | reduced_gg | surface             | sd          | grid_ieee     |\n",
-    "|       142 | O400       | reduced_gg | surface             | lsp         | grid_ieee     |\n",
-    "|       143 | O400       | reduced_gg | surface             | cp          | grid_ieee     |\n",
-    "|       144 | O400       | reduced_gg | surface             | sf          | grid_ieee     |\n",
-    "|       145 | O400       | reduced_gg | surface             | bld         | grid_ieee     |\n",
-    "|       146 | O400       | reduced_gg | surface             | sshf        | grid_ieee     |\n",
-    "|       147 | O400       | reduced_gg | surface             | slhf        | grid_ieee     |\n",
-    "|       148 | O400       | reduced_gg | surface             | chnk        | grid_ieee     |\n",
-    "|       151 | O400       | reduced_gg | surface             | msl         | grid_ieee     |\n",
-    "|       159 | O400       | reduced_gg | surface             | blh         | grid_ieee     |\n",
-    "|       164 | O400       | reduced_gg | surface             | tcc         | grid_ieee     |\n",
-    "|       165 | O400       | reduced_gg | surface             | 10u         | grid_ieee     |\n",
-    "|       166 | O400       | reduced_gg | surface             | 10v         | grid_ieee     |\n",
-    "|       167 | O400       | reduced_gg | surface             | 2t          | grid_ieee     |\n",
-    "|       168 | O400       | reduced_gg | surface             | 2d          | grid_ieee     |\n",
-    "|       169 | O400       | reduced_gg | surface             | ssrd        | grid_ieee     |\n",
-    "|       170 | O400       | reduced_gg | surface             | stl2        | grid_ieee     |\n",
-    "|       172 | O400       | reduced_gg | surface             | lsm         | grid_ieee     |\n",
-    "|       175 | O400       | reduced_gg | surface             | strd        | grid_ieee     |\n",
-    "|       176 | O400       | reduced_gg | surface             | ssr         | grid_ieee     |\n",
-    "|       177 | O400       | reduced_gg | surface             | str         | grid_ieee     |\n",
-    "|       178 | O400       | reduced_gg | surface             | tsr         | grid_ieee     |\n",
-    "|       179 | O400       | reduced_gg | surface             | ttr         | grid_ieee     |\n",
-    "|       180 | O400       | reduced_gg | surface             | ewss        | grid_ieee     |\n",
-    "|       181 | O400       | reduced_gg | surface             | nsss        | grid_ieee     |\n",
-    "|       182 | O400       | reduced_gg | surface             | e           | grid_ieee     |\n",
-    "|       183 | O400       | reduced_gg | surface             | stl3        | grid_ieee     |\n",
-    "|       186 | O400       | reduced_gg | surface             | lcc         | grid_ieee     |\n",
-    "|       187 | O400       | reduced_gg | surface             | mcc         | grid_ieee     |\n",
-    "|       188 | O400       | reduced_gg | surface             | hcc         | grid_ieee     |\n",
-    "|       189 | O400       | reduced_gg | surface             | sund        | grid_ieee     |\n",
-    "|       195 | O400       | reduced_gg | surface             | lgws        | grid_ieee     |\n",
-    "|       196 | O400       | reduced_gg | surface             | mgws        | grid_ieee     |\n",
-    "|       197 | O400       | reduced_gg | surface             | gwd         | grid_ieee     |\n",
-    "|       198 | O400       | reduced_gg | surface             | src         | grid_ieee     |\n",
-    "|       201 | O400       | reduced_gg | surface             | mx2t        | grid_ieee     |\n",
-    "|       202 | O400       | reduced_gg | surface             | mn2t        | grid_ieee     |\n",
-    "|       205 | O400       | reduced_gg | surface             | ro          | grid_ieee     |\n",
-    "|       206 | O400       | reduced_gg | surface             | tco3        | grid_ieee     |\n",
-    "|       208 | O400       | reduced_gg | surface             | tsrc        | grid_ieee     |\n",
-    "|       209 | O400       | reduced_gg | surface             | ttrc        | grid_ieee     |\n",
-    "|       210 | O400       | reduced_gg | surface             | ssrc        | grid_ieee     |\n",
-    "|       211 | O400       | reduced_gg | surface             | strc        | grid_ieee     |\n",
-    "|       212 | O400       | reduced_gg | surface             | tisr        | grid_ieee     |\n",
-    "|       213 | O400       | reduced_gg | surface             | vimd        | grid_ieee     |\n",
-    "|    228216 | O400       | reduced_gg | surface             | fzra        | grid_ieee     |\n",
-    "|    228217 | O400       | reduced_gg | surface             | ilspf       | grid_ieee     |\n",
-    "|    228218 | O400       | reduced_gg | surface             | crr         | grid_ieee     |\n",
-    "|    228219 | O400       | reduced_gg | surface             | lsrr        | grid_ieee     |\n",
-    "|    228220 | O400       | reduced_gg | depthBelowLandLayer | csfr        | grid_ieee     |\n",
-    "|    228221 | O400       | reduced_gg | depthBelowLandLayer | lssfr       | grid_ieee     |\n",
-    "|       228 | O400       | reduced_gg | depthBelowLandLayer | tp          | grid_ieee     |\n",
-    "|       229 | O400       | reduced_gg | depthBelowLandLayer | iews        | grid_ieee     |\n",
-    "|       230 | O400       | reduced_gg | depthBelowLandLayer | inss        | grid_ieee     |\n",
-    "|       231 | O400       | reduced_gg | depthBelowLandLayer | ishf        | grid_ieee     |\n",
-    "|       232 | O400       | reduced_gg | depthBelowLandLayer | ie          | grid_ieee     |\n",
-    "|       235 | O400       | reduced_gg | depthBelowLandLayer | skt         | grid_ieee     |\n",
-    "|       236 | O400       | reduced_gg | depthBelowLandLayer | stl4        | grid_ieee     |\n",
-    "|       238 | O400       | reduced_gg | depthBelowLandLayer | tsn         | grid_ieee     |\n",
-    "|       243 | O400       | reduced_gg | depthBelowLandLayer | fal         | grid_ieee     |\n",
-    "|       244 | O400       | reduced_gg | depthBelowLandLayer | fsr         | grid_ieee     |\n",
-    "|       245 | O400       | reduced_gg | depthBelowLandLayer | flsr        | grid_ieee     |\n",
-    "|    228251 | O400       | reduced_gg | depthBelowLandLayer | pev         | grid_ieee     |\n",
-    "|    260015 | O400       | reduced_gg | depthBelowLandLayer | ptype       | grid_ieee     |\n",
-    "|    260121 | O400       | reduced_gg | depthBelowLandLayer | kx          | grid_ieee     |\n",
-    "|    260123 | O400       | reduced_gg | depthBelowLandLayer | totalx      | grid_ieee     |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f18ec120-3193-42d4-bcda-5a8a80bc574c",
-   "metadata": {},
-   "source": [
-    "### Wave Model Parameters\n",
-    "\n",
-    "The following parameters are available for the wave model data:\n",
-    "```python\n",
-    "['wstar', 'rhoao', 'phioc', 'tauoc', 'ust', 'vst', 'hmax', 'wmb', 'mp1', 'mp2', 'wdw', 'swh', 'mwd', 'pp1d', 'mwp', 'cdww', 'wind','dwi']\n",
-    "```\n",
-    "\n",
-    "The corresponding IDs and more information are given in the table below:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "968e5b37-1165-4396-b0eb-8b9e03dd7d2b",
-   "metadata": {},
-   "source": [
-    "|   paramId | gridName   | gridType   | typeOfLevel       | shortName   | packingType   |\n",
-    "|----------:|:-----------|:-----------|:------------------|:------------|:--------------|\n",
-    "|    140208 | not_found  | reduced_ll | meanSea           | wstar       | grid_simple   |\n",
-    "|    140209 | not_found  | reduced_ll | meanSea           | rhoao       | grid_simple   |\n",
-    "|    140212 | not_found  | reduced_ll | meanSea           | phioc       | grid_simple   |\n",
-    "|    140214 | not_found  | reduced_ll | meanSea           | tauoc       | grid_simple   |\n",
-    "|    140215 | not_found  | reduced_ll | meanSea           | ust         | grid_simple   |\n",
-    "|    140216 | not_found  | reduced_ll | meanSea           | vst         | grid_simple   |\n",
-    "|    140218 | not_found  | reduced_ll | meanSea           | hmax        | grid_simple   |\n",
-    "|    140219 | not_found  | reduced_ll | meanSea           | wmb         | grid_simple   |\n",
-    "|    140220 | not_found  | reduced_ll | meanSea           | mp1         | grid_simple   |\n",
-    "|    140221 | not_found  | reduced_ll | meanSea           | mp2         | grid_simple   |\n",
-    "|    140222 | not_found  | reduced_ll | meanSea           | wdw         | grid_simple   |\n",
-    "|    140229 | not_found  | reduced_ll | meanSea           | swh         | grid_simple   |\n",
-    "|    140230 | not_found  | reduced_ll | meanSea           | mwd         | grid_simple   |\n",
-    "|    140231 | not_found  | reduced_ll | meanSea           | pp1d        | grid_simple   |\n",
-    "|    140232 | not_found  | reduced_ll | meanSea           | mwp         | grid_simple   |\n",
-    "|    140233 | not_found  | reduced_ll | heightAboveGround | cdww        | grid_simple   |\n",
-    "|    140245 | not_found  | reduced_ll | heightAboveGround | wind        | grid_simple   |\n",
-    "|    140249 | not_found  | reduced_ll | heightAboveGround | dwi         | grid_simple   |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
-   "metadata": {},
-   "source": [
-    "## Available Steps \n",
-    "\n",
-    "The following timesteps are available: \n",
-    "\n",
-    "```python \n",
-    "[0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144, 156, 168, 180, 192, 204, 216, 228, 240]\n",
-    " ```\n",
-    "Timestep 0 is 2020-07-21T 00:00:00 UTC, increasing in 12-hour steps to 240  which is 2020-07-31T 00:00:00 UTC.\n",
-    "\n",
-    "## Using the `load_hplp_data` Function\n",
-    "\n",
-    "To load the GRIB files, you can use the following function:\n",
-    "\n",
-    "```python\n",
-    "load_hplp_data(leveltype=None, gridtype=None, step=None, remap=False)\n",
-    "```\n",
-    "\n",
-    "### Parameters\n",
-    "\n",
-    "- **`leveltype`**: Defines the type of data to load. Choose one of the following:\n",
-    "  - `\"pl\"`: Pressure level fields.\n",
-    "  - `\"ml\"`: Model level fields.\n",
-    "  - `\"sfc\"`: Surface level fields.\n",
-    "  - `\"wave\"`: Wave model fields.\n",
-    "- **`gridtype`**: Specifies the grid type for the data. Options include:\n",
-    "  - `\"reduced_gg\"`: Reduced Gaussian grid (available for `pl`, `ml`, and `sfc`).\n",
-    "  - `\"sh\"`: Spherical harmonic coefficients (available for `pl` and `ml`).\n",
-    "  - `\"reduced_ll\"`: Reduced lat-long grid (available for `wave`).\n",
-    "- **`step`**: The `ml` data on the `reduced_gg` grid is split into two files. Specify the timestep (`step`) to load the corresponding file. Valid steps are:\n",
-    "  - `0-120`: First file (2020-07-21T00:00:00 to 2020-07-26T00:00:00).\n",
-    "  - `132-240`: Second file (2020-07-26T12:00:00 to 2020-07-31T00:00:00).\n",
-    "- **`remap`**: When using gridtype=`sh`, data cannot be directly plotted. To load remapped data on an O400 grid, set `remap=True`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# URL of S3 bucket\n",
-    "BASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
-   "metadata": {},
-   "source": [
-    "First, the package `zarr` (modern dataset format that is specifically designed for chunked access), its dependencies, and `gribscan` need to be imported for the remote access."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[pyodide]: Loading aiohttp, aiosignal, asciitree, async-timeout, attrs, cffi, cfgrib, click, eccodes, findlibs, frozenlist, fsspec, gribscan, msgpack, multidict, numcodecs, numpy, openblas, pandas, propcache, pycparser, python-dateutil, pytz, scipy, tzdata, xarray, yarl, zarr\n",
-      "[pyodide]: Loaded aiohttp, aiosignal, asciitree, async-timeout, attrs, cffi, cfgrib, click, eccodes, findlibs, frozenlist, fsspec, gribscan, msgpack, multidict, numcodecs, numpy, openblas, pandas, propcache, pycparser, python-dateutil, pytz, scipy, tzdata, xarray, yarl, zarr\n",
-      "[pyodide]: Memory usage has grown to 202.8MiB (from 49.9MiB) for this notebook\n"
-     ]
+  "metadata": {
+    "kernelspec": {
+      "name": "python",
+      "display_name": "Python (Pyodide)",
+      "language": "python"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "python",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8"
     }
-   ],
-   "source": [
-    "import aiohttp\n",
-    "import fsspec\n",
-    "import gribscan\n",
-    "import zarr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5885a858-f6a6-429b-ae83-69e3f965f032",
-   "metadata": {},
-   "source": [
-    "The `load_hplp_data` function simplifies remote access to GRIB datasets stored on the S3 bucket. It validates input parameters, constructs the correct URL for the requested data, and loads it efficiently as an xarray dataset using the corresponding `.ref` file.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3a68e663-c6c8-49c2-9678-929d87f772b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "import requests\n",
-    "import xarray as xr\n",
-    "\n",
-    "def load_hplp_data(leveltype=None, gridtype=None, step=None, remap=False):\n",
-    "    if leveltype not in {\"pl\", \"ml\", \"sfc\", \"wave\"}:\n",
-    "        raise ValueError(f\"Invalid leveltype: '{leveltype}'. Available leveltypes: pl, ml, sfc, wave\")\n",
-    "\n",
-    "    if leveltype in {\"ml\", \"pl\"} and not gridtype:\n",
-    "        raise ValueError(f\"Gridtype is required for leveltype '{leveltype}'. Available: reduced_gg, sh\")\n",
-    "\n",
-    "    if remap and gridtype != \"sh\":\n",
-    "        raise ValueError(f\"Only 'sh' fields can be remapped.\")\n",
-    "\n",
-    "    if leveltype == \"wave\" and gridtype != \"reduced_ll\":\n",
-    "        print(\"Warning: Wave model data is stored on a reduced_ll grid.\")\n",
-    "\n",
-    "    if leveltype == \"sfc\" and gridtype != \"reduced_gg\":\n",
-    "        print(\"Warning: Surface level data is stored on a reduced_gg grid.\")\n",
-    "\n",
-    "    if step and not (leveltype == \"ml\" and gridtype == \"reduced_gg\"):\n",
-    "        print(\"Warning: Specifying 'step' is unnecessary for this configuration and will be ignored.\")\n",
-    "\n",
-    "    if leveltype in {\"sfc\", \"wave\"}:\n",
-    "        url = f\"{BASE_URL}/hplp/hplp_{leveltype}.grib\"\n",
-    "    elif leveltype == \"ml\" and gridtype == \"reduced_gg\":\n",
-    "        if step is None:\n",
-    "            raise ValueError(\n",
-    "                \"The ml reduced_gg data is split into two parts:\\n\"\n",
-    "                \"  - Steps: 0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120 (2020-07-21T00:00:00 to 2020-07-26T00:00:00)\\n\"\n",
-    "                \"  - Steps: 132, 144, 156, 168, 180, 192, 204, 216, 228, 240 (2020-07-26T12:00:00 to 2020-07-31T00:00:00)\\n\"\n",
-    "                \"Specify a step smaller than 120 for accessing the first part, \\n\"\n",
-    "                \"and a step greater or equal to 132 for accessing the second part.\"\n",
-    "            )          \n",
-    "        if step <= 120:\n",
-    "            url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_0_120.grib\"\n",
-    "        else:\n",
-    "            url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_132_240.grib\"\n",
-    "    else:\n",
-    "        url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}\" + (\"_O400.grib\" if remap else \".grib\")\n",
-    "    ref = requests.get(f\"{url}.ref\").json()\n",
-    "\n",
-    "    print(f\"Loading dataset {url}\")\n",
-    "\n",
-    "    return xr.open_dataset(\n",
-    "        \"reference://\",\n",
-    "        engine=\"zarr\",\n",
-    "        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n",
-    "        consolidated=False,\n",
-    "    )"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "10b46f55-ec2a-45ea-87c6-46173ce58696",
-   "metadata": {},
-   "source": [
-    "## Example Usage\n",
-    "\n",
-    "To load a GRIB file for pressure level parameters, you can use the following example. This will load the pressure level data stored on a `reduced_gg` grid. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "37c31fe0-661a-4b71-afb0-61f7fb804f5f",
-   "metadata": {},
-   "outputs": [
+  "nbformat_minor": 5,
+  "nbformat": 4,
+  "cells": [
+    {
+      "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
+      "cell_type": "markdown",
+      "source": "# IFS hplp experiment data",
+      "metadata": {}
+    },
+    {
+      "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
+      "cell_type": "markdown",
+      "source": "> [!NOTE]\n> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n\n> [!CAUTION]\n> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep.",
+      "metadata": {}
+    },
+    {
+      "id": "463bf2c0-ac3f-4786-9687-362393802dce",
+      "cell_type": "markdown",
+      "source": "This notebook is a comprehensive guide to accessing GRIB files for the [hplp experiment](https://apps.ecmwf.int/ifs-experiments/rd/hplp/). It includes details about the available parameters, levels, and steps, along with instructions for using the `load_hplp_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/). The GRIB files are not downloaded locally; instead, they are accessed remotely using `gribscan`, `fsspec`, and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `gribscan`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point single-time atmospheric and wave model (long window 4Dvar) outputs from the IFS.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco399), corresponding to a horizontal resolution of approximately 28 km, and with 137 vertical model levels. The 10-day weather forecast is provided in 12-hour intervals, beginning from 2020-07-21 at 00:00:00 UTC.\nThe total volume of the available dataset is about 187 GB.\n\nThe GRIB files are organized into different types of data fields based on the levels at which they are stored.\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in hPa):\n```python\n[1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000]\n```\n\nThe corresponding IDs and more information are given in the table below:",
+      "metadata": {}
+    },
+    {
+      "id": "1c1d220c-d8b8-4e7c-90f4-0625b4cbc15b",
+      "cell_type": "markdown",
+      "source": "|   paramId | gridName   | gridType   | typeOfLevel   | shortName   | packingType      |\n|----------:|:-----------|:-----------|:--------------|:------------|:-----------------|\n|       129 | not_found  | sh         | isobaricInhPa | z           | spectral_complex |\n|       130 | not_found  | sh         | isobaricInhPa | t           | spectral_complex |\n|       133 | O400       | reduced_gg | isobaricInhPa | q           | spectral_complex |\n|       135 | not_found  | sh         | isobaricInhPa | w           | spectral_complex |\n|       138 | not_found  | sh         | isobaricInhPa | vo          | spectral_complex |\n|       155 | not_found  | sh         | isobaricInhPa | d           | spectral_complex |\n|       131 | not_found  | sh         | isobaricInhPa | u           | spectral_complex |\n|       132 | not_found  | sh         | isobaricInhPa | v           | spectral_complex |\n|       157 | not_found  | sh         | isobaricInhPa | r           | spectral_complex |",
+      "metadata": {}
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_pl_reduced_gg.grib\n"
-     ]
+      "id": "789d34ac-2fdb-4d92-8c0c-c62b363a57a0",
+      "cell_type": "markdown",
+      "source": "### Model Level Parameters\n\nThe following parameters are available as model level fields:\n```python\n[\"crwc\", \"cswc\", \"z\", \"t\", \"q\", \"w\", \"vo\", \"lnsp\", \"d\", \"u\", \"v\", \"o3\", \"clwc\", \"ciwc\", \"cc\"]\n```\nThese parameters are stored at the following levels:\n```python\n[list(range(1, 138))] # Levels 1 to 137\n```\nFor more information about the model levels please see [here](https://confluence.ecmwf.int/display/UDOC/Model+level+definitions) and [here](https://confluence.ecmwf.int/display/UDOC/L137+model+level+definitions).\n\nThe corresponding IDs and more information are given in the table below:",
+      "metadata": {}
     },
     {
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-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\n",
-       "Dimensions:  (value: 654400, level: 21, time: 21)\n",
-       "Coordinates:\n",
-       "    lat      (value) float64 5MB ...\n",
-       "  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n",
-       "    lon      (value) float64 5MB ...\n",
-       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
-       "Dimensions without coordinates: value\n",
-       "Data variables:\n",
-       "    q        (time, level, value) float64 2GB ...\n",
-       "Attributes:\n",
-       "    GRIB_edition:            1\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8d6e63da-6a18-44b8-8f61-2736502626c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8d6e63da-6a18-44b8-8f61-2736502626c0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>value</span>: 654400</li><li><span class='xr-has-index'>level</span>: 21</li><li><span class='xr-has-index'>time</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-43f5ce37-a2f9-4b70-85d4-79fbc2fa8bf8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43f5ce37-a2f9-4b70-85d4-79fbc2fa8bf8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-01fb177b-facf-4779-b220-2fc346cb6572' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01fb177b-facf-4779-b220-2fc346cb6572' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44e3996a-dddb-441e-b1ab-998f4c3c46dc' class='xr-var-data-in' type='checkbox'><label for='data-44e3996a-dddb-441e-b1ab-998f4c3c46dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 500 700 850 925 1000</div><input id='attrs-36c00e96-1d19-45eb-9bd9-075d676e3994' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36c00e96-1d19-45eb-9bd9-075d676e3994' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd631c10-41aa-4e84-9963-87c8d340becf' class='xr-var-data-in' type='checkbox'><label for='data-bd631c10-41aa-4e84-9963-87c8d340becf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n",
-       "        200,  250,  300,  400,  500,  700,  850,  925, 1000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-21570a56-59ad-4fdc-8bb0-560a7d61c990' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21570a56-59ad-4fdc-8bb0-560a7d61c990' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eacfc7fa-9d0c-4450-a87d-fc5b9f47d6ad' class='xr-var-data-in' type='checkbox'><label for='data-eacfc7fa-9d0c-4450-a87d-fc5b9f47d6ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-610ac5b8-bdb9-4880-8cf7-7eeb61adae56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-610ac5b8-bdb9-4880-8cf7-7eeb61adae56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfb4ec5d-3764-4091-9db4-191e27b40025' class='xr-var-data-in' type='checkbox'><label for='data-bfb4ec5d-3764-4091-9db4-191e27b40025' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ddf1ad7c-3bda-487e-ba9b-623cc5ad98a3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ddf1ad7c-3bda-487e-ba9b-623cc5ad98a3' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9093ea3d-07ef-4e28-bace-3d259bd5913c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9093ea3d-07ef-4e28-bace-3d259bd5913c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1673522-1a16-4b97-a0e0-94dc130f76fc' class='xr-var-data-in' type='checkbox'><label for='data-f1673522-1a16-4b97-a0e0-94dc130f76fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>133</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>q</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>specific_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>q</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c56e0a4-0bd0-43b7-84b2-c30331550c98' class='xr-section-summary-in' type='checkbox'  ><label for='section-4c56e0a4-0bd0-43b7-84b2-c30331550c98' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c7600c2-6de3-4b92-be03-e4e456277b20' class='xr-index-data-in' type='checkbox'/><label for='index-2c7600c2-6de3-4b92-be03-e4e456277b20' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n",
-       "        200,  250,  300,  400,  500,  700,  850,  925, 1000],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e650c38c-6253-483d-b6d3-ee2eae70191b' class='xr-index-data-in' type='checkbox'/><label for='index-e650c38c-6253-483d-b6d3-ee2eae70191b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-31 00:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a6f703b-89ff-41c1-8f79-ff29efd1bbcd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5a6f703b-89ff-41c1-8f79-ff29efd1bbcd' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n",
-       "</dd></dl></div></li></ul></div></div>"
+      "id": "d96a88c4-7954-42b5-989b-c433d4c87f4e",
+      "cell_type": "markdown",
+      "source": "|   paramId | gridName   | gridType   | typeOfLevel   | shortName   | packingType   |\n|----------:|:-----------|:-----------|:--------------|:------------|:--------------|\n|        75 | O400       | reduced_gg | hybrid        | crwc        | grid_ieee     |\n|        76 | O400       | reduced_gg | hybrid        | cswc        | grid_ieee     |\n|       129 | not_found  | sh         | hybrid        | z           | grid_ieee     |\n|       130 | not_found  | sh         | hybrid        | t           | grid_ieee     |\n|       133 | O400       | reduced_gg | hybrid        | q           | grid_ieee     |\n|       135 | not_found  | sh         | hybrid        | w           | grid_ieee     |\n|       138 | not_found  | sh         | hybrid        | vo          | grid_ieee     |\n|       152 | not_found  | sh         | hybrid        | lnsp        | grid_ieee     |\n|       155 | not_found  | sh         | hybrid        | d           | spectral_ieee |\n|       131 | not_found  | sh         | hybrid        | u           | spectral_ieee |\n|       132 | not_found  | sh         | sh            | v           | spectral_ieee |\n|       203 | O400       | reduced_gg | sh            | o3          | spectral_ieee |\n|       246 | O400       | reduced_gg | sh            | clwc        | spectral_ieee |\n|       247 | O400       | reduced_gg | sh            | ciwc        | spectral_ieee |\n|       248 | O400       | reduced_gg | sh            | cc          | spectral_ieee |",
+      "metadata": {}
+    },
+    {
+      "id": "8539c000-1d96-439e-bc4d-2076c6f3f442",
+      "cell_type": "markdown",
+      "source": "### Surface Level Parameters\n\nThe following parameters are available as surface level fields:\n```python\n['cin', 'sro', 'ssro', 'vis', 'i10fg', 'ci', 'asn', 'rsn', 'sst', 'istl1', 'istl2', 'istl3', 'istl4', 'swvl1', 'swvl2', 'swvl3', 'swvl4', 'es', 'smlt', 'dsrp', '10fg', 'lspf', 'uvb', 'par', 'cape', 'tclw', 'tciw', 'tcrw', 'tcsw', 'z', 'ocu', 'ocv', 'tcw', 'tcwv', 'stl1', 'sd', 'lsp', 'cp', 'sf', 'bld', 'sshf', 'slhf', 'chnk', 'msl', 'blh', 'tcc', '10u', '10v', '2t', '2d', 'ssrd', 'stl2', 'lsm', 'strd', 'ssr', 'str', 'tsr', 'ttr', 'ewss', 'nsss', 'e', 'stl3', 'lcc', 'mcc', 'hcc', 'sund', 'lgws', 'mgws', 'gwd', 'src', 'mx2t', 'mn2t', 'ro', 'tco3', 'tsrc', 'ttrc', 'ssrc', 'strc',  'tisr', 'vimd', 'fzra', 'ilspf', 'crr', 'lsrr', 'csfr', 'lssfr', 'tp', 'iews', 'inss', 'ishf', 'ie', 'skt', 'stl4', 'tsn', 'fal', 'fsr', 'flsr', 'pev', 'ptype', 'kx', 'totalx']\n```\n\nThe corresponding IDs and more information are given in the (hidden) table below:",
+      "metadata": {}
+    },
+    {
+      "id": "557c2ea2-1790-4af2-921d-46110b5502d3",
+      "cell_type": "markdown",
+      "source": "|   paramId | gridName   | gridType   | typeOfLevel         | shortName   | packingType   |\n|----------:|:-----------|:-----------|:--------------------|:------------|:--------------|\n|    228001 | O400       | reduced_gg | surface             | cin         | grid_ieee     |\n|         8 | O400       | reduced_gg | surface             | sro         | grid_ieee     |\n|         9 | O400       | reduced_gg | surface             | ssro        | grid_ieee     |\n|      3020 | O400       | reduced_gg | surface             | vis         | grid_ieee     |\n|    228029 | O400       | reduced_gg | surface             | i10fg       | grid_ieee     |\n|        31 | O400       | reduced_gg | surface             | ci          | grid_ieee     |\n|        32 | O400       | reduced_gg | surface             | asn         | grid_ieee     |\n|        33 | O400       | reduced_gg | surface             | rsn         | grid_ieee     |\n|        34 | O400       | reduced_gg | surface             | sst         | grid_ieee     |\n|        35 | O400       | reduced_gg | surface             | istl1       | grid_ieee     |\n|        36 | O400       | reduced_gg | surface             | istl2       | grid_ieee     |\n|        37 | O400       | reduced_gg | surface             | istl3       | grid_ieee     |\n|        38 | O400       | reduced_gg | surface             | istl4       | grid_ieee     |\n|        39 | O400       | reduced_gg | surface             | swvl1       | grid_ieee     |\n|        40 | O400       | reduced_gg | surface             | swvl2       | grid_ieee     |\n|        41 | O400       | reduced_gg | surface             | swvl3       | grid_ieee     |\n|        42 | O400       | reduced_gg | surface             | swvl4       | grid_ieee     |\n|        44 | O400       | reduced_gg | surface             | es          | grid_ieee     |\n|        45 | O400       | reduced_gg | surface             | smlt        | grid_ieee     |\n|        47 | O400       | reduced_gg | surface             | dsrp        | grid_ieee     |\n|        49 | O400       | reduced_gg | surface             | 10fg        | grid_ieee     |\n|        50 | O400       | reduced_gg | surface             | lspf        | grid_ieee     |\n|        57 | O400       | reduced_gg | surface             | uvb         | grid_ieee     |\n|        58 | O400       | reduced_gg | surface             | par         | grid_ieee     |\n|        59 | O400       | reduced_gg | surface             | cape        | grid_ieee     |\n|        78 | O400       | reduced_gg | surface             | tclw        | grid_ieee     |\n|        79 | O400       | reduced_gg | surface             | tciw        | grid_ieee     |\n|    228089 | O400       | reduced_gg | surface             | tcrw        | grid_ieee     |\n|    228090 | O400       | reduced_gg | surface             | tcsw        | grid_ieee     |\n|       129 | O400       | reduced_gg | surface             | z           | grid_ieee     |\n|    151131 | O400       | reduced_gg | surface             | ocu         | grid_ieee     |\n|    151132 | O400       | reduced_gg | surface             | ocv         | grid_ieee     |\n|       136 | O400       | reduced_gg | surface             | tcw         | grid_ieee     |\n|       137 | O400       | reduced_gg | surface             | tcwv        | grid_ieee     |\n|       139 | O400       | reduced_gg | surface             | stl1        | grid_ieee     |\n|       141 | O400       | reduced_gg | surface             | sd          | grid_ieee     |\n|       142 | O400       | reduced_gg | surface             | lsp         | grid_ieee     |\n|       143 | O400       | reduced_gg | surface             | cp          | grid_ieee     |\n|       144 | O400       | reduced_gg | surface             | sf          | grid_ieee     |\n|       145 | O400       | reduced_gg | surface             | bld         | grid_ieee     |\n|       146 | O400       | reduced_gg | surface             | sshf        | grid_ieee     |\n|       147 | O400       | reduced_gg | surface             | slhf        | grid_ieee     |\n|       148 | O400       | reduced_gg | surface             | chnk        | grid_ieee     |\n|       151 | O400       | reduced_gg | surface             | msl         | grid_ieee     |\n|       159 | O400       | reduced_gg | surface             | blh         | grid_ieee     |\n|       164 | O400       | reduced_gg | surface             | tcc         | grid_ieee     |\n|       165 | O400       | reduced_gg | surface             | 10u         | grid_ieee     |\n|       166 | O400       | reduced_gg | surface             | 10v         | grid_ieee     |\n|       167 | O400       | reduced_gg | surface             | 2t          | grid_ieee     |\n|       168 | O400       | reduced_gg | surface             | 2d          | grid_ieee     |\n|       169 | O400       | reduced_gg | surface             | ssrd        | grid_ieee     |\n|       170 | O400       | reduced_gg | surface             | stl2        | grid_ieee     |\n|       172 | O400       | reduced_gg | surface             | lsm         | grid_ieee     |\n|       175 | O400       | reduced_gg | surface             | strd        | grid_ieee     |\n|       176 | O400       | reduced_gg | surface             | ssr         | grid_ieee     |\n|       177 | O400       | reduced_gg | surface             | str         | grid_ieee     |\n|       178 | O400       | reduced_gg | surface             | tsr         | grid_ieee     |\n|       179 | O400       | reduced_gg | surface             | ttr         | grid_ieee     |\n|       180 | O400       | reduced_gg | surface             | ewss        | grid_ieee     |\n|       181 | O400       | reduced_gg | surface             | nsss        | grid_ieee     |\n|       182 | O400       | reduced_gg | surface             | e           | grid_ieee     |\n|       183 | O400       | reduced_gg | surface             | stl3        | grid_ieee     |\n|       186 | O400       | reduced_gg | surface             | lcc         | grid_ieee     |\n|       187 | O400       | reduced_gg | surface             | mcc         | grid_ieee     |\n|       188 | O400       | reduced_gg | surface             | hcc         | grid_ieee     |\n|       189 | O400       | reduced_gg | surface             | sund        | grid_ieee     |\n|       195 | O400       | reduced_gg | surface             | lgws        | grid_ieee     |\n|       196 | O400       | reduced_gg | surface             | mgws        | grid_ieee     |\n|       197 | O400       | reduced_gg | surface             | gwd         | grid_ieee     |\n|       198 | O400       | reduced_gg | surface             | src         | grid_ieee     |\n|       201 | O400       | reduced_gg | surface             | mx2t        | grid_ieee     |\n|       202 | O400       | reduced_gg | surface             | mn2t        | grid_ieee     |\n|       205 | O400       | reduced_gg | surface             | ro          | grid_ieee     |\n|       206 | O400       | reduced_gg | surface             | tco3        | grid_ieee     |\n|       208 | O400       | reduced_gg | surface             | tsrc        | grid_ieee     |\n|       209 | O400       | reduced_gg | surface             | ttrc        | grid_ieee     |\n|       210 | O400       | reduced_gg | surface             | ssrc        | grid_ieee     |\n|       211 | O400       | reduced_gg | surface             | strc        | grid_ieee     |\n|       212 | O400       | reduced_gg | surface             | tisr        | grid_ieee     |\n|       213 | O400       | reduced_gg | surface             | vimd        | grid_ieee     |\n|    228216 | O400       | reduced_gg | surface             | fzra        | grid_ieee     |\n|    228217 | O400       | reduced_gg | surface             | ilspf       | grid_ieee     |\n|    228218 | O400       | reduced_gg | surface             | crr         | grid_ieee     |\n|    228219 | O400       | reduced_gg | surface             | lsrr        | grid_ieee     |\n|    228220 | O400       | reduced_gg | depthBelowLandLayer | csfr        | grid_ieee     |\n|    228221 | O400       | reduced_gg | depthBelowLandLayer | lssfr       | grid_ieee     |\n|       228 | O400       | reduced_gg | depthBelowLandLayer | tp          | grid_ieee     |\n|       229 | O400       | reduced_gg | depthBelowLandLayer | iews        | grid_ieee     |\n|       230 | O400       | reduced_gg | depthBelowLandLayer | inss        | grid_ieee     |\n|       231 | O400       | reduced_gg | depthBelowLandLayer | ishf        | grid_ieee     |\n|       232 | O400       | reduced_gg | depthBelowLandLayer | ie          | grid_ieee     |\n|       235 | O400       | reduced_gg | depthBelowLandLayer | skt         | grid_ieee     |\n|       236 | O400       | reduced_gg | depthBelowLandLayer | stl4        | grid_ieee     |\n|       238 | O400       | reduced_gg | depthBelowLandLayer | tsn         | grid_ieee     |\n|       243 | O400       | reduced_gg | depthBelowLandLayer | fal         | grid_ieee     |\n|       244 | O400       | reduced_gg | depthBelowLandLayer | fsr         | grid_ieee     |\n|       245 | O400       | reduced_gg | depthBelowLandLayer | flsr        | grid_ieee     |\n|    228251 | O400       | reduced_gg | depthBelowLandLayer | pev         | grid_ieee     |\n|    260015 | O400       | reduced_gg | depthBelowLandLayer | ptype       | grid_ieee     |\n|    260121 | O400       | reduced_gg | depthBelowLandLayer | kx          | grid_ieee     |\n|    260123 | O400       | reduced_gg | depthBelowLandLayer | totalx      | grid_ieee     |",
+      "metadata": {}
+    },
+    {
+      "id": "f18ec120-3193-42d4-bcda-5a8a80bc574c",
+      "cell_type": "markdown",
+      "source": "### Wave Model Parameters\n\nThe following parameters are available for the wave model data:\n```python\n['wstar', 'rhoao', 'phioc', 'tauoc', 'ust', 'vst', 'hmax', 'wmb', 'mp1', 'mp2', 'wdw', 'swh', 'mwd', 'pp1d', 'mwp', 'cdww', 'wind','dwi']\n```\n\nThe corresponding IDs and more information are given in the table below:",
+      "metadata": {}
+    },
+    {
+      "id": "968e5b37-1165-4396-b0eb-8b9e03dd7d2b",
+      "cell_type": "markdown",
+      "source": "|   paramId | gridName   | gridType   | typeOfLevel       | shortName   | packingType   |\n|----------:|:-----------|:-----------|:------------------|:------------|:--------------|\n|    140208 | not_found  | reduced_ll | meanSea           | wstar       | grid_simple   |\n|    140209 | not_found  | reduced_ll | meanSea           | rhoao       | grid_simple   |\n|    140212 | not_found  | reduced_ll | meanSea           | phioc       | grid_simple   |\n|    140214 | not_found  | reduced_ll | meanSea           | tauoc       | grid_simple   |\n|    140215 | not_found  | reduced_ll | meanSea           | ust         | grid_simple   |\n|    140216 | not_found  | reduced_ll | meanSea           | vst         | grid_simple   |\n|    140218 | not_found  | reduced_ll | meanSea           | hmax        | grid_simple   |\n|    140219 | not_found  | reduced_ll | meanSea           | wmb         | grid_simple   |\n|    140220 | not_found  | reduced_ll | meanSea           | mp1         | grid_simple   |\n|    140221 | not_found  | reduced_ll | meanSea           | mp2         | grid_simple   |\n|    140222 | not_found  | reduced_ll | meanSea           | wdw         | grid_simple   |\n|    140229 | not_found  | reduced_ll | meanSea           | swh         | grid_simple   |\n|    140230 | not_found  | reduced_ll | meanSea           | mwd         | grid_simple   |\n|    140231 | not_found  | reduced_ll | meanSea           | pp1d        | grid_simple   |\n|    140232 | not_found  | reduced_ll | meanSea           | mwp         | grid_simple   |\n|    140233 | not_found  | reduced_ll | heightAboveGround | cdww        | grid_simple   |\n|    140245 | not_found  | reduced_ll | heightAboveGround | wind        | grid_simple   |\n|    140249 | not_found  | reduced_ll | heightAboveGround | dwi         | grid_simple   |",
+      "metadata": {}
+    },
+    {
+      "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
+      "cell_type": "markdown",
+      "source": "## Available Steps \n\nThe following timesteps are available: \n\n```python \n[0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144, 156, 168, 180, 192, 204, 216, 228, 240]\n ```\nTimestep 0 is 2020-07-21T 00:00:00 UTC, increasing in 12-hour steps to 240  which is 2020-07-31T 00:00:00 UTC.\n\n## Using the `load_hplp_data` Function\n\nTo load the GRIB files, you can use the following function:\n\n```python\nload_hplp_data(leveltype=None, gridtype=None, step=None, remap=False)\n```\n\n### Parameters\n\n- **`leveltype`**: Defines the type of data to load. Choose one of the following:\n  - `\"pl\"`: Pressure level fields.\n  - `\"ml\"`: Model level fields.\n  - `\"sfc\"`: Surface level fields.\n  - `\"wave\"`: Wave model fields.\n- **`gridtype`**: Specifies the grid type for the data. Options include:\n  - `\"reduced_gg\"`: Reduced Gaussian grid (available for `pl`, `ml`, and `sfc`).\n  - `\"sh\"`: Spherical harmonic coefficients (available for `pl` and `ml`).\n  - `\"reduced_ll\"`: Reduced lat-long grid (available for `wave`).\n- **`step`**: The `ml` data on the `reduced_gg` grid is split into two files. Specify the timestep (`step`) to load the corresponding file. Valid steps are:\n  - `0-120`: First file (2020-07-21T00:00:00 to 2020-07-26T00:00:00).\n  - `132-240`: Second file (2020-07-26T12:00:00 to 2020-07-31T00:00:00).\n- **`remap`**: When using gridtype=`sh`, data cannot be directly plotted. To load remapped data on an O400 grid, set `remap=True`.",
+      "metadata": {}
+    },
+    {
+      "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
+      "cell_type": "code",
+      "source": "# URL of S3 bucket\nBASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\"",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": 1
+    },
+    {
+      "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
+      "cell_type": "markdown",
+      "source": "First, the package `zarr` (modern dataset format that is specifically designed for chunked access), its dependencies, and `gribscan` need to be imported for the remote access.",
+      "metadata": {}
+    },
+    {
+      "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
+      "cell_type": "code",
+      "source": "import aiohttp\nimport fsspec\nimport gribscan\nimport zarr",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Loading aiohttp, aiosignal, asciitree, async-timeout, attrs, cffi, cfgrib, click, eccodes, findlibs, frozenlist, fsspec, gribscan, msgpack, multidict, numcodecs, numpy, openblas, pandas, propcache, pycparser, python-dateutil, pytz, scipy, tzdata, xarray, yarl, zarr\n[pyodide]: Loaded aiohttp, aiosignal, asciitree, async-timeout, attrs, cffi, cfgrib, click, eccodes, findlibs, frozenlist, fsspec, gribscan, msgpack, multidict, numcodecs, numpy, openblas, pandas, propcache, pycparser, python-dateutil, pytz, scipy, tzdata, xarray, yarl, zarr\n[pyodide]: Memory usage has grown to 202.8MiB (from 49.9MiB) for this notebook\n",
+          "output_type": "stream"
+        }
       ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 2GB\n",
-       "Dimensions:  (value: 654400, level: 21, time: 21)\n",
-       "Coordinates:\n",
-       "    lat      (value) float64 5MB ...\n",
-       "  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n",
-       "    lon      (value) float64 5MB ...\n",
-       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
-       "Dimensions without coordinates: value\n",
-       "Data variables:\n",
-       "    q        (time, level, value) float64 2GB ...\n",
-       "Attributes:\n",
-       "    GRIB_edition:            1\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc..."
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_pl = load_hplp_data(leveltype=\"pl\", gridtype=\"reduced_gg\")\n",
-    "ds_pl"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33418b16-9735-4eb9-b540-942325d552df",
-   "metadata": {},
-   "source": [
-    "The `earthkit` package is used for plotting the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "169008a5-cfa4-4078-8bad-32510c9a91e7",
-   "metadata": {},
-   "outputs": [
+      "execution_count": 2
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[pyodide]: Loading Cartopy, Jinja2, MarkupSafe, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cftime, cligj, cloudpickle, cycler, dask, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, importlib_metadata, jsonschema, jsonschema_specifications, kiwisolver, locket, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, partd, pdbufr, pint, pkgconfig, pyparsing, pyproj, pyrsistent, pyshp, pyyaml, rasterio, referencing, rpds-py, setuptools, shapely, toolz, tqdm, typing-extensions, zipp\n",
-      "[pyodide]: Loaded Cartopy, Jinja2, MarkupSafe, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cftime, cligj, cloudpickle, cycler, dask, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, importlib_metadata, jsonschema, jsonschema_specifications, kiwisolver, locket, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, partd, pdbufr, pint, pkgconfig, pyparsing, pyproj, pyrsistent, pyshp, pyyaml, rasterio, referencing, rpds-py, setuptools, shapely, toolz, tqdm, typing-extensions, zipp\n"
-     ]
+      "id": "5885a858-f6a6-429b-ae83-69e3f965f032",
+      "cell_type": "markdown",
+      "source": "The `load_hplp_data` function simplifies remote access to GRIB datasets stored on the S3 bucket. It validates input parameters, constructs the correct URL for the requested data, and loads it efficiently as an xarray dataset using the corresponding `.ref` file.\n",
+      "metadata": {}
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/lib/python3.12/site-packages/earthkit/regrid/utils/caching.py:27: UserWarning: only soft file lock is available\n",
-      "  from filelock import FileLock\n"
-     ]
+      "id": "3a68e663-c6c8-49c2-9678-929d87f772b4",
+      "cell_type": "code",
+      "source": "import json\n\nimport requests\nimport xarray as xr\n\ndef load_hplp_data(leveltype=None, gridtype=None, step=None, remap=False):\n    if leveltype not in {\"pl\", \"ml\", \"sfc\", \"wave\"}:\n        raise ValueError(f\"Invalid leveltype: '{leveltype}'. Available leveltypes: pl, ml, sfc, wave\")\n\n    if leveltype in {\"ml\", \"pl\"} and not gridtype:\n        raise ValueError(f\"Gridtype is required for leveltype '{leveltype}'. Available: reduced_gg, sh\")\n\n    if remap and gridtype != \"sh\":\n        raise ValueError(f\"Only 'sh' fields can be remapped.\")\n\n    if leveltype == \"wave\" and gridtype != \"reduced_ll\":\n        print(\"Warning: Wave model data is stored on a reduced_ll grid.\")\n\n    if leveltype == \"sfc\" and gridtype != \"reduced_gg\":\n        print(\"Warning: Surface level data is stored on a reduced_gg grid.\")\n\n    if step and not (leveltype == \"ml\" and gridtype == \"reduced_gg\"):\n        print(\"Warning: Specifying 'step' is unnecessary for this configuration and will be ignored.\")\n\n    if leveltype in {\"sfc\", \"wave\"}:\n        url = f\"{BASE_URL}/hplp/hplp_{leveltype}.grib\"\n    elif leveltype == \"ml\" and gridtype == \"reduced_gg\":\n        if step is None:\n            raise ValueError(\n                \"The ml reduced_gg data is split into two parts:\\n\"\n                \"  - Steps: 0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120 (2020-07-21T00:00:00 to 2020-07-26T00:00:00)\\n\"\n                \"  - Steps: 132, 144, 156, 168, 180, 192, 204, 216, 228, 240 (2020-07-26T12:00:00 to 2020-07-31T00:00:00)\\n\"\n                \"Specify a step smaller than 120 for accessing the first part, \\n\"\n                \"and a step greater or equal to 132 for accessing the second part.\"\n            )          \n        if step <= 120:\n            url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_0_120.grib\"\n        else:\n            url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_132_240.grib\"\n    else:\n        url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}\" + (\"_O400.grib\" if remap else \".grib\")\n    ref = requests.get(f\"{url}.ref\").json()\n\n    print(f\"Loading dataset {url}\")\n\n    return xr.open_dataset(\n        \"reference://\",\n        engine=\"zarr\",\n        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n        consolidated=False,\n    )",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": 3
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[pyodide]: Memory usage has grown to 292.0MiB (from 202.8MiB) for this notebook\n"
-     ]
-    }
-   ],
-   "source": [
-    "import earthkit.plots\n",
-    "import earthkit.plots.quickmap"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6ee55166-f151-474c-b36c-1e07bc516906",
-   "metadata": {},
-   "source": [
-    "The data can be visualized as demonstrated below. For example, the specific humidity at **pressure level 1000hPa** on **2020-07-31 at 00:00:00 UTC** is plotted.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2e12e256",
-   "metadata": {},
-   "outputs": [
+      "id": "10b46f55-ec2a-45ea-87c6-46173ce58696",
+      "cell_type": "markdown",
+      "source": "## Example Usage\n\nTo load a GRIB file for pressure level parameters, you can use the following example. This will load the pressure level data stored on a `reduced_gg` grid. \n",
+      "metadata": {}
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip\n",
-      "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
-     ]
+      "id": "37c31fe0-661a-4b71-afb0-61f7fb804f5f",
+      "cell_type": "code",
+      "source": "ds_pl = load_hplp_data(leveltype=\"pl\", gridtype=\"reduced_gg\")\nds_pl",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_pl_reduced_gg.grib\n",
+          "output_type": "stream"
+        },
+        {
+          "execution_count": 4,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "<xarray.Dataset> Size: 2GB\nDimensions:  (value: 654400, level: 21, time: 21)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    q        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...",
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2;\n}\n\n.xr-array-data,\n.xr-array-in:checked ~ .xr-array-preview {\n  display: none;\n}\n\n.xr-array-in:checked ~ .xr-array-data,\n.xr-array-preview {\n  display: inline-block;\n}\n\n.xr-dim-list {\n  display: inline-block !important;\n  list-style: none;\n  padding: 0 !important;\n  margin: 0;\n}\n\n.xr-dim-list li {\n  display: inline-block;\n  padding: 0;\n  margin: 0;\n}\n\n.xr-dim-list:before {\n  content: '(';\n}\n\n.xr-dim-list:after {\n  content: ')';\n}\n\n.xr-dim-list li:not(:last-child):after {\n  content: ',';\n  padding-right: 5px;\n}\n\n.xr-has-index {\n  font-weight: bold;\n}\n\n.xr-var-list,\n.xr-var-item {\n  display: contents;\n}\n\n.xr-var-item > div,\n.xr-var-item label,\n.xr-var-item > .xr-var-name span {\n  background-color: var(--xr-background-color-row-even);\n  margin-bottom: 0;\n}\n\n.xr-var-item > .xr-var-name:hover span {\n  padding-right: 5px;\n}\n\n.xr-var-list > li:nth-child(odd) > div,\n.xr-var-list > li:nth-child(odd) > label,\n.xr-var-list > li:nth-child(odd) > .xr-var-name span {\n  background-color: var(--xr-background-color-row-odd);\n}\n\n.xr-var-name {\n  grid-column: 1;\n}\n\n.xr-var-dims {\n  grid-column: 2;\n}\n\n.xr-var-dtype {\n  grid-column: 3;\n  text-align: right;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-preview {\n  grid-column: 4;\n}\n\n.xr-index-preview {\n  grid-column: 2 / 5;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-name,\n.xr-var-dims,\n.xr-var-dtype,\n.xr-preview,\n.xr-attrs dt {\n  white-space: nowrap;\n  overflow: hidden;\n  text-overflow: ellipsis;\n  padding-right: 10px;\n}\n\n.xr-var-name:hover,\n.xr-var-dims:hover,\n.xr-var-dtype:hover,\n.xr-attrs dt:hover {\n  overflow: visible;\n  width: auto;\n  z-index: 1;\n}\n\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  display: none;\n  background-color: var(--xr-background-color) !important;\n  padding-bottom: 5px !important;\n}\n\n.xr-var-attrs-in:checked ~ .xr-var-attrs,\n.xr-var-data-in:checked ~ .xr-var-data,\n.xr-index-data-in:checked ~ .xr-index-data {\n  display: block;\n}\n\n.xr-var-data > table {\n  float: right;\n}\n\n.xr-var-name span,\n.xr-var-data,\n.xr-index-name div,\n.xr-index-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2,\n.xr-no-icon {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\nDimensions:  (value: 654400, level: 21, time: 21)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    q        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1fedd7af-88be-466e-ac2c-4cabc1fce06a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1fedd7af-88be-466e-ac2c-4cabc1fce06a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>value</span>: 654400</li><li><span class='xr-has-index'>level</span>: 21</li><li><span class='xr-has-index'>time</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d3c1afa5-1576-40cb-9f93-eda1bf842f26' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3c1afa5-1576-40cb-9f93-eda1bf842f26' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-52aeb07b-6085-4863-9344-144c109afac9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52aeb07b-6085-4863-9344-144c109afac9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-915330a2-7547-4b0e-bccc-6e367fb50b89' class='xr-var-data-in' type='checkbox'><label for='data-915330a2-7547-4b0e-bccc-6e367fb50b89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 500 700 850 925 1000</div><input id='attrs-d0518e50-5241-4b11-a7bf-9c28fda6ce2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0518e50-5241-4b11-a7bf-9c28fda6ce2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-694892fc-0273-4217-8583-c45811c0c374' class='xr-var-data-in' type='checkbox'><label for='data-694892fc-0273-4217-8583-c45811c0c374' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fab5b8d7-3519-4bd4-88fe-0d7e315432f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fab5b8d7-3519-4bd4-88fe-0d7e315432f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7484b4f4-1c27-48d5-af9a-b10e636c743b' class='xr-var-data-in' type='checkbox'><label for='data-7484b4f4-1c27-48d5-af9a-b10e636c743b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-ec771e90-65ea-4c1c-a9a5-3490d9a80f1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec771e90-65ea-4c1c-a9a5-3490d9a80f1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4f2bc91-8f46-45c7-b8c7-b24e55a8ffe2' class='xr-var-data-in' type='checkbox'><label for='data-b4f2bc91-8f46-45c7-b8c7-b24e55a8ffe2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a47b7d1-ab02-40c0-ac70-85a9e304f9e5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a47b7d1-ab02-40c0-ac70-85a9e304f9e5' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-487ee364-bbb4-46f0-8baf-5a6edc01fd83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-487ee364-bbb4-46f0-8baf-5a6edc01fd83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-203afd7d-c41f-4169-9f01-b153ab984bee' class='xr-var-data-in' type='checkbox'><label for='data-203afd7d-c41f-4169-9f01-b153ab984bee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>133</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>q</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>specific_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>q</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5edf564f-d246-4824-9cf8-a257bfb49e8a' class='xr-section-summary-in' type='checkbox'  ><label for='section-5edf564f-d246-4824-9cf8-a257bfb49e8a' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2aa76ac4-aa5c-44f9-849e-22dceda5ca41' class='xr-index-data-in' type='checkbox'/><label for='index-2aa76ac4-aa5c-44f9-849e-22dceda5ca41' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000],\n      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe07ca7a-bb99-4322-b232-407be8755434' class='xr-index-data-in' type='checkbox'/><label for='index-fe07ca7a-bb99-4322-b232-407be8755434' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16850edf-eb00-4290-98ff-053792f7de16' class='xr-section-summary-in' type='checkbox'  checked><label for='section-16850edf-eb00-4290-98ff-053792f7de16' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 4
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[pyodide]: Memory usage has grown to 793.9MiB (from 292.0MiB) for this notebook\n"
-     ]
+      "id": "33418b16-9735-4eb9-b540-942325d552df",
+      "cell_type": "markdown",
+      "source": "The `earthkit` package is used for plotting the data.",
+      "metadata": {}
     },
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 700x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "earthkit.plots.quickmap.plot(\n",
-    "    ds_pl[\"q\"].sel(level=1000).sel(time=\"2020-07-31T00:00:00\"),\n",
-    "    x=\"lon\", y=\"lat\",\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7731f334-8487-4256-ac41-8b693a504b35",
-   "metadata": {},
-   "source": [
-    "The following example will load GRIB files for pressure level data stored as **spherical harmonic coefficients (sh)**. Since this data requires remapping to an **O400 grid** for visualization, the remapping step is included.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "5a574d92-3c0a-429b-97e8-d024d78c5daf",
-   "metadata": {},
-   "outputs": [
+      "id": "169008a5-cfa4-4078-8bad-32510c9a91e7",
+      "cell_type": "code",
+      "source": "import earthkit.plots\nimport earthkit.plots.quickmap",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Loading Cartopy, Jinja2, MarkupSafe, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cftime, cligj, cloudpickle, cycler, dask, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, importlib_metadata, jsonschema, jsonschema_specifications, kiwisolver, locket, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, partd, pdbufr, pint, pkgconfig, pyparsing, pyproj, pyrsistent, pyshp, pyyaml, rasterio, referencing, rpds-py, setuptools, shapely, toolz, tqdm, typing-extensions, zipp\n[pyodide]: Loaded Cartopy, Jinja2, MarkupSafe, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cftime, cligj, cloudpickle, cycler, dask, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, importlib_metadata, jsonschema, jsonschema_specifications, kiwisolver, locket, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, partd, pdbufr, pint, pkgconfig, pyparsing, pyproj, pyrsistent, pyshp, pyyaml, rasterio, referencing, rpds-py, setuptools, shapely, toolz, tqdm, typing-extensions, zipp\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stderr",
+          "text": "/lib/python3.12/site-packages/earthkit/regrid/utils/caching.py:27: UserWarning: only soft file lock is available\n  from filelock import FileLock\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 292.0MiB (from 202.8MiB) for this notebook\n",
+          "output_type": "stream"
+        }
+      ],
+      "execution_count": 5
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_pl_sh_O400.grib\n"
-     ]
+      "id": "6ee55166-f151-474c-b36c-1e07bc516906",
+      "cell_type": "markdown",
+      "source": "The data can be visualized as demonstrated below. For example, the specific humidity at **pressure level 1000hPa** on **2020-07-31 at 00:00:00 UTC** is plotted.\n",
+      "metadata": {}
     },
     {
-     "data": {
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-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "html[data-theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
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-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block !important;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
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-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
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-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
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-       "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
-       "  opacity: 0;\n",
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-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
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-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0);\n",
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-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
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-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
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-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 18GB\n",
-       "Dimensions:  (time: 21, level: 21, value: 654400)\n",
-       "Coordinates:\n",
-       "    lat      (value) float64 5MB ...\n",
-       "  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n",
-       "    lon      (value) float64 5MB ...\n",
-       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
-       "Dimensions without coordinates: value\n",
-       "Data variables:\n",
-       "    d        (time, level, value) float64 2GB ...\n",
-       "    r        (time, level, value) float64 2GB ...\n",
-       "    t        (time, level, value) float64 2GB ...\n",
-       "    u        (time, level, value) float64 2GB ...\n",
-       "    v        (time, level, value) float64 2GB ...\n",
-       "    vo       (time, level, value) float64 2GB ...\n",
-       "    w        (time, level, value) float64 2GB ...\n",
-       "    z        (time, level, value) float64 2GB ...\n",
-       "Attributes:\n",
-       "    GRIB_edition:            1\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4e8cf0f9-ac10-4de8-83bb-07808e533ee8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4e8cf0f9-ac10-4de8-83bb-07808e533ee8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span class='xr-has-index'>level</span>: 21</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-35b6a18d-2cec-4761-af34-3de25e69a74b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-35b6a18d-2cec-4761-af34-3de25e69a74b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-82c0e70f-19a2-4a7c-a0a7-81e3af27c769' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82c0e70f-19a2-4a7c-a0a7-81e3af27c769' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb71df00-ba5e-4654-bf53-56f54c39148f' class='xr-var-data-in' type='checkbox'><label for='data-bb71df00-ba5e-4654-bf53-56f54c39148f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 500 700 850 925 1000</div><input id='attrs-585ef21b-f7ca-417a-a7d6-811da5045e35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-585ef21b-f7ca-417a-a7d6-811da5045e35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21edf3ce-16ff-45f3-9438-b5c92da107cf' class='xr-var-data-in' type='checkbox'><label for='data-21edf3ce-16ff-45f3-9438-b5c92da107cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n",
-       "        200,  250,  300,  400,  500,  700,  850,  925, 1000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-918e9901-c227-411f-9bf4-47b0a7ebfa6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-918e9901-c227-411f-9bf4-47b0a7ebfa6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2422212-d2c4-4dc2-8a74-f452dff1e90b' class='xr-var-data-in' type='checkbox'><label for='data-f2422212-d2c4-4dc2-8a74-f452dff1e90b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-e1d7eb9b-8cb2-4583-9a81-5185a3b010fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1d7eb9b-8cb2-4583-9a81-5185a3b010fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de19481b-dd0b-455a-9c38-1c33974d2796' class='xr-var-data-in' type='checkbox'><label for='data-de19481b-dd0b-455a-9c38-1c33974d2796' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4f925b9d-2efb-48ea-9ce9-f55b32e020db' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4f925b9d-2efb-48ea-9ce9-f55b32e020db' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>d</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-609760c9-c819-4d24-9b42-6006fd80ec4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-609760c9-c819-4d24-9b42-6006fd80ec4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3a5df56-abcd-4823-9629-c18712e03366' class='xr-var-data-in' type='checkbox'><label for='data-f3a5df56-abcd-4823-9629-c18712e03366' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>155</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>d</dd><dt><span>GRIB_units :</span></dt><dd>s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Divergence</dd><dt><span>GRIB_cfName :</span></dt><dd>divergence_of_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s**-1</dd><dt><span>long_name :</span></dt><dd>Divergence</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3d8af75b-b0ed-4c97-8df0-66a5040d6a30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d8af75b-b0ed-4c97-8df0-66a5040d6a30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c94b95c-6867-47be-9ad8-9fa3219e4f90' class='xr-var-data-in' type='checkbox'><label for='data-1c94b95c-6867-47be-9ad8-9fa3219e4f90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>157</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>r</dd><dt><span>GRIB_units :</span></dt><dd>%</dd><dt><span>GRIB_name :</span></dt><dd>Relative humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>relative_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>r</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Relative humidity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-37c24bfe-27db-4351-805d-0230628906d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37c24bfe-27db-4351-805d-0230628906d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b38a2b98-4464-40b3-8d6d-0fbaf71cc9ec' class='xr-var-data-in' type='checkbox'><label for='data-b38a2b98-4464-40b3-8d6d-0fbaf71cc9ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>130</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Temperature</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49d8e546-9b54-4846-bbef-5606ced2e7a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49d8e546-9b54-4846-bbef-5606ced2e7a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-949dd32e-92e8-40e1-8439-12697a82d88b' class='xr-var-data-in' type='checkbox'><label for='data-949dd32e-92e8-40e1-8439-12697a82d88b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>131</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>u</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>U component of wind</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>U component of wind</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-58369383-d9d3-4fb4-b13e-b1da2943a630' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58369383-d9d3-4fb4-b13e-b1da2943a630' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93de18dd-5e19-4d13-999d-e7816bbe3284' class='xr-var-data-in' type='checkbox'><label for='data-93de18dd-5e19-4d13-999d-e7816bbe3284' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>132</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>v</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>V component of wind</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>V component of wind</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vo</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b05ab3c-6fde-4903-9ffd-c6b747f83941' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b05ab3c-6fde-4903-9ffd-c6b747f83941' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77413552-0afa-44dc-8f42-958d656073f0' class='xr-var-data-in' type='checkbox'><label for='data-77413552-0afa-44dc-8f42-958d656073f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>138</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vo</dd><dt><span>GRIB_units :</span></dt><dd>s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Vorticity (relative)</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_relative_vorticity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vo</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s**-1</dd><dt><span>long_name :</span></dt><dd>Vorticity (relative)</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6051ead6-4a2b-463e-9658-63f979849af8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6051ead6-4a2b-463e-9658-63f979849af8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-382c0893-769b-4b79-afc2-8adc4ce4f13d' class='xr-var-data-in' type='checkbox'><label for='data-382c0893-769b-4b79-afc2-8adc4ce4f13d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>135</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>w</dd><dt><span>GRIB_units :</span></dt><dd>Pa s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Vertical velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd><dt><span>GRIB_cfVarName :</span></dt><dd>w</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 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1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Pa s**-1</dd><dt><span>long_name :</span></dt><dd>Vertical velocity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e991dac9-ad83-429c-8577-8e7f9dcfaed0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e991dac9-ad83-429c-8577-8e7f9dcfaed0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81490b7e-25c6-4958-b2bf-da2a0a818dde' class='xr-var-data-in' type='checkbox'><label for='data-81490b7e-25c6-4958-b2bf-da2a0a818dde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>long_name :</span></dt><dd>Geopotential</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a063e59e-2972-4360-a210-2b0868da8c83' class='xr-section-summary-in' type='checkbox'  ><label for='section-a063e59e-2972-4360-a210-2b0868da8c83' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-21b367d4-2904-4213-8d80-ce38d6e85f20' class='xr-index-data-in' type='checkbox'/><label for='index-21b367d4-2904-4213-8d80-ce38d6e85f20' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n",
-       "        200,  250,  300,  400,  500,  700,  850,  925, 1000],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f306d80f-eaf6-4cdb-b8ce-84f85f51b28c' class='xr-index-data-in' type='checkbox'/><label for='index-f306d80f-eaf6-4cdb-b8ce-84f85f51b28c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-31 00:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-45c8227b-87c4-437e-bf70-6c9123ca24e2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45c8227b-87c4-437e-bf70-6c9123ca24e2' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n",
-       "</dd></dl></div></li></ul></div></div>"
+      "id": "2e12e256",
+      "cell_type": "code",
+      "source": "earthkit.plots.quickmap.plot(\n    ds_pl[\"q\"].sel(level=1000).sel(time=\"2020-07-31T00:00:00\"),\n    x=\"lon\", y=\"lat\",\n);",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "text": "/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip\n  warnings.warn(f'Downloading: {url}', DownloadWarning)\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 794.0MiB (from 292.0MiB) for this notebook\n",
+          "output_type": "stream"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 700x600 with 2 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 18GB\n",
-       "Dimensions:  (time: 21, level: 21, value: 654400)\n",
-       "Coordinates:\n",
-       "    lat      (value) float64 5MB ...\n",
-       "  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n",
-       "    lon      (value) float64 5MB ...\n",
-       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
-       "Dimensions without coordinates: value\n",
-       "Data variables:\n",
-       "    d        (time, level, value) float64 2GB ...\n",
-       "    r        (time, level, value) float64 2GB ...\n",
-       "    t        (time, level, value) float64 2GB ...\n",
-       "    u        (time, level, value) float64 2GB ...\n",
-       "    v        (time, level, value) float64 2GB ...\n",
-       "    vo       (time, level, value) float64 2GB ...\n",
-       "    w        (time, level, value) float64 2GB ...\n",
-       "    z        (time, level, value) float64 2GB ...\n",
-       "Attributes:\n",
-       "    GRIB_edition:            1\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc..."
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_pl_O400 = load_hplp_data(leveltype=\"pl\", gridtype=\"sh\", remap=True)\n",
-    "ds_pl_O400"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32f314be-e6dc-4a81-b12c-c740fcc043d9",
-   "metadata": {},
-   "source": [
-    "The data can be visualized as demonstrated below. For example, the temperature at **pressure level 1hPa** on **2020-07-31 at 00:00:00 UTC** is plotted.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "36e11d63-baec-4897-a5d1-26195cf5545f",
-   "metadata": {},
-   "outputs": [
+      "execution_count": 6
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[pyodide]: Memory usage has grown to 923.7MiB (from 793.9MiB) for this notebook\n"
-     ]
+      "id": "7731f334-8487-4256-ac41-8b693a504b35",
+      "cell_type": "markdown",
+      "source": "The following example will load GRIB files for pressure level data stored as **spherical harmonic coefficients (sh)**. Since this data requires remapping to an **O400 grid** for visualization, the remapping step is included.\n",
+      "metadata": {}
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "earthkit.plots.quickmap.plot(\n",
-    "    ds_pl_O400[\"t\"].sel(level=1).sel(time=\"2020-07-31T00:00:00\"),\n",
-    "    x=\"lon\", y=\"lat\",\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "92cc1ccd-bb9b-48f2-81ab-9641c75d84ec",
-   "metadata": {},
-   "source": [
-    "The model level data stored on a **reduced_gg** grid is split into two files. Specifying a timestep of `240` ensures that the second part of the data is loaded, covering the time range from **2020-07-26 12:00:00 UTC** to **2020-07-31 00:00:00 UTC**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b287eccb-eb9b-47b1-95c2-7c66dc02b033",
-   "metadata": {},
-   "outputs": [
+      "id": "5a574d92-3c0a-429b-97e8-d024d78c5daf",
+      "cell_type": "code",
+      "source": "ds_pl_O400 = load_hplp_data(leveltype=\"pl\", gridtype=\"sh\", remap=True)\nds_pl_O400",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_pl_sh_O400.grib\n",
+          "output_type": "stream"
+        },
+        {
+          "execution_count": 7,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "<xarray.Dataset> Size: 18GB\nDimensions:  (time: 21, level: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    d        (time, level, value) float64 2GB ...\n    r        (time, level, value) float64 2GB ...\n    t        (time, level, value) float64 2GB ...\n    u        (time, level, value) float64 2GB ...\n    v        (time, level, value) float64 2GB ...\n    vo       (time, level, value) float64 2GB ...\n    w        (time, level, value) float64 2GB ...\n    z        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 18GB\nDimensions:  (time: 21, level: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    d        (time, level, value) float64 2GB ...\n    r        (time, level, value) float64 2GB ...\n    t        (time, level, value) float64 2GB ...\n    u        (time, level, value) float64 2GB ...\n    v        (time, level, value) float64 2GB ...\n    vo       (time, level, value) float64 2GB ...\n    w        (time, level, value) float64 2GB ...\n    z        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-db64db46-3c06-4308-84e5-6810970ad240' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-db64db46-3c06-4308-84e5-6810970ad240' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span class='xr-has-index'>level</span>: 21</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e8e951c3-2f01-4f19-9951-fbbd27dda11c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e8e951c3-2f01-4f19-9951-fbbd27dda11c' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fd9f22cf-4eac-47f8-b601-88e62dbee548' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd9f22cf-4eac-47f8-b601-88e62dbee548' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18628bc9-5d29-4f93-8920-e13f14d576b7' class='xr-var-data-in' type='checkbox'><label for='data-18628bc9-5d29-4f93-8920-e13f14d576b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 500 700 850 925 1000</div><input id='attrs-0316f366-c93e-4230-a52a-55e2a3270ae8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0316f366-c93e-4230-a52a-55e2a3270ae8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b40b191-91aa-488c-b89c-a78f796d6718' class='xr-var-data-in' type='checkbox'><label for='data-8b40b191-91aa-488c-b89c-a78f796d6718' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-15699af3-45b5-4526-8644-d08c1c9dadf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15699af3-45b5-4526-8644-d08c1c9dadf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a7040c8-6166-45ae-897e-6ef459653f1e' class='xr-var-data-in' type='checkbox'><label for='data-7a7040c8-6166-45ae-897e-6ef459653f1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-fe2366e6-0112-42ef-b2cc-82098fb40e6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe2366e6-0112-42ef-b2cc-82098fb40e6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80bab319-1839-4777-9fa7-d2cd86ccb017' class='xr-var-data-in' type='checkbox'><label for='data-80bab319-1839-4777-9fa7-d2cd86ccb017' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c7279364-832d-44d6-9756-86e7c551732b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c7279364-832d-44d6-9756-86e7c551732b' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>d</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf1695e6-65ee-4406-9b51-5f1e6d48e8fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf1695e6-65ee-4406-9b51-5f1e6d48e8fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c774e0c-e7c4-4a15-8180-53be570471fa' class='xr-var-data-in' type='checkbox'><label for='data-0c774e0c-e7c4-4a15-8180-53be570471fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>155</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>d</dd><dt><span>GRIB_units :</span></dt><dd>s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Divergence</dd><dt><span>GRIB_cfName :</span></dt><dd>divergence_of_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s**-1</dd><dt><span>long_name :</span></dt><dd>Divergence</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b126f268-44b7-4446-ab86-41a6ae4a4792' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b126f268-44b7-4446-ab86-41a6ae4a4792' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c331bbd-0428-4c45-9e68-2bce044a80f5' class='xr-var-data-in' type='checkbox'><label for='data-0c331bbd-0428-4c45-9e68-2bce044a80f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>157</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>r</dd><dt><span>GRIB_units :</span></dt><dd>%</dd><dt><span>GRIB_name :</span></dt><dd>Relative humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>relative_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>r</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Relative humidity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49230fda-44d9-4bf7-b0a0-3f64fef8784f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49230fda-44d9-4bf7-b0a0-3f64fef8784f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1172b8db-3efe-4b98-873e-a31785ef2b72' class='xr-var-data-in' type='checkbox'><label for='data-1172b8db-3efe-4b98-873e-a31785ef2b72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>130</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Temperature</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9d75d8d-b736-4ebb-9c02-a5d5cf473a8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9d75d8d-b736-4ebb-9c02-a5d5cf473a8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1babf3e4-691e-4246-a9ae-64144bc60925' class='xr-var-data-in' type='checkbox'><label for='data-1babf3e4-691e-4246-a9ae-64144bc60925' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>131</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>u</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>U component of wind</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>U component of wind</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-12cd3613-e3f1-41b3-898b-12383807300f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12cd3613-e3f1-41b3-898b-12383807300f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61a711da-4f80-4b28-9f4a-0ae72a2733b7' class='xr-var-data-in' type='checkbox'><label for='data-61a711da-4f80-4b28-9f4a-0ae72a2733b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>132</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>v</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>V component of wind</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>V component of wind</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vo</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dbfd4f75-ac41-43db-ab8e-d1a7e51361a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbfd4f75-ac41-43db-ab8e-d1a7e51361a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9804b2b3-41e3-4a69-9ef8-68fc1032344d' class='xr-var-data-in' type='checkbox'><label for='data-9804b2b3-41e3-4a69-9ef8-68fc1032344d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>138</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vo</dd><dt><span>GRIB_units :</span></dt><dd>s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Vorticity (relative)</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_relative_vorticity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vo</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s**-1</dd><dt><span>long_name :</span></dt><dd>Vorticity (relative)</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0717440-277b-4d6c-8f3a-9bb73334dda3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0717440-277b-4d6c-8f3a-9bb73334dda3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8894c3b9-3856-45d2-b5aa-544dfbb9ea2a' class='xr-var-data-in' type='checkbox'><label for='data-8894c3b9-3856-45d2-b5aa-544dfbb9ea2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>135</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>w</dd><dt><span>GRIB_units :</span></dt><dd>Pa s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Vertical velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd><dt><span>GRIB_cfVarName :</span></dt><dd>w</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 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384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Pa s**-1</dd><dt><span>long_name :</span></dt><dd>Vertical velocity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-06d5b0d3-9487-4415-8cac-a5297f2c6e50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06d5b0d3-9487-4415-8cac-a5297f2c6e50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f999bc5-6b9f-4fcc-90a9-e6b46361b65e' class='xr-var-data-in' type='checkbox'><label for='data-2f999bc5-6b9f-4fcc-90a9-e6b46361b65e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 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112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>long_name :</span></dt><dd>Geopotential</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b4f42500-6f4b-448b-a9ff-67424319e662' class='xr-section-summary-in' type='checkbox'  ><label for='section-b4f42500-6f4b-448b-a9ff-67424319e662' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a41a635d-5f15-4ed0-9f06-5cf9341add00' class='xr-index-data-in' type='checkbox'/><label for='index-a41a635d-5f15-4ed0-9f06-5cf9341add00' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000],\n      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-306f042a-5159-4a9b-ad3a-52a490bb44a9' class='xr-index-data-in' type='checkbox'/><label for='index-306f042a-5159-4a9b-ad3a-52a490bb44a9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b750efdb-0568-4af9-ba6e-e5a4eeed59eb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b750efdb-0568-4af9-ba6e-e5a4eeed59eb' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 7
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_ml_reduced_gg_levels_132_240.grib\n"
-     ]
+      "id": "32f314be-e6dc-4a81-b12c-c740fcc043d9",
+      "cell_type": "markdown",
+      "source": "The data can be visualized as demonstrated below. For example, the temperature at **pressure level 1hPa** on **2020-07-31 at 00:00:00 UTC** is plotted.\n",
+      "metadata": {}
     },
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-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 50GB\n",
-       "Dimensions:  (time: 10, level: 137, value: 654400)\n",
-       "Coordinates:\n",
-       "    lat      (value) float64 5MB ...\n",
-       "  * level    (level) int64 1kB 1 2 3 4 5 6 7 8 ... 131 132 133 134 135 136 137\n",
-       "    lon      (value) float64 5MB ...\n",
-       "  * time     (time) datetime64[ns] 80B 2020-07-26T12:00:00 ... 2020-07-31\n",
-       "Dimensions without coordinates: value\n",
-       "Data variables:\n",
-       "    cc       (time, level, value) float64 7GB ...\n",
-       "    ciwc     (time, level, value) float64 7GB ...\n",
-       "    clwc     (time, level, value) float64 7GB ...\n",
-       "    crwc     (time, level, value) float64 7GB ...\n",
-       "    cswc     (time, level, value) float64 7GB ...\n",
-       "    o3       (time, level, value) float64 7GB ...\n",
-       "    q        (time, level, value) float64 7GB ...\n",
-       "Attributes:\n",
-       "    GRIB_edition:            2\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7c5f2836-fb72-4671-8555-e31df1cc7786' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c5f2836-fb72-4671-8555-e31df1cc7786' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10</li><li><span class='xr-has-index'>level</span>: 137</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6406049d-7491-4d8c-9ecf-4610480141da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6406049d-7491-4d8c-9ecf-4610480141da' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5e394cf0-ac79-429e-9042-08759f5e2f17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e394cf0-ac79-429e-9042-08759f5e2f17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfb8a6ac-fed6-4633-898b-5be2ace9d7d4' class='xr-var-data-in' type='checkbox'><label for='data-cfb8a6ac-fed6-4633-898b-5be2ace9d7d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 133 134 135 136 137</div><input id='attrs-3c11a5fe-48e5-4941-a589-90276fdedb41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c11a5fe-48e5-4941-a589-90276fdedb41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b7aa516-3779-4332-94d6-53dad1348ff7' class='xr-var-data-in' type='checkbox'><label for='data-9b7aa516-3779-4332-94d6-53dad1348ff7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n",
-       "        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,\n",
-       "        29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,\n",
-       "        43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,\n",
-       "        57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,\n",
-       "        71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,\n",
-       "        85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,\n",
-       "        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,\n",
-       "       113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,\n",
-       "       127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a95c2422-f0d9-4f72-a69e-16cae239b7d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a95c2422-f0d9-4f72-a69e-16cae239b7d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b715b39d-f15e-457c-a009-04b5a0c789db' class='xr-var-data-in' type='checkbox'><label for='data-b715b39d-f15e-457c-a009-04b5a0c789db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-26T12:00:00 ... 2020-07-31</div><input id='attrs-687092df-e04e-4f27-a6ce-5a44368fed88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-687092df-e04e-4f27-a6ce-5a44368fed88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf0be958-532d-4f58-8d0d-1a4a78fa8ed1' class='xr-var-data-in' type='checkbox'><label for='data-cf0be958-532d-4f58-8d0d-1a4a78fa8ed1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-26T12:00:00.000000000&#x27;, &#x27;2020-07-27T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-27T12:00:00.000000000&#x27;, &#x27;2020-07-28T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-28T12:00:00.000000000&#x27;, &#x27;2020-07-29T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-29T12:00:00.000000000&#x27;, &#x27;2020-07-30T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-30T12:00:00.000000000&#x27;, &#x27;2020-07-31T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d055675e-1231-40e8-9ac1-7b03c27b26bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d055675e-1231-40e8-9ac1-7b03c27b26bf' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3d83118-1b4b-4b61-b87f-cb18f6171aa5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3d83118-1b4b-4b61-b87f-cb18f6171aa5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ccffce3-cd0d-47f9-b4f8-6f0414f8ddc5' class='xr-var-data-in' type='checkbox'><label for='data-7ccffce3-cd0d-47f9-b4f8-6f0414f8ddc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>248</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Fraction of cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Fraction of cloud cover</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ciwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bbda85ea-5e28-41dd-93b5-6c12d7187074' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbda85ea-5e28-41dd-93b5-6c12d7187074' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd791ba0-94eb-4a10-bbc8-919505033502' class='xr-var-data-in' type='checkbox'><label for='data-cd791ba0-94eb-4a10-bbc8-919505033502' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>247</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ciwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific cloud ice water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ciwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 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956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific cloud ice water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1684b8ea-96dc-4868-8a18-2e934dd87ea0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1684b8ea-96dc-4868-8a18-2e934dd87ea0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa5daa39-ebed-4076-97fb-fafb082dd6d1' class='xr-var-data-in' type='checkbox'><label for='data-fa5daa39-ebed-4076-97fb-fafb082dd6d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>246</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>clwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific cloud liquid water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>clwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific cloud liquid water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ce2c19b-1b4d-4fa2-9f01-3ae40ee44448' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ce2c19b-1b4d-4fa2-9f01-3ae40ee44448' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff1ef3b2-b72a-46d6-b629-9c03a284e300' class='xr-var-data-in' type='checkbox'><label for='data-ff1ef3b2-b72a-46d6-b629-9c03a284e300' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>75</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>crwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific rain water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>crwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific rain water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cswc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84f0a623-95d9-406a-a806-bf3efdc401ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84f0a623-95d9-406a-a806-bf3efdc401ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3cfa228-0d1e-41af-8002-52c108669bf2' class='xr-var-data-in' type='checkbox'><label for='data-c3cfa228-0d1e-41af-8002-52c108669bf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>76</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cswc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific snow water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cswc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific snow water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-840a23fe-0b7a-4a38-936d-5982815672a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-840a23fe-0b7a-4a38-936d-5982815672a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f99438c7-8863-4013-a5cb-95de50489d08' class='xr-var-data-in' type='checkbox'><label for='data-f99438c7-8863-4013-a5cb-95de50489d08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>203</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>o3</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Ozone mass mixing ratio</dd><dt><span>GRIB_cfName :</span></dt><dd>mass_fraction_of_ozone_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>o3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Ozone mass mixing ratio</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e8088310-d08a-48d1-9530-ab041cef70c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8088310-d08a-48d1-9530-ab041cef70c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33cfea28-3b60-4e9b-8968-2c841290a39f' class='xr-var-data-in' type='checkbox'><label for='data-33cfea28-3b60-4e9b-8968-2c841290a39f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>133</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>q</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>specific_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>q</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-016040a9-d7c9-4e24-be0f-8da030f4f4b5' class='xr-section-summary-in' type='checkbox'  ><label for='section-016040a9-d7c9-4e24-be0f-8da030f4f4b5' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fab40ce2-ab97-4b26-b32c-3266a224825f' class='xr-index-data-in' type='checkbox'/><label for='index-fab40ce2-ab97-4b26-b32c-3266a224825f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
-       "       ...\n",
-       "       128, 129, 130, 131, 132, 133, 134, 135, 136, 137],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;, length=137))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f9e4ee9d-6b9c-452c-9ffb-25c2548dcbaa' class='xr-index-data-in' type='checkbox'/><label for='index-f9e4ee9d-6b9c-452c-9ffb-25c2548dcbaa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-26 12:00:00&#x27;, &#x27;2020-07-27 00:00:00&#x27;,\n",
-       "               &#x27;2020-07-27 12:00:00&#x27;, &#x27;2020-07-28 00:00:00&#x27;,\n",
-       "               &#x27;2020-07-28 12:00:00&#x27;, &#x27;2020-07-29 00:00:00&#x27;,\n",
-       "               &#x27;2020-07-29 12:00:00&#x27;, &#x27;2020-07-30 00:00:00&#x27;,\n",
-       "               &#x27;2020-07-30 12:00:00&#x27;, &#x27;2020-07-31 00:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2ccc49fd-ad02-47fe-9608-081bc2e5f82e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2ccc49fd-ad02-47fe-9608-081bc2e5f82e' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n",
-       "</dd></dl></div></li></ul></div></div>"
+      "id": "36e11d63-baec-4897-a5d1-26195cf5545f",
+      "cell_type": "code",
+      "source": "earthkit.plots.quickmap.plot(\n    ds_pl_O400[\"t\"].sel(level=1).sel(time=\"2020-07-31T00:00:00\"),\n    x=\"lon\", y=\"lat\",\n);",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 700x600 with 2 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 50GB\n",
-       "Dimensions:  (time: 10, level: 137, value: 654400)\n",
-       "Coordinates:\n",
-       "    lat      (value) float64 5MB ...\n",
-       "  * level    (level) int64 1kB 1 2 3 4 5 6 7 8 ... 131 132 133 134 135 136 137\n",
-       "    lon      (value) float64 5MB ...\n",
-       "  * time     (time) datetime64[ns] 80B 2020-07-26T12:00:00 ... 2020-07-31\n",
-       "Dimensions without coordinates: value\n",
-       "Data variables:\n",
-       "    cc       (time, level, value) float64 7GB ...\n",
-       "    ciwc     (time, level, value) float64 7GB ...\n",
-       "    clwc     (time, level, value) float64 7GB ...\n",
-       "    crwc     (time, level, value) float64 7GB ...\n",
-       "    cswc     (time, level, value) float64 7GB ...\n",
-       "    o3       (time, level, value) float64 7GB ...\n",
-       "    q        (time, level, value) float64 7GB ...\n",
-       "Attributes:\n",
-       "    GRIB_edition:            2\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc..."
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_ml = load_hplp_data(leveltype=\"ml\", gridtype=\"reduced_gg\", step=240)\n",
-    "ds_ml"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1343d519-ec42-4fd3-88c9-8b3fea2bfc38",
-   "metadata": {},
-   "source": [
-    "The data can be visualized as demonstrated below. For example, again, the specific humidity at **level 137** on **2020-07-31 at 00:00:00 UTC** is plotted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "0ad8e22f-66ea-4711-8607-e392815e4594",
-   "metadata": {},
-   "outputs": [
+      "execution_count": 8
+    },
     {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 700x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "earthkit.plots.quickmap.plot(\n",
-    "    ds_ml[\"q\"].sel(level=137).sel(time=\"2020-07-31T00:00:00\"),\n",
-    "    x=\"lon\", y=\"lat\",\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0320963f-0d01-4ee8-9cd4-d2aa765ebac6",
-   "metadata": {},
-   "source": [
-    "The surface level data is stored on a **reduced_gg** grid and can be loaded as in the following example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "989d5d36-6eb2-4bdc-804f-48cdc9deec9c",
-   "metadata": {},
-   "outputs": [
+      "id": "92cc1ccd-bb9b-48f2-81ab-9641c75d84ec",
+      "cell_type": "markdown",
+      "source": "The model level data stored on a **reduced_gg** grid is split into two files. Specifying a timestep of `240` ensures that the second part of the data is loaded, covering the time range from **2020-07-26 12:00:00 UTC** to **2020-07-31 00:00:00 UTC**.",
+      "metadata": {}
+    },
+    {
+      "id": "b287eccb-eb9b-47b1-95c2-7c66dc02b033",
+      "cell_type": "code",
+      "source": "ds_ml = load_hplp_data(leveltype=\"ml\", gridtype=\"reduced_gg\", step=240)\nds_ml",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_ml_reduced_gg_levels_132_240.grib\n",
+          "output_type": "stream"
+        },
+        {
+          "execution_count": 9,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "<xarray.Dataset> Size: 50GB\nDimensions:  (time: 10, level: 137, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 1kB 1 2 3 4 5 6 7 8 ... 131 132 133 134 135 136 137\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 80B 2020-07-26T12:00:00 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    cc       (time, level, value) float64 7GB ...\n    ciwc     (time, level, value) float64 7GB ...\n    clwc     (time, level, value) float64 7GB ...\n    crwc     (time, level, value) float64 7GB ...\n    cswc     (time, level, value) float64 7GB ...\n    o3       (time, level, value) float64 7GB ...\n    q        (time, level, value) float64 7GB ...\nAttributes:\n    GRIB_edition:            2\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...",
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 content: '►';\n  font-size: 11px;\n  width: 15px;\n  text-align: center;\n}\n\n.xr-section-summary-in:disabled + label:before {\n  color: var(--xr-disabled-color);\n}\n\n.xr-section-summary-in:checked + label:before {\n  content: '▼';\n}\n\n.xr-section-summary-in:checked + label > span {\n  display: none;\n}\n\n.xr-section-summary,\n.xr-section-inline-details {\n  padding-top: 4px;\n  padding-bottom: 4px;\n}\n\n.xr-section-inline-details {\n  grid-column: 2 / -1;\n}\n\n.xr-section-details {\n  display: none;\n  grid-column: 1 / -1;\n  margin-bottom: 5px;\n}\n\n.xr-section-summary-in:checked ~ .xr-section-details {\n  display: contents;\n}\n\n.xr-array-wrap {\n  grid-column: 1 / -1;\n  display: grid;\n  grid-template-columns: 20px auto;\n}\n\n.xr-array-wrap > label {\n  grid-column: 1;\n  vertical-align: top;\n}\n\n.xr-preview {\n  color: var(--xr-font-color3);\n}\n\n.xr-array-preview,\n.xr-array-data {\n  padding: 0 5px !important;\n  grid-column: 2;\n}\n\n.xr-array-data,\n.xr-array-in:checked ~ .xr-array-preview {\n  display: none;\n}\n\n.xr-array-in:checked ~ .xr-array-data,\n.xr-array-preview {\n  display: inline-block;\n}\n\n.xr-dim-list {\n  display: inline-block !important;\n  list-style: none;\n  padding: 0 !important;\n  margin: 0;\n}\n\n.xr-dim-list li {\n  display: inline-block;\n  padding: 0;\n  margin: 0;\n}\n\n.xr-dim-list:before {\n  content: '(';\n}\n\n.xr-dim-list:after {\n  content: ')';\n}\n\n.xr-dim-list li:not(:last-child):after {\n  content: ',';\n  padding-right: 5px;\n}\n\n.xr-has-index {\n  font-weight: bold;\n}\n\n.xr-var-list,\n.xr-var-item {\n  display: contents;\n}\n\n.xr-var-item > div,\n.xr-var-item label,\n.xr-var-item > .xr-var-name span {\n  background-color: var(--xr-background-color-row-even);\n  margin-bottom: 0;\n}\n\n.xr-var-item > .xr-var-name:hover span {\n  padding-right: 5px;\n}\n\n.xr-var-list > li:nth-child(odd) > div,\n.xr-var-list > li:nth-child(odd) > label,\n.xr-var-list > li:nth-child(odd) > .xr-var-name span {\n  background-color: var(--xr-background-color-row-odd);\n}\n\n.xr-var-name {\n  grid-column: 1;\n}\n\n.xr-var-dims {\n  grid-column: 2;\n}\n\n.xr-var-dtype {\n  grid-column: 3;\n  text-align: right;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-preview {\n  grid-column: 4;\n}\n\n.xr-index-preview {\n  grid-column: 2 / 5;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-name,\n.xr-var-dims,\n.xr-var-dtype,\n.xr-preview,\n.xr-attrs dt {\n  white-space: nowrap;\n  overflow: hidden;\n  text-overflow: ellipsis;\n  padding-right: 10px;\n}\n\n.xr-var-name:hover,\n.xr-var-dims:hover,\n.xr-var-dtype:hover,\n.xr-attrs dt:hover {\n  overflow: visible;\n  width: auto;\n  z-index: 1;\n}\n\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  display: none;\n  background-color: var(--xr-background-color) !important;\n  padding-bottom: 5px !important;\n}\n\n.xr-var-attrs-in:checked ~ .xr-var-attrs,\n.xr-var-data-in:checked ~ .xr-var-data,\n.xr-index-data-in:checked ~ .xr-index-data {\n  display: block;\n}\n\n.xr-var-data > table {\n  float: right;\n}\n\n.xr-var-name span,\n.xr-var-data,\n.xr-index-name div,\n.xr-index-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2,\n.xr-no-icon {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 50GB\nDimensions:  (time: 10, level: 137, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 1kB 1 2 3 4 5 6 7 8 ... 131 132 133 134 135 136 137\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 80B 2020-07-26T12:00:00 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    cc       (time, level, value) float64 7GB ...\n    ciwc     (time, level, value) float64 7GB ...\n    clwc     (time, level, value) float64 7GB ...\n    crwc     (time, level, value) float64 7GB ...\n    cswc     (time, level, value) float64 7GB ...\n    o3       (time, level, value) float64 7GB ...\n    q        (time, level, value) float64 7GB ...\nAttributes:\n    GRIB_edition:            2\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8a3a7295-c7aa-4117-89c2-c40541edb524' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8a3a7295-c7aa-4117-89c2-c40541edb524' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10</li><li><span class='xr-has-index'>level</span>: 137</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-484a3f40-c9ec-4019-b8ec-929def15123f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-484a3f40-c9ec-4019-b8ec-929def15123f' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf49fd14-64eb-4a9e-8576-1c8d5eb3a288' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf49fd14-64eb-4a9e-8576-1c8d5eb3a288' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3112f5e8-aa9b-4a4e-9ce5-844d915539bf' class='xr-var-data-in' type='checkbox'><label for='data-3112f5e8-aa9b-4a4e-9ce5-844d915539bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 133 134 135 136 137</div><input id='attrs-3cccdf5a-9787-4708-aff5-7508d46f01b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3cccdf5a-9787-4708-aff5-7508d46f01b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbce50f4-03e1-4f26-88c7-c710559a4797' class='xr-var-data-in' type='checkbox'><label for='data-bbce50f4-03e1-4f26-88c7-c710559a4797' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,\n        29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,\n        43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,\n        57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,\n        71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,\n        85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,\n        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,\n       113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,\n       127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed488c65-3609-41a5-9ee4-60945620cc42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed488c65-3609-41a5-9ee4-60945620cc42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89a47b2f-b5cb-4857-8e70-aba07a0744fe' class='xr-var-data-in' type='checkbox'><label for='data-89a47b2f-b5cb-4857-8e70-aba07a0744fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-26T12:00:00 ... 2020-07-31</div><input id='attrs-6602d500-33cc-4d46-ad97-5535ccb76107' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6602d500-33cc-4d46-ad97-5535ccb76107' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d177e2b-b059-49b7-93dd-b1406060a4b1' class='xr-var-data-in' type='checkbox'><label for='data-5d177e2b-b059-49b7-93dd-b1406060a4b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-26T12:00:00.000000000&#x27;, &#x27;2020-07-27T00:00:00.000000000&#x27;,\n       &#x27;2020-07-27T12:00:00.000000000&#x27;, &#x27;2020-07-28T00:00:00.000000000&#x27;,\n       &#x27;2020-07-28T12:00:00.000000000&#x27;, &#x27;2020-07-29T00:00:00.000000000&#x27;,\n       &#x27;2020-07-29T12:00:00.000000000&#x27;, &#x27;2020-07-30T00:00:00.000000000&#x27;,\n       &#x27;2020-07-30T12:00:00.000000000&#x27;, &#x27;2020-07-31T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc637be7-e346-49d8-9df8-2438237ed460' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc637be7-e346-49d8-9df8-2438237ed460' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5ee603c-36e5-4a9b-aaf8-583f520a87f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5ee603c-36e5-4a9b-aaf8-583f520a87f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fe67be5-263c-431c-9d0d-9d371135171f' class='xr-var-data-in' type='checkbox'><label for='data-7fe67be5-263c-431c-9d0d-9d371135171f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>248</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Fraction of cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Fraction of cloud cover</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ciwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f72074a6-624b-49cc-b087-22fb23723176' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f72074a6-624b-49cc-b087-22fb23723176' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d63e1b72-20be-435f-b7d8-5700ba0f95d1' class='xr-var-data-in' type='checkbox'><label for='data-d63e1b72-20be-435f-b7d8-5700ba0f95d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>247</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ciwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific cloud ice water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ciwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 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1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific cloud ice water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d561f8a4-0109-47e1-babb-61ea8ab71ba9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d561f8a4-0109-47e1-babb-61ea8ab71ba9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-685d23d8-44c8-4699-8c05-0459cdcfcd61' class='xr-var-data-in' type='checkbox'><label for='data-685d23d8-44c8-4699-8c05-0459cdcfcd61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>246</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>clwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific cloud liquid water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>clwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific cloud liquid water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-109c0950-6b98-4802-be8c-398c2809e3e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-109c0950-6b98-4802-be8c-398c2809e3e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-206a2e2a-9624-4271-a438-c7f1f8a752d3' class='xr-var-data-in' type='checkbox'><label for='data-206a2e2a-9624-4271-a438-c7f1f8a752d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>75</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>crwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific rain water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>crwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific rain water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cswc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ea208c8f-fc75-4b07-ac70-85661659267f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea208c8f-fc75-4b07-ac70-85661659267f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37c7d949-e1c7-4ef2-b25f-a434751fcbad' class='xr-var-data-in' type='checkbox'><label for='data-37c7d949-e1c7-4ef2-b25f-a434751fcbad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>76</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cswc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific snow water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cswc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific snow water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-05232d2a-7bc8-4544-8e86-a9aa497c9a38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05232d2a-7bc8-4544-8e86-a9aa497c9a38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d12d5490-4941-4fb4-b43b-87ca3dd8d4cc' class='xr-var-data-in' type='checkbox'><label for='data-d12d5490-4941-4fb4-b43b-87ca3dd8d4cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>203</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>o3</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Ozone mass mixing ratio</dd><dt><span>GRIB_cfName :</span></dt><dd>mass_fraction_of_ozone_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>o3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Ozone mass mixing ratio</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-017c8ef2-2f45-4dfb-985f-361e88e911bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-017c8ef2-2f45-4dfb-985f-361e88e911bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fafad87-73db-4632-911e-dc22098f97ca' class='xr-var-data-in' type='checkbox'><label for='data-4fafad87-73db-4632-911e-dc22098f97ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>133</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>q</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>specific_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>q</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3e8569e-5240-4c81-8a99-06194a8681c9' class='xr-section-summary-in' type='checkbox'  ><label for='section-e3e8569e-5240-4c81-8a99-06194a8681c9' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0ff12cfc-6cbd-4de9-9f73-4f1c9f7d0cb3' class='xr-index-data-in' type='checkbox'/><label for='index-0ff12cfc-6cbd-4de9-9f73-4f1c9f7d0cb3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n       ...\n       128, 129, 130, 131, 132, 133, 134, 135, 136, 137],\n      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;, length=137))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-51b6774b-5689-44d5-9caf-c0678274d5fc' class='xr-index-data-in' type='checkbox'/><label for='index-51b6774b-5689-44d5-9caf-c0678274d5fc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-26 12:00:00&#x27;, &#x27;2020-07-27 00:00:00&#x27;,\n               &#x27;2020-07-27 12:00:00&#x27;, &#x27;2020-07-28 00:00:00&#x27;,\n               &#x27;2020-07-28 12:00:00&#x27;, &#x27;2020-07-29 00:00:00&#x27;,\n               &#x27;2020-07-29 12:00:00&#x27;, &#x27;2020-07-30 00:00:00&#x27;,\n               &#x27;2020-07-30 12:00:00&#x27;, &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d3d30fa-5bd8-4f54-8f22-3c70fefb7b0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d3d30fa-5bd8-4f54-8f22-3c70fefb7b0c' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 9
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_sfc.grib\n"
-     ]
+      "id": "1343d519-ec42-4fd3-88c9-8b3fea2bfc38",
+      "cell_type": "markdown",
+      "source": "The data can be visualized as demonstrated below. For example, again, the specific humidity at **level 137** on **2020-07-31 at 00:00:00 UTC** is plotted.",
+      "metadata": {}
     },
     {
-     "data": {
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-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
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-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "html[data-theme=dark],\n",
-       "body[data-theme=dark],\n",
-       "body.vscode-dark {\n",
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-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
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-       "\n",
-       ".xr-wrap {\n",
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-       "  min-width: 300px;\n",
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-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
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-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
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-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
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-       "\n",
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-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
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-       "\n",
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-       "\n",
-       ".xr-sections {\n",
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-       "\n",
-       ".xr-section-summary > span {\n",
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-       "  padding-left: 0.5em;\n",
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-       "\n",
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-       "\n",
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-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
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-       "\n",
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-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
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-       "\n",
-       ".xr-section-summary,\n",
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-       "  padding-bottom: 4px;\n",
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-       "\n",
-       ".xr-section-details {\n",
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-       "\n",
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-       "\n",
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-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
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-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
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-       "\n",
-       ".xr-array-data,\n",
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-       "  display: none;\n",
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-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
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-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
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-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
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-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
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-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
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-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
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-       "  padding-right: 5px;\n",
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-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
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-       ".xr-var-item > .xr-var-name span {\n",
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-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
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-       "\n",
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-       "\n",
-       ".xr-var-dims {\n",
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-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
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-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
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-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
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-       "  width: auto;\n",
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-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
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-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
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-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 11GB\n",
-       "Dimensions:  (time: 21, value: 654400)\n",
-       "Coordinates:\n",
-       "    lat      (value) float64 5MB ...\n",
-       "    lon      (value) float64 5MB ...\n",
-       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
-       "Dimensions without coordinates: value\n",
-       "Data variables: (12/101)\n",
-       "    10fg     (time, value) float64 110MB ...\n",
-       "    10u      (time, value) float64 110MB ...\n",
-       "    10v      (time, value) float64 110MB ...\n",
-       "    2d       (time, value) float64 110MB ...\n",
-       "    2t       (time, value) float64 110MB ...\n",
-       "    asn      (time, value) float64 110MB ...\n",
-       "    ...       ...\n",
-       "    ttr      (time, value) float64 110MB ...\n",
-       "    ttrc     (time, value) float64 110MB ...\n",
-       "    uvb      (time, value) float64 110MB ...\n",
-       "    vimd     (time, value) float64 110MB ...\n",
-       "    vis      (time, value) float64 110MB ...\n",
-       "    z        (time, value) float64 110MB ...\n",
-       "Attributes:\n",
-       "    GRIB_edition:            2\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d6e20773-0fbd-44d8-827c-0bb005c569b7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d6e20773-0fbd-44d8-827c-0bb005c569b7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1030ebe0-064e-4e70-a328-572a1e9948c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1030ebe0-064e-4e70-a328-572a1e9948c8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aae59fd7-117a-49ad-a9c8-93e13362cf4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aae59fd7-117a-49ad-a9c8-93e13362cf4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef154508-65a5-4f61-a6ac-2957b0e5490a' class='xr-var-data-in' type='checkbox'><label for='data-ef154508-65a5-4f61-a6ac-2957b0e5490a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf7b3ec2-5e8a-4576-9ec3-ae77e26d5b02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf7b3ec2-5e8a-4576-9ec3-ae77e26d5b02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-041984fa-47b9-4173-b5c0-12b64d089ae1' class='xr-var-data-in' type='checkbox'><label for='data-041984fa-47b9-4173-b5c0-12b64d089ae1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-95740a49-11e0-4b7e-adbd-337c03a5b8b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95740a49-11e0-4b7e-adbd-337c03a5b8b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82dbf6a3-d086-4d56-b9e4-06d3f3a48e49' class='xr-var-data-in' type='checkbox'><label for='data-82dbf6a3-d086-4d56-b9e4-06d3f3a48e49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf47ba93-7905-4a9e-8f3c-681e99ff936c' class='xr-section-summary-in' type='checkbox'  ><label for='section-cf47ba93-7905-4a9e-8f3c-681e99ff936c' class='xr-section-summary' >Data variables: <span>(101)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10fg</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4330fec5-039a-49c9-9ab5-63c7c979cd2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4330fec5-039a-49c9-9ab5-63c7c979cd2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-743351a8-3723-49a9-93e2-483a92d31547' class='xr-var-data-in' type='checkbox'><label for='data-743351a8-3723-49a9-93e2-483a92d31547' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>49</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10fg</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Maximum 10 metre wind gust since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fg10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Maximum 10 metre wind gust since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8ba373db-0f35-4c33-bdbe-8b591dcf6226' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ba373db-0f35-4c33-bdbe-8b591dcf6226' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68dab872-2b07-4a54-899c-f69318b7e38d' class='xr-var-data-in' type='checkbox'><label for='data-68dab872-2b07-4a54-899c-f69318b7e38d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>165</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10u</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre U wind component</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f17e00b-a8be-42c7-a74c-1a8de0e39fb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f17e00b-a8be-42c7-a74c-1a8de0e39fb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a3327d3-9045-4f43-84fc-0bff4d095609' class='xr-var-data-in' type='checkbox'><label for='data-6a3327d3-9045-4f43-84fc-0bff4d095609' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>166</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10v</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre V wind component</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a7973a04-4618-4024-9adf-812147ece4ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7973a04-4618-4024-9adf-812147ece4ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6538ad77-929e-4f80-90c0-99f419424f60' class='xr-var-data-in' type='checkbox'><label for='data-6538ad77-929e-4f80-90c0-99f419424f60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-21210fd6-e469-4adf-b6b0-2c8925433b86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21210fd6-e469-4adf-b6b0-2c8925433b86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e20733-d545-404c-a1d0-18921f9e995a' class='xr-var-data-in' type='checkbox'><label for='data-f3e20733-d545-404c-a1d0-18921f9e995a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>asn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5580b684-15ea-4412-83a3-d21edc956927' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5580b684-15ea-4412-83a3-d21edc956927' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00b1a9b8-c259-4d26-a93b-27cd46d51660' class='xr-var-data-in' type='checkbox'><label for='data-00b1a9b8-c259-4d26-a93b-27cd46d51660' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>32</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>asn</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Snow albedo</dd><dt><span>GRIB_cfVarName :</span></dt><dd>asn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Snow albedo</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bld</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e60e67c0-6694-4313-8259-b48dcfefe609' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e60e67c0-6694-4313-8259-b48dcfefe609' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92ff03ea-9476-413e-b2fc-fde42c102caf' class='xr-var-data-in' type='checkbox'><label for='data-92ff03ea-9476-413e-b2fc-fde42c102caf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>145</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>bld</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated boundary layer dissipation</dd><dt><span>GRIB_cfName :</span></dt><dd>kinetic_energy_dissipation_in_atmosphere_boundary_layer</dd><dt><span>GRIB_cfVarName :</span></dt><dd>bld</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated boundary layer dissipation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blh</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d1889f68-5666-4825-98a8-bdd54f52968e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1889f68-5666-4825-98a8-bdd54f52968e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c103f950-3607-44a5-b61f-b980a0ed83fc' class='xr-var-data-in' type='checkbox'><label for='data-c103f950-3607-44a5-b61f-b980a0ed83fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>159</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>blh</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Boundary layer height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>blh</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Boundary layer height</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cape</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5187718-a1cf-4be6-84cb-605911632026' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5187718-a1cf-4be6-84cb-605911632026' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bddae4a7-3576-4a35-aaf5-b02efa9da2ff' class='xr-var-data-in' type='checkbox'><label for='data-bddae4a7-3576-4a35-aaf5-b02efa9da2ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>59</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cape</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective available potential energy</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cape</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective available potential energy</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chnk</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b8e21707-ea42-48b2-8cf1-217543d78194' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8e21707-ea42-48b2-8cf1-217543d78194' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5892a35e-f0e7-45b5-bb82-9450662cc9c2' class='xr-var-data-in' type='checkbox'><label for='data-5892a35e-f0e7-45b5-bb82-9450662cc9c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>148</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>chnk</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Charnock</dd><dt><span>GRIB_cfVarName :</span></dt><dd>chnk</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Charnock</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ci</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cc05fd9b-7706-4c46-803f-e2dbbb34e3e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc05fd9b-7706-4c46-803f-e2dbbb34e3e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87461e7a-35be-449c-ac8f-748514051088' class='xr-var-data-in' type='checkbox'><label for='data-87461e7a-35be-449c-ac8f-748514051088' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>31</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ci</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>GRIB_cfName :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>siconc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cin</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7912ac99-2f5e-4878-bc76-bd6f567e3cd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7912ac99-2f5e-4878-bc76-bd6f567e3cd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2a39c65-5841-4202-8e6d-c478a3adc277' class='xr-var-data-in' type='checkbox'><label for='data-a2a39c65-5841-4202-8e6d-c478a3adc277' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228001</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cin</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective inhibition</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cin</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective inhibition</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-85a8e3af-e2a8-4637-8379-ecc4b732f606' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85a8e3af-e2a8-4637-8379-ecc4b732f606' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53848e1c-2276-4306-9ed2-98887110da2d' class='xr-var-data-in' type='checkbox'><label for='data-53848e1c-2276-4306-9ed2-98887110da2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>143</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Convective precipitation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Convective precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0dface96-9d7a-4361-b62d-4660d84f7893' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dface96-9d7a-4361-b62d-4660d84f7893' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c55d404-5586-4c01-aee5-f7bd5b408daf' class='xr-var-data-in' type='checkbox'><label for='data-7c55d404-5586-4c01-aee5-f7bd5b408daf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228218</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>crr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective rain rate</dd><dt><span>GRIB_cfVarName :</span></dt><dd>crr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 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1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Convective rain rate</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>csfr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-38b23da5-6b3a-4fac-9c6f-04071f910631' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38b23da5-6b3a-4fac-9c6f-04071f910631' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba7c7c02-9603-40f3-9556-ba88bd514ba3' class='xr-var-data-in' type='checkbox'><label for='data-ba7c7c02-9603-40f3-9556-ba88bd514ba3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228220</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>csfr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective snowfall rate water equivalent</dd><dt><span>GRIB_cfVarName :</span></dt><dd>csfr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Convective snowfall rate water equivalent</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dsrp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b3473e5b-566d-463e-8f83-b5c780f88f28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3473e5b-566d-463e-8f83-b5c780f88f28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85f4fe30-a443-4aa1-9e35-30cd72440df5' class='xr-var-data-in' type='checkbox'><label for='data-85f4fe30-a443-4aa1-9e35-30cd72440df5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>47</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>dsrp</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface direct normal short-wave (solar) radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dsrp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface direct normal short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5bf9b557-7b28-40b1-8b21-5ebc0d7cae12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bf9b557-7b28-40b1-8b21-5ebc0d7cae12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43ee563b-2355-402c-b418-246f90796540' class='xr-var-data-in' type='checkbox'><label for='data-43ee563b-2355-402c-b418-246f90796540' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>182</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>e</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Evaporation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_water_evaporation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>e</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b3fbf1fd-a9d9-45c8-be51-5a9ac089cc3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3fbf1fd-a9d9-45c8-be51-5a9ac089cc3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88b0f3ca-89a5-4fc5-b7c7-5b2b6afbf7b7' class='xr-var-data-in' type='checkbox'><label for='data-88b0f3ca-89a5-4fc5-b7c7-5b2b6afbf7b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>44</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>es</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow evaporation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>es</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snow evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d146fdf8-4b7d-4cf6-b8af-0e3d182ca71c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d146fdf8-4b7d-4cf6-b8af-0e3d182ca71c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1937e3c4-d6b2-401b-b467-38d351ff3348' class='xr-var-data-in' type='checkbox'><label for='data-1937e3c4-d6b2-401b-b467-38d351ff3348' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>180</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ewss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated eastward turbulent surface stress</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downward_eastward_stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ewss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Time-integrated eastward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fal</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ffa74af9-cddd-4a8e-8d19-1c7200ddb745' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffa74af9-cddd-4a8e-8d19-1c7200ddb745' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc964044-8090-4c91-bc40-32558c310907' class='xr-var-data-in' type='checkbox'><label for='data-bc964044-8090-4c91-bc40-32558c310907' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>243</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fal</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Forecast albedo</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fal</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Forecast albedo</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0734c2e2-aa5a-4ecf-ac9a-b738df83f59e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0734c2e2-aa5a-4ecf-ac9a-b738df83f59e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94edd35e-2143-4792-ab8c-aed7cb858ae9' class='xr-var-data-in' type='checkbox'><label for='data-94edd35e-2143-4792-ab8c-aed7cb858ae9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>245</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>flsr</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Forecast logarithm of surface roughness for heat</dd><dt><span>GRIB_cfVarName :</span></dt><dd>flsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Forecast logarithm of surface roughness for heat</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d76e9437-06dc-4d0d-83bd-6f9261abc220' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d76e9437-06dc-4d0d-83bd-6f9261abc220' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe5f6e7b-276a-47ce-94ed-537254bd7811' class='xr-var-data-in' type='checkbox'><label for='data-fe5f6e7b-276a-47ce-94ed-537254bd7811' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>244</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fsr</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Forecast surface roughness</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Forecast surface roughness</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fzra</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dc51dff6-933b-48d6-b4b7-02d8e9dc65a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc51dff6-933b-48d6-b4b7-02d8e9dc65a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8040c01d-6ee2-453b-b6ac-0911caba14f3' class='xr-var-data-in' type='checkbox'><label for='data-8040c01d-6ee2-453b-b6ac-0911caba14f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228216</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fzra</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Accumulated freezing rain</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fzra</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Accumulated freezing rain</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gwd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e247a28d-e39e-44fb-b461-4e2dcd1d32b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e247a28d-e39e-44fb-b461-4e2dcd1d32b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eb0518c-4af1-4ace-bbb2-25ba3cf62c57' class='xr-var-data-in' type='checkbox'><label for='data-3eb0518c-4af1-4ace-bbb2-25ba3cf62c57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>197</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>gwd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Gravity wave dissipation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>gwd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Gravity wave dissipation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e48a4375-1a79-44cc-af3d-9f893f45bd64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e48a4375-1a79-44cc-af3d-9f893f45bd64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3955749f-9928-40fd-8b1a-b0eca96eddc5' class='xr-var-data-in' type='checkbox'><label for='data-3955749f-9928-40fd-8b1a-b0eca96eddc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>188</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>hcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>High cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>High cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>i10fg</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71f103af-ed59-4e51-b665-efb8b8700007' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71f103af-ed59-4e51-b665-efb8b8700007' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a432187-56b6-4594-b1ba-7981e6f7800c' class='xr-var-data-in' type='checkbox'><label for='data-8a432187-56b6-4594-b1ba-7981e6f7800c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228029</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>i10fg</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous 10 metre wind gust</dd><dt><span>GRIB_cfVarName :</span></dt><dd>i10fg</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Instantaneous 10 metre wind gust</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ie</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ea6be8e6-c414-4d26-9eed-a7b19ab60fba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea6be8e6-c414-4d26-9eed-a7b19ab60fba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3a1adc6-6f06-47d1-8b61-b5709c93a230' class='xr-var-data-in' type='checkbox'><label for='data-c3a1adc6-6f06-47d1-8b61-b5709c93a230' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>232</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ie</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous moisture flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ie</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Instantaneous moisture flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iews</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8545f0ca-8158-4424-b926-fbccf7257257' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8545f0ca-8158-4424-b926-fbccf7257257' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97902f2b-504e-467a-90bb-c0253770f3aa' class='xr-var-data-in' type='checkbox'><label for='data-97902f2b-504e-467a-90bb-c0253770f3aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>229</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>iews</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous eastward turbulent surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>iews</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous eastward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ilspf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-033f75f8-02ff-46e9-8ba1-6d78193aef72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-033f75f8-02ff-46e9-8ba1-6d78193aef72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cdd3ad4-8566-41ec-be5f-bacacfbe5144' class='xr-var-data-in' type='checkbox'><label for='data-6cdd3ad4-8566-41ec-be5f-bacacfbe5144' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228217</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ilspf</dd><dt><span>GRIB_units :</span></dt><dd>Proportion</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous large-scale precipitation fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ilspf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Proportion</dd><dt><span>long_name :</span></dt><dd>Instantaneous large-scale precipitation fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45a10835-9d31-49e0-b8a6-a622fee660cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45a10835-9d31-49e0-b8a6-a622fee660cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9f23c2d-f5b0-452d-89af-cf39e6c87315' class='xr-var-data-in' type='checkbox'><label for='data-d9f23c2d-f5b0-452d-89af-cf39e6c87315' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>230</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>inss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous northward turbulent surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>inss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous northward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ishf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f0773b4f-2753-4910-9697-5985da9ca053' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0773b4f-2753-4910-9697-5985da9ca053' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-123658a1-c4dd-4557-abbb-e16a6e880ac1' class='xr-var-data-in' type='checkbox'><label for='data-123658a1-c4dd-4557-abbb-e16a6e880ac1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>231</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ishf</dd><dt><span>GRIB_units :</span></dt><dd>W m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous surface sensible heat net flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ishf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>W m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous surface sensible heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-34e5e4dc-700d-4d77-b0f9-b5bd21dbcea8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34e5e4dc-700d-4d77-b0f9-b5bd21dbcea8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7015e57-24d5-4297-ae66-45159a40229a' class='xr-var-data-in' type='checkbox'><label for='data-f7015e57-24d5-4297-ae66-45159a40229a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>35</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl1</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 1</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d548bd7e-3c9a-4957-aa75-7c15c37fa9c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d548bd7e-3c9a-4957-aa75-7c15c37fa9c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c7a0ead-71f0-42e9-ad0a-db15e8efd596' class='xr-var-data-in' type='checkbox'><label for='data-6c7a0ead-71f0-42e9-ad0a-db15e8efd596' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>36</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl2</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-62603528-c6c8-43d4-b9e4-eae9f4bfda5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62603528-c6c8-43d4-b9e4-eae9f4bfda5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2326bf87-bd6d-4358-a272-c185155787b7' class='xr-var-data-in' type='checkbox'><label for='data-2326bf87-bd6d-4358-a272-c185155787b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>37</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl3</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0cb5fe80-76c6-4917-ac70-0d20c446728b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cb5fe80-76c6-4917-ac70-0d20c446728b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-047bc4d9-47e1-41b8-9b65-34560d7f12e8' class='xr-var-data-in' type='checkbox'><label for='data-047bc4d9-47e1-41b8-9b65-34560d7f12e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>38</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl4</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kx</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df63305e-268e-43f3-a933-ecce12c47d74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df63305e-268e-43f3-a933-ecce12c47d74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36d04360-7e0d-4cf6-893c-4c1ccc42b751' class='xr-var-data-in' type='checkbox'><label for='data-36d04360-7e0d-4cf6-893c-4c1ccc42b751' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260121</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>kx</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>K index</dd><dt><span>GRIB_cfVarName :</span></dt><dd>kx</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>K index</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-06171991-86f9-4328-a481-a4eb9be73352' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06171991-86f9-4328-a481-a4eb9be73352' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca9e8885-a25c-494d-b809-c8f17873a708' class='xr-var-data-in' type='checkbox'><label for='data-ca9e8885-a25c-494d-b809-c8f17873a708' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>186</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Low cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Low cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lgws</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-360cda36-93ff-43ff-978a-29c1bcd21066' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-360cda36-93ff-43ff-978a-29c1bcd21066' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e40087d6-fdec-4159-840b-e19e44293d84' class='xr-var-data-in' type='checkbox'><label for='data-e40087d6-fdec-4159-840b-e19e44293d84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>195</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lgws</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Eastward gravity wave surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lgws</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 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1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 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832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Eastward gravity wave surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsm</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0cf13f02-46f9-4260-944f-8b0cb49506f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cf13f02-46f9-4260-944f-8b0cb49506f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-860dd268-856d-4a2d-803a-bfa5d5f9192d' class='xr-var-data-in' type='checkbox'><label for='data-860dd268-856d-4a2d-803a-bfa5d5f9192d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>172</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsm</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Land-sea mask</dd><dt><span>GRIB_cfName :</span></dt><dd>land_binary_mask</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsm</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Land-sea mask</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8939a644-cfce-4cf7-8b39-32f6528eefac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8939a644-cfce-4cf7-8b39-32f6528eefac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-547fcf75-42e9-44f4-9971-052e6dbc3b1d' class='xr-var-data-in' type='checkbox'><label for='data-547fcf75-42e9-44f4-9971-052e6dbc3b1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>142</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Large-scale precipitation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_stratiform_precipitation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Large-scale precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lspf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d794c20d-7296-49a6-b493-0dbae2cf4c43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d794c20d-7296-49a6-b493-0dbae2cf4c43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6556e85a-fab4-4c12-a32f-8748bcfa78f3' class='xr-var-data-in' type='checkbox'><label for='data-6556e85a-fab4-4c12-a32f-8748bcfa78f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>50</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lspf</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Large-scale precipitation fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lspf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Large-scale precipitation fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsrr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4a3d8e58-70ab-4628-be6c-17356a808fb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a3d8e58-70ab-4628-be6c-17356a808fb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0efd73a-2178-497b-943a-999923ac056c' class='xr-var-data-in' type='checkbox'><label for='data-d0efd73a-2178-497b-943a-999923ac056c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228219</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsrr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Large scale rain rate</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsrr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Large scale rain rate</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lssfr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-391fb502-0585-4be3-bd04-4d3f274a1265' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-391fb502-0585-4be3-bd04-4d3f274a1265' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01b7ca5e-4205-412d-be1f-e89787f9890d' class='xr-var-data-in' type='checkbox'><label for='data-01b7ca5e-4205-412d-be1f-e89787f9890d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228221</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lssfr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Large scale snowfall rate water equivalent</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lssfr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Large scale snowfall rate water equivalent</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca8de65f-977a-4a13-aaac-d863abbfc463' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca8de65f-977a-4a13-aaac-d863abbfc463' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-286038eb-10f4-4cc9-a006-2a038b3dd5c2' class='xr-var-data-in' type='checkbox'><label for='data-286038eb-10f4-4cc9-a006-2a038b3dd5c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>187</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Medium cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Medium cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mgws</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3babd423-6077-43b8-844c-84d641b4a2d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3babd423-6077-43b8-844c-84d641b4a2d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ce0da29-c69e-4eb4-9396-bd0927db6b22' class='xr-var-data-in' type='checkbox'><label for='data-8ce0da29-c69e-4eb4-9396-bd0927db6b22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>196</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mgws</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Northward gravity wave surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mgws</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Northward gravity wave surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mn2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32cb8fbb-a6e2-4517-ba25-751911cb5824' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32cb8fbb-a6e2-4517-ba25-751911cb5824' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5bce66f-43eb-4dfd-96d4-52b4b04b863d' class='xr-var-data-in' type='checkbox'><label for='data-e5bce66f-43eb-4dfd-96d4-52b4b04b863d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>202</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mn2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mn2t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f48502e5-2432-4ca5-958a-6e805ef038d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f48502e5-2432-4ca5-958a-6e805ef038d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e49e8cd-4f2f-4914-bb85-98a03332648a' class='xr-var-data-in' type='checkbox'><label for='data-6e49e8cd-4f2f-4914-bb85-98a03332648a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>msl</dd><dt><span>GRIB_units :</span></dt><dd>Pa</dd><dt><span>GRIB_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>GRIB_cfName :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>GRIB_cfVarName :</span></dt><dd>msl</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>Mean sea level pressure</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mx2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ceb2d97f-9912-4bdc-a907-e3a981313aaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ceb2d97f-9912-4bdc-a907-e3a981313aaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-707f55c2-8a18-4dca-a1a4-b52174eee2fa' class='xr-var-data-in' type='checkbox'><label for='data-707f55c2-8a18-4dca-a1a4-b52174eee2fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>201</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mx2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mx2t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b957b58b-ae9d-4dd2-ac26-6c8dbc37b0e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b957b58b-ae9d-4dd2-ac26-6c8dbc37b0e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6775af3a-07ba-4916-9797-fa25f75a77fb' class='xr-var-data-in' type='checkbox'><label for='data-6775af3a-07ba-4916-9797-fa25f75a77fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>181</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>nsss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated northward turbulent surface stress</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downward_northward_stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>nsss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Time-integrated northward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocu</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-add3e0be-a7bd-4057-9c8c-5b758a92b9b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-add3e0be-a7bd-4057-9c8c-5b758a92b9b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8fbb419-5c00-4947-bbb0-ce9930cd0265' class='xr-var-data-in' type='checkbox'><label for='data-c8fbb419-5c00-4947-bbb0-ce9930cd0265' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151131</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ocu</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Eastward surface sea water velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>uoe</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Eastward surface sea water velocity</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocv</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45f410ba-d703-4df4-baa6-2b74e36eefc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45f410ba-d703-4df4-baa6-2b74e36eefc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5f2b829-12d6-485f-931b-c16524e4fefe' class='xr-var-data-in' type='checkbox'><label for='data-c5f2b829-12d6-485f-931b-c16524e4fefe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151132</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ocv</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Northward surface sea water velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>von</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Northward surface sea water velocity</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>par</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a6072bf6-5bdd-4a92-a5c3-dc8748356560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6072bf6-5bdd-4a92-a5c3-dc8748356560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34cd21a5-f6c5-4372-b605-bbf4e46a096a' class='xr-var-data-in' type='checkbox'><label for='data-34cd21a5-f6c5-4372-b605-bbf4e46a096a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>58</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>par</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Photosynthetically active radiation at the surface</dd><dt><span>GRIB_cfVarName :</span></dt><dd>par</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Photosynthetically active radiation at the surface</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pev</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cbd8ec4a-5e25-490a-b4d6-ced99b5da25e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbd8ec4a-5e25-490a-b4d6-ced99b5da25e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cccf4c5-4b95-434f-b734-a08d919a2f22' class='xr-var-data-in' type='checkbox'><label for='data-5cccf4c5-4b95-434f-b734-a08d919a2f22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228251</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>pev</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Potential evaporation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>pev</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Potential evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ptype</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3a7109bc-b747-481a-b7a0-3509e4abf34c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a7109bc-b747-481a-b7a0-3509e4abf34c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f4a61c0-f2bc-4487-86ea-d4f375577e74' class='xr-var-data-in' type='checkbox'><label for='data-0f4a61c0-f2bc-4487-86ea-d4f375577e74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260015</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ptype</dd><dt><span>GRIB_units :</span></dt><dd>(Code table 4.201)</dd><dt><span>GRIB_name :</span></dt><dd>Precipitation type</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ptype</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(Code table 4.201)</dd><dt><span>long_name :</span></dt><dd>Precipitation type</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a1db728f-9603-4862-9e0f-1d9b0e2e52d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1db728f-9603-4862-9e0f-1d9b0e2e52d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30f245bc-2445-4966-935d-94cc7b3c14f4' class='xr-var-data-in' type='checkbox'><label for='data-30f245bc-2445-4966-935d-94cc7b3c14f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>205</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-64083807-54fe-48f4-969a-d35da34bd114' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64083807-54fe-48f4-969a-d35da34bd114' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be432ccc-0bc6-4140-90a7-1b663c0cd0cc' class='xr-var-data-in' type='checkbox'><label for='data-be432ccc-0bc6-4140-90a7-1b663c0cd0cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>33</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>rsn</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Snow density</dd><dt><span>GRIB_cfVarName :</span></dt><dd>rsn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-3</dd><dt><span>long_name :</span></dt><dd>Snow density</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5adf497f-1771-4942-9057-c8f967715a67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5adf497f-1771-4942-9057-c8f967715a67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb8f85cf-4892-471f-88f3-40f5d50f1c9a' class='xr-var-data-in' type='checkbox'><label for='data-fb8f85cf-4892-471f-88f3-40f5d50f1c9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>141</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sd</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow depth</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snow depth</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-95450a9a-e9aa-499e-8a57-32e094827532' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95450a9a-e9aa-499e-8a57-32e094827532' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49ee2ac1-05ef-4002-8859-dd790c324333' class='xr-var-data-in' type='checkbox'><label for='data-49ee2ac1-05ef-4002-8859-dd790c324333' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>144</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sf</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snowfall</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snowfall</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6d09aa6c-72f6-4b88-a8c7-6a9e2d0d3a47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d09aa6c-72f6-4b88-a8c7-6a9e2d0d3a47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47782e4e-76e8-4c9d-b393-1b49caeb490d' class='xr-var-data-in' type='checkbox'><label for='data-47782e4e-76e8-4c9d-b393-1b49caeb490d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>235</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>skt</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Skin temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>skt</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Skin temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-38796003-83a3-4c28-be13-b6069acc0350' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38796003-83a3-4c28-be13-b6069acc0350' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29b69f46-fead-4b26-ba2d-0492a4ee2eca' class='xr-var-data-in' type='checkbox'><label for='data-29b69f46-fead-4b26-ba2d-0492a4ee2eca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>147</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>slhf</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated surface latent heat net flux</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_upward_latent_heat_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>slhf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated surface latent heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-34eb1b49-d2d8-4422-b4de-7041ce6c5988' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34eb1b49-d2d8-4422-b4de-7041ce6c5988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c722d396-6aba-47e6-9d95-138f3824aaec' class='xr-var-data-in' type='checkbox'><label for='data-c722d396-6aba-47e6-9d95-138f3824aaec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>45</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>smlt</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snowmelt</dd><dt><span>GRIB_cfVarName :</span></dt><dd>smlt</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snowmelt</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>src</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c897c184-6925-438c-b421-87ca79843173' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c897c184-6925-438c-b421-87ca79843173' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dade50ac-1837-48c9-b3b5-0de471aab10e' class='xr-var-data-in' type='checkbox'><label for='data-dade50ac-1837-48c9-b3b5-0de471aab10e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>198</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>src</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Skin reservoir content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>src</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Skin reservoir content</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bc63dae2-9779-4328-b296-df5f88f39c48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc63dae2-9779-4328-b296-df5f88f39c48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96e39ff4-4a3c-4388-a720-8005d075ecbe' class='xr-var-data-in' type='checkbox'><label for='data-96e39ff4-4a3c-4388-a720-8005d075ecbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>8</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Surface runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Surface runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fd886f92-7196-4a77-a051-d7ffb38d4767' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd886f92-7196-4a77-a051-d7ffb38d4767' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7eb73835-e10d-4ddc-9e82-b9ea12879a0f' class='xr-var-data-in' type='checkbox'><label for='data-7eb73835-e10d-4ddc-9e82-b9ea12879a0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>146</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sshf</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated surface sensible heat net flux</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_upward_sensible_heat_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sshf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated surface sensible heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-735c0747-262c-4c36-afca-2287fa7fe5d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-735c0747-262c-4c36-afca-2287fa7fe5d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7897ac5a-a61a-4596-9231-a0ae9bc5e6de' class='xr-var-data-in' type='checkbox'><label for='data-7897ac5a-a61a-4596-9231-a0ae9bc5e6de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>176</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net short-wave (solar) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6960824b-d6f2-465d-9d50-767594873ec2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6960824b-d6f2-465d-9d50-767594873ec2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebce7b91-a51a-4ed3-862e-d5f8224f1a89' class='xr-var-data-in' type='checkbox'><label for='data-ebce7b91-a51a-4ed3-862e-d5f8224f1a89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>210</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net short-wave (solar) radiation, clear sky</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net short-wave (solar) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cec36b8c-8fb9-4605-9d19-7f7e869151ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cec36b8c-8fb9-4605-9d19-7f7e869151ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cde63e2b-a5a6-4319-8cd0-95c0ccdd4a1b' class='xr-var-data-in' type='checkbox'><label for='data-cde63e2b-a5a6-4319-8cd0-95c0ccdd4a1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>169</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssrd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface short-wave (solar) radiation downwards</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downwelling_shortwave_flux_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssrd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 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968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 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916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface short-wave (solar) radiation downwards</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-19fc16e7-d9a4-4d3f-afb7-73ea5b6eedf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19fc16e7-d9a4-4d3f-afb7-73ea5b6eedf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ceb9db13-9c40-40cd-80c6-b6d85f483419' class='xr-var-data-in' type='checkbox'><label for='data-ceb9db13-9c40-40cd-80c6-b6d85f483419' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>9</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Sub-surface runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 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1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Sub-surface runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d7f786e-0372-4f18-a514-6dd8a6596af9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d7f786e-0372-4f18-a514-6dd8a6596af9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-229e7a74-972a-499b-980b-7d440de940e6' class='xr-var-data-in' type='checkbox'><label for='data-229e7a74-972a-499b-980b-7d440de940e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>34</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sst</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Sea surface temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sst</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Sea surface temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e6a98d73-d19d-46bd-b8e2-1cf985673d3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6a98d73-d19d-46bd-b8e2-1cf985673d3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41380e9c-b2bb-4fbd-b2d6-2796d5c9f773' class='xr-var-data-in' type='checkbox'><label for='data-41380e9c-b2bb-4fbd-b2d6-2796d5c9f773' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>139</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl1</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 1</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9f110e1-aaa5-4eae-9fd5-1e1c2cc03560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9f110e1-aaa5-4eae-9fd5-1e1c2cc03560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc7def63-368b-4439-b32a-f20fd5e6bd33' class='xr-var-data-in' type='checkbox'><label for='data-fc7def63-368b-4439-b32a-f20fd5e6bd33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>170</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl2</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e805f259-2203-4592-8e5d-36c0bcd27cb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e805f259-2203-4592-8e5d-36c0bcd27cb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc594351-9475-47e9-930f-7007da19d461' class='xr-var-data-in' type='checkbox'><label for='data-cc594351-9475-47e9-930f-7007da19d461' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>183</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl3</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5b58a18f-a517-4954-a542-d430618c7e1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b58a18f-a517-4954-a542-d430618c7e1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdb1d0e2-544f-4e3d-b605-1145db42096d' class='xr-var-data-in' type='checkbox'><label for='data-fdb1d0e2-544f-4e3d-b605-1145db42096d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>236</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl4</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2c3bec16-dc63-4736-a2cf-f8897a662c44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c3bec16-dc63-4736-a2cf-f8897a662c44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-172bf16f-a9b3-4b59-9348-a1b813adb162' class='xr-var-data-in' type='checkbox'><label for='data-172bf16f-a9b3-4b59-9348-a1b813adb162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>177</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>str</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net long-wave (thermal) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_upward_longwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>str</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net long-wave (thermal) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c02ae11c-d947-44e4-a811-1fdd24ca41e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c02ae11c-d947-44e4-a811-1fdd24ca41e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e8ed450-790e-483d-9715-3abd7e75b8d9' class='xr-var-data-in' type='checkbox'><label for='data-2e8ed450-790e-483d-9715-3abd7e75b8d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>211</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>strc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net long-wave (thermal) radiation, clear sky</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>strc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net long-wave (thermal) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9c02985a-8fdd-4203-b619-6b20349ca705' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c02985a-8fdd-4203-b619-6b20349ca705' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9168707-1a0b-4462-aa66-39308740a63e' class='xr-var-data-in' type='checkbox'><label for='data-b9168707-1a0b-4462-aa66-39308740a63e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>175</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>strd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface long-wave (thermal) radiation downwards</dd><dt><span>GRIB_cfVarName :</span></dt><dd>strd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface long-wave (thermal) radiation downwards</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sund</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-717648be-d3ec-4724-8f23-9c381bd9505f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-717648be-d3ec-4724-8f23-9c381bd9505f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5da470d8-2a21-4ca6-b1dd-b8403df2a8cc' class='xr-var-data-in' type='checkbox'><label for='data-5da470d8-2a21-4ca6-b1dd-b8403df2a8cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>189</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sund</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Sunshine duration</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sund</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Sunshine duration</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-625e31c8-6c8e-4a8b-84e2-4f977d2c3046' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-625e31c8-6c8e-4a8b-84e2-4f977d2c3046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c028ff6a-3e7b-4550-9311-67e3b866c30d' class='xr-var-data-in' type='checkbox'><label for='data-c028ff6a-3e7b-4550-9311-67e3b866c30d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>39</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl1</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-98ee7ab4-51e1-4c14-98ea-764dc6850d98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98ee7ab4-51e1-4c14-98ea-764dc6850d98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b810f896-1095-4e30-bf56-c7ce6f672845' class='xr-var-data-in' type='checkbox'><label for='data-b810f896-1095-4e30-bf56-c7ce6f672845' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>40</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl2</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-89930f05-7b73-4084-af5a-2677b3272dbc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89930f05-7b73-4084-af5a-2677b3272dbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b79ce420-09ae-49cc-85be-0a668358401f' class='xr-var-data-in' type='checkbox'><label for='data-b79ce420-09ae-49cc-85be-0a668358401f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>41</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl3</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-489f6467-36b4-49d7-87d3-379d2f95b876' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-489f6467-36b4-49d7-87d3-379d2f95b876' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2c19650-d602-4fe5-a8af-1b235e94b28c' class='xr-var-data-in' type='checkbox'><label for='data-e2c19650-d602-4fe5-a8af-1b235e94b28c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>42</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl4</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d03152e-4416-425c-8b22-1ba25b5fcd2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d03152e-4416-425c-8b22-1ba25b5fcd2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b02eec91-48ac-4329-ac88-ca5936f50ae5' class='xr-var-data-in' type='checkbox'><label for='data-b02eec91-48ac-4329-ac88-ca5936f50ae5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>164</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Total cloud cover</dd><dt><span>GRIB_cfName :</span></dt><dd>cloud_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Total cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9849e3df-cead-43f5-a729-607062bc585b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9849e3df-cead-43f5-a729-607062bc585b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25a0449c-3f29-4ea8-99b9-f1c63f63e8cb' class='xr-var-data-in' type='checkbox'><label for='data-25a0449c-3f29-4ea8-99b9-f1c63f63e8cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>79</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tciw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud ice water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tciw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud ice water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-90c7ca71-c09a-4719-be3b-bc42913f7f1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90c7ca71-c09a-4719-be3b-bc42913f7f1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7956e51-dce2-4e2d-aa91-9e9caec5ad28' class='xr-var-data-in' type='checkbox'><label for='data-b7956e51-dce2-4e2d-aa91-9e9caec5ad28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>78</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tclw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud liquid water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tclw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud liquid water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tco3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-858fa190-6c5a-4719-ab61-881ae850555a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-858fa190-6c5a-4719-ab61-881ae850555a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a118f38-4eb7-40b9-8bcc-30e1fd7cba48' class='xr-var-data-in' type='checkbox'><label for='data-9a118f38-4eb7-40b9-8bcc-30e1fd7cba48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>206</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tco3</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column ozone</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_mass_content_of_ozone</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tco3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column ozone</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcrw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e6aaaa62-1814-4b52-b3a2-9a39075c1ad1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6aaaa62-1814-4b52-b3a2-9a39075c1ad1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af3fe057-9fc8-4984-a7b6-ba283db31f55' class='xr-var-data-in' type='checkbox'><label for='data-af3fe057-9fc8-4984-a7b6-ba283db31f55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228089</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcrw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column rain water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcrw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column rain water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcsw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-046599eb-177c-4c67-bd41-7e606b226ac1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-046599eb-177c-4c67-bd41-7e606b226ac1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e6777b-2e19-4de4-bb4a-915366c8a535' class='xr-var-data-in' type='checkbox'><label for='data-85e6777b-2e19-4de4-bb4a-915366c8a535' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228090</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcsw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column snow water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcsw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column snow water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5cdd0942-4cbb-432e-a9fd-b94acce7c23d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cdd0942-4cbb-432e-a9fd-b94acce7c23d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd76410d-5804-4901-8565-9b93fd621588' class='xr-var-data-in' type='checkbox'><label for='data-cd76410d-5804-4901-8565-9b93fd621588' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>136</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcwv</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-896640aa-d553-47a9-a340-2f4e5f894b86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-896640aa-d553-47a9-a340-2f4e5f894b86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99078f07-e996-45de-b4b7-694db69a22b5' class='xr-var-data-in' type='checkbox'><label for='data-99078f07-e996-45de-b4b7-694db69a22b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>137</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcwv</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column vertically-integrated water vapour</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_atmosphere_mass_content_of_water_vapor</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcwv</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column vertically-integrated water vapour</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1710dd02-1ab3-4cbb-a9a8-2526303a4269' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1710dd02-1ab3-4cbb-a9a8-2526303a4269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a3e5f6b-933f-4098-9453-3119cc9b2cfd' class='xr-var-data-in' type='checkbox'><label for='data-4a3e5f6b-933f-4098-9453-3119cc9b2cfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>212</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tisr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>TOA incident short-wave (solar) radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tisr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>TOA incident short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>totalx</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9faf3a86-5f41-4005-8a67-7d47d455bad0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9faf3a86-5f41-4005-8a67-7d47d455bad0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e16f0f25-d72c-45c5-b40e-727cce256d32' class='xr-var-data-in' type='checkbox'><label for='data-e16f0f25-d72c-45c5-b40e-727cce256d32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260123</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>totalx</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Total totals index</dd><dt><span>GRIB_cfVarName :</span></dt><dd>totalx</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Total totals index</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f1395563-36c6-4e9e-99c5-013725eb1b1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1395563-36c6-4e9e-99c5-013725eb1b1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44b6fd27-935e-47f2-816a-9edfae922fae' class='xr-var-data-in' type='checkbox'><label for='data-44b6fd27-935e-47f2-816a-9edfae922fae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Total precipitation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3032e4f2-d519-4fda-a1d0-7dfeb01e481a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3032e4f2-d519-4fda-a1d0-7dfeb01e481a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d091b4ab-7e4d-47d3-9654-589d6405f796' class='xr-var-data-in' type='checkbox'><label for='data-d091b4ab-7e4d-47d3-9654-589d6405f796' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>238</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsn</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Temperature of snow layer</dd><dt><span>GRIB_cfName :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature of snow layer</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c313d8ec-d82b-46f6-8e85-3f8bd09f6f35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c313d8ec-d82b-46f6-8e85-3f8bd09f6f35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5459ee50-9570-4944-9dc0-310726886dd5' class='xr-var-data-in' type='checkbox'><label for='data-5459ee50-9570-4944-9dc0-310726886dd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>178</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net short-wave (solar) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>toa_net_upward_shortwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a54d965e-64bc-4007-95ef-92b3bc2a3541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a54d965e-64bc-4007-95ef-92b3bc2a3541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e81f5cbf-053f-4577-bd5a-5275fa115fc5' class='xr-var-data-in' type='checkbox'><label for='data-e81f5cbf-053f-4577-bd5a-5275fa115fc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>208</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net short-wave (solar) radiation, clear sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net short-wave (solar) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9f6c42ec-0034-4f3b-b182-3327c35e1016' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f6c42ec-0034-4f3b-b182-3327c35e1016' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80959366-7b76-4db1-b0a0-7283b7015e24' class='xr-var-data-in' type='checkbox'><label for='data-80959366-7b76-4db1-b0a0-7283b7015e24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>179</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ttr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net long-wave (thermal) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>toa_outgoing_longwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ttr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net long-wave (thermal) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3346f4f5-a52f-4a4c-8a8c-85f48d0c34ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3346f4f5-a52f-4a4c-8a8c-85f48d0c34ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4ccd850-2b1d-43f4-a383-1f7b264108fe' class='xr-var-data-in' type='checkbox'><label for='data-b4ccd850-2b1d-43f4-a383-1f7b264108fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>209</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ttrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net long-wave (thermal) radiation, clear sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ttrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net long-wave (thermal) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uvb</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2c508b2d-66e2-4ddc-a782-bc7bc8fc118a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c508b2d-66e2-4ddc-a782-bc7bc8fc118a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb511a9a-ed46-4133-af29-799caf0c58c9' class='xr-var-data-in' type='checkbox'><label for='data-eb511a9a-ed46-4133-af29-799caf0c58c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>57</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>uvb</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface downward UV radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>uvb</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface downward UV radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vimd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4cbee88b-845c-466b-9276-4c817e0dea00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cbee88b-845c-466b-9276-4c817e0dea00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-372d688b-f36a-44b8-b2db-2aefe17ae79e' class='xr-var-data-in' type='checkbox'><label for='data-372d688b-f36a-44b8-b2db-2aefe17ae79e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>213</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vimd</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Vertically integrated moisture divergence</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vimd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Vertically integrated moisture divergence</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vis</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f3282000-ead3-4bd2-8a85-838d7eb1ce1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3282000-ead3-4bd2-8a85-838d7eb1ce1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-021397cf-b021-4e15-8b48-02b4750405c4' class='xr-var-data-in' type='checkbox'><label for='data-021397cf-b021-4e15-8b48-02b4750405c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>3020</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vis</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Visibility</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vis</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Visibility</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d8cd4b3-4c11-4fe4-975b-a90e261d77db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d8cd4b3-4c11-4fe4-975b-a90e261d77db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-974e217c-8e51-4c8e-9c08-28b74aa42511' class='xr-var-data-in' type='checkbox'><label for='data-974e217c-8e51-4c8e-9c08-28b74aa42511' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>long_name :</span></dt><dd>Geopotential</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-414662f6-2075-407a-8d32-d45147a33446' class='xr-section-summary-in' type='checkbox'  ><label for='section-414662f6-2075-407a-8d32-d45147a33446' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-879a5a36-beb2-4265-ada3-f6f0915d5b08' class='xr-index-data-in' type='checkbox'/><label for='index-879a5a36-beb2-4265-ada3-f6f0915d5b08' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-31 00:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-07373ff8-447c-4bb6-bd35-75b5e0c8cfc1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-07373ff8-447c-4bb6-bd35-75b5e0c8cfc1' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n",
-       "</dd></dl></div></li></ul></div></div>"
+      "id": "0ad8e22f-66ea-4711-8607-e392815e4594",
+      "cell_type": "code",
+      "source": "earthkit.plots.quickmap.plot(\n    ds_ml[\"q\"].sel(level=137).sel(time=\"2020-07-31T00:00:00\"),\n    x=\"lon\", y=\"lat\",\n);",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 892.6MiB (from 794.0MiB) for this notebook\n",
+          "output_type": "stream"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 700x600 with 2 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 11GB\n",
-       "Dimensions:  (time: 21, value: 654400)\n",
-       "Coordinates:\n",
-       "    lat      (value) float64 5MB ...\n",
-       "    lon      (value) float64 5MB ...\n",
-       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
-       "Dimensions without coordinates: value\n",
-       "Data variables: (12/101)\n",
-       "    10fg     (time, value) float64 110MB ...\n",
-       "    10u      (time, value) float64 110MB ...\n",
-       "    10v      (time, value) float64 110MB ...\n",
-       "    2d       (time, value) float64 110MB ...\n",
-       "    2t       (time, value) float64 110MB ...\n",
-       "    asn      (time, value) float64 110MB ...\n",
-       "    ...       ...\n",
-       "    ttr      (time, value) float64 110MB ...\n",
-       "    ttrc     (time, value) float64 110MB ...\n",
-       "    uvb      (time, value) float64 110MB ...\n",
-       "    vimd     (time, value) float64 110MB ...\n",
-       "    vis      (time, value) float64 110MB ...\n",
-       "    z        (time, value) float64 110MB ...\n",
-       "Attributes:\n",
-       "    GRIB_edition:            2\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc..."
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_sfc = load_hplp_data(leveltype=\"sfc\", gridtype=\"reduced_gg\")\n",
-    "ds_sfc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af588355-687f-43b1-a487-91a5f78e074b",
-   "metadata": {},
-   "source": [
-    "The data can be visualized as demonstrated below. For example, the 2 metre temperature in degrees Celsius on **2020-07-31 at 00:00:00 UTC** is plotted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "5638d36a-f0ca-442f-8879-36411fc61a75",
-   "metadata": {},
-   "outputs": [
+      "execution_count": 10
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "earthkit.plots.quickmap.plot(\n",
-    "    ds_sfc[\"2t\"].sel(time=\"2020-07-31T00:00:00\"),\n",
-    "    units=\"degC\", x=\"lon\", y=\"lat\",\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "658ac441-3d9d-46ab-9e5a-d4c5a159679b",
-   "metadata": {},
-   "source": [
-    "The wave model data is stored on a **reduced_ll** grid and can be loaded as in the following example.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "d0fa5bad-4af2-4cb9-8f76-4814295c41e4",
-   "metadata": {},
-   "outputs": [
+      "id": "0320963f-0d01-4ee8-9cd4-d2aa765ebac6",
+      "cell_type": "markdown",
+      "source": "The surface level data is stored on a **reduced_gg** grid and can be loaded as in the following example.",
+      "metadata": {}
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_wave.grib\n"
-     ]
+      "id": "989d5d36-6eb2-4bdc-804f-48cdc9deec9c",
+      "cell_type": "code",
+      "source": "ds_sfc = load_hplp_data(leveltype=\"sfc\", gridtype=\"reduced_gg\")\nds_sfc",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_sfc.grib\n",
+          "output_type": "stream"
+        },
+        {
+          "execution_count": 11,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "<xarray.Dataset> Size: 11GB\nDimensions:  (time: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables: (12/101)\n    10fg     (time, value) float64 110MB ...\n    10u      (time, value) float64 110MB ...\n    10v      (time, value) float64 110MB ...\n    2d       (time, value) float64 110MB ...\n    2t       (time, value) float64 110MB ...\n    asn      (time, value) float64 110MB ...\n    ...       ...\n    ttr      (time, value) float64 110MB ...\n    ttrc     (time, value) float64 110MB ...\n    uvb      (time, value) float64 110MB ...\n    vimd     (time, value) float64 110MB ...\n    vis      (time, value) float64 110MB ...\n    z        (time, value) float64 110MB ...\nAttributes:\n    GRIB_edition:            2\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 11GB\nDimensions:  (time: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables: (12/101)\n    10fg     (time, value) float64 110MB ...\n    10u      (time, value) float64 110MB ...\n    10v      (time, value) float64 110MB ...\n    2d       (time, value) float64 110MB ...\n    2t       (time, value) float64 110MB ...\n    asn      (time, value) float64 110MB ...\n    ...       ...\n    ttr      (time, value) float64 110MB ...\n    ttrc     (time, value) float64 110MB ...\n    uvb      (time, value) float64 110MB ...\n    vimd     (time, value) float64 110MB ...\n    vis      (time, value) float64 110MB ...\n    z        (time, value) float64 110MB ...\nAttributes:\n    GRIB_edition:            2\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-af0b84c6-4690-4453-82a1-cd7a21be6c33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-af0b84c6-4690-4453-82a1-cd7a21be6c33' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e15a82b2-279a-432f-98b8-9a1d483d1fed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e15a82b2-279a-432f-98b8-9a1d483d1fed' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6e525395-3616-4e49-b9e4-a5cc45d1a793' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e525395-3616-4e49-b9e4-a5cc45d1a793' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-468911d2-f019-48be-b774-27babaa61404' class='xr-var-data-in' type='checkbox'><label for='data-468911d2-f019-48be-b774-27babaa61404' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-146465b6-25bf-4034-9121-88f49bb09bc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-146465b6-25bf-4034-9121-88f49bb09bc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2d141b7-4e86-4488-a796-dd530427a283' class='xr-var-data-in' type='checkbox'><label for='data-c2d141b7-4e86-4488-a796-dd530427a283' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-09be4c47-8b66-4f33-a583-3f467b675263' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09be4c47-8b66-4f33-a583-3f467b675263' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71deb6f2-28b0-4e39-b21a-3fd21baa4cc6' class='xr-var-data-in' type='checkbox'><label for='data-71deb6f2-28b0-4e39-b21a-3fd21baa4cc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fae185ab-33aa-4017-9fe9-896e1e92880c' class='xr-section-summary-in' type='checkbox'  ><label for='section-fae185ab-33aa-4017-9fe9-896e1e92880c' class='xr-section-summary' >Data variables: <span>(101)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10fg</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9bf62ce5-0b45-46ad-93f0-8317311e8cb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bf62ce5-0b45-46ad-93f0-8317311e8cb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dff66f8a-8e7c-4033-adf1-0ccd597f5f63' class='xr-var-data-in' type='checkbox'><label for='data-dff66f8a-8e7c-4033-adf1-0ccd597f5f63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>49</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10fg</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Maximum 10 metre wind gust since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fg10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Maximum 10 metre wind gust since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-22b3c579-e4b5-4879-ba97-2c19acd39571' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22b3c579-e4b5-4879-ba97-2c19acd39571' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31c9c467-91e8-45b3-ba56-f0226589c80b' class='xr-var-data-in' type='checkbox'><label for='data-31c9c467-91e8-45b3-ba56-f0226589c80b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>165</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10u</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre U wind component</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6a1bdd14-6e02-4741-bfc6-55d96909ac67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a1bdd14-6e02-4741-bfc6-55d96909ac67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adcac85d-05b4-491c-9934-aa41ab39ef95' class='xr-var-data-in' type='checkbox'><label for='data-adcac85d-05b4-491c-9934-aa41ab39ef95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>166</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10v</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre V wind component</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a0b83988-0d1f-455d-ae81-bb863eb42a63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0b83988-0d1f-455d-ae81-bb863eb42a63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f7ca0db-b55e-409e-8880-324a139339a8' class='xr-var-data-in' type='checkbox'><label for='data-4f7ca0db-b55e-409e-8880-324a139339a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-52daccda-3a23-46f1-b66a-2a2c0662da06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52daccda-3a23-46f1-b66a-2a2c0662da06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff527b16-27a2-419b-96d6-500207a660d0' class='xr-var-data-in' type='checkbox'><label for='data-ff527b16-27a2-419b-96d6-500207a660d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 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1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>asn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0f39135-59f4-4f2b-a6ed-8204f8f1c737' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0f39135-59f4-4f2b-a6ed-8204f8f1c737' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcf4938a-e4d7-4408-9702-19bc92613ed2' class='xr-var-data-in' type='checkbox'><label for='data-bcf4938a-e4d7-4408-9702-19bc92613ed2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>32</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>asn</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Snow albedo</dd><dt><span>GRIB_cfVarName :</span></dt><dd>asn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Snow albedo</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bld</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-512d3ec5-ea89-46f7-b549-2724c4e03770' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-512d3ec5-ea89-46f7-b549-2724c4e03770' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cff59088-0171-44f5-af1e-a54a132b7256' class='xr-var-data-in' type='checkbox'><label for='data-cff59088-0171-44f5-af1e-a54a132b7256' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>145</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>bld</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated boundary layer dissipation</dd><dt><span>GRIB_cfName :</span></dt><dd>kinetic_energy_dissipation_in_atmosphere_boundary_layer</dd><dt><span>GRIB_cfVarName :</span></dt><dd>bld</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated boundary layer dissipation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blh</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a9efe709-7ecc-41d3-94d5-764846b728d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9efe709-7ecc-41d3-94d5-764846b728d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3312b4f-c393-4c4d-92ca-415a24ce806b' class='xr-var-data-in' type='checkbox'><label for='data-e3312b4f-c393-4c4d-92ca-415a24ce806b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>159</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>blh</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Boundary layer height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>blh</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Boundary layer height</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cape</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3ae7b03-ad5e-4dda-be05-d111f6d8e57d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3ae7b03-ad5e-4dda-be05-d111f6d8e57d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-319e1d29-e506-43d8-a9be-1b8ce43fae7f' class='xr-var-data-in' type='checkbox'><label for='data-319e1d29-e506-43d8-a9be-1b8ce43fae7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>59</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cape</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective available potential energy</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cape</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective available potential energy</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chnk</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dde54a6f-06f0-48a3-93b1-04f71241b3b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dde54a6f-06f0-48a3-93b1-04f71241b3b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f26fe835-c7c1-44f7-a1f0-9f0c574bc77f' class='xr-var-data-in' type='checkbox'><label for='data-f26fe835-c7c1-44f7-a1f0-9f0c574bc77f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>148</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>chnk</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Charnock</dd><dt><span>GRIB_cfVarName :</span></dt><dd>chnk</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Charnock</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ci</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2fd17f42-0931-45ce-9a94-f5a5e900f54e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fd17f42-0931-45ce-9a94-f5a5e900f54e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-163c2443-64fe-4584-bb6b-e4b237f1ae6d' class='xr-var-data-in' type='checkbox'><label for='data-163c2443-64fe-4584-bb6b-e4b237f1ae6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>31</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ci</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>GRIB_cfName :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>siconc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cin</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-06ccb102-1256-4fe2-8bd0-eb5228b5f093' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06ccb102-1256-4fe2-8bd0-eb5228b5f093' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe59fb00-11de-480b-a9c6-a46f7a25f24d' class='xr-var-data-in' type='checkbox'><label for='data-fe59fb00-11de-480b-a9c6-a46f7a25f24d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228001</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cin</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective inhibition</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cin</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective inhibition</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a9081aca-f6c7-492d-b961-d81ccdae223b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9081aca-f6c7-492d-b961-d81ccdae223b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81197e59-51e0-41bc-ade6-7f171cec7ea2' class='xr-var-data-in' type='checkbox'><label for='data-81197e59-51e0-41bc-ade6-7f171cec7ea2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>143</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Convective precipitation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Convective precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-02b92619-1f42-4734-bff6-a8f8d1edd805' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02b92619-1f42-4734-bff6-a8f8d1edd805' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3af131e-484c-4a24-839d-57e503886af7' class='xr-var-data-in' type='checkbox'><label for='data-a3af131e-484c-4a24-839d-57e503886af7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228218</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>crr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective rain rate</dd><dt><span>GRIB_cfVarName :</span></dt><dd>crr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Convective rain rate</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>csfr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ffd0b5d-008d-40e8-9c22-027df91687e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ffd0b5d-008d-40e8-9c22-027df91687e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ae6f47c-98c5-4dad-99f3-d4135fe3939f' class='xr-var-data-in' type='checkbox'><label for='data-4ae6f47c-98c5-4dad-99f3-d4135fe3939f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228220</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>csfr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective snowfall rate water equivalent</dd><dt><span>GRIB_cfVarName :</span></dt><dd>csfr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Convective snowfall rate water equivalent</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dsrp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aefb4ed7-3098-4277-b927-1ee92582fdda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aefb4ed7-3098-4277-b927-1ee92582fdda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b3a42a4-ed0b-439b-ade2-c4fcfe389f80' class='xr-var-data-in' type='checkbox'><label for='data-2b3a42a4-ed0b-439b-ade2-c4fcfe389f80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>47</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>dsrp</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface direct normal short-wave (solar) radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dsrp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface direct normal short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dc722e7a-0ff6-4521-8f38-b0d5ee179c7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc722e7a-0ff6-4521-8f38-b0d5ee179c7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9852e538-5133-4d40-b8aa-d5d3e165b868' class='xr-var-data-in' type='checkbox'><label for='data-9852e538-5133-4d40-b8aa-d5d3e165b868' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>182</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>e</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Evaporation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_water_evaporation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>e</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2e9348f2-3097-4100-b941-0597784b77fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e9348f2-3097-4100-b941-0597784b77fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fe2c44b-0a4f-4c62-ba4b-7513b6155d97' class='xr-var-data-in' type='checkbox'><label for='data-0fe2c44b-0a4f-4c62-ba4b-7513b6155d97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>44</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>es</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow evaporation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>es</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snow evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ddb6319-0002-4586-8225-04a1c5a6e17d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ddb6319-0002-4586-8225-04a1c5a6e17d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6f8f2e9-18f1-484e-b3d3-f88ab354fdba' class='xr-var-data-in' type='checkbox'><label for='data-b6f8f2e9-18f1-484e-b3d3-f88ab354fdba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>180</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ewss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated eastward turbulent surface stress</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downward_eastward_stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ewss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Time-integrated eastward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fal</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d8b8dc36-d7ee-4c11-9dbd-07fef0e63d29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8b8dc36-d7ee-4c11-9dbd-07fef0e63d29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acef5167-c2bf-4a40-92f1-32c7b2acc116' class='xr-var-data-in' type='checkbox'><label for='data-acef5167-c2bf-4a40-92f1-32c7b2acc116' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>243</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fal</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Forecast albedo</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fal</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Forecast albedo</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-10df4cb3-09af-429e-a4ca-f806dfdd8d37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10df4cb3-09af-429e-a4ca-f806dfdd8d37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b7291a1-2846-46ee-9cc9-53ba90e24cd5' class='xr-var-data-in' type='checkbox'><label for='data-7b7291a1-2846-46ee-9cc9-53ba90e24cd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>245</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>flsr</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Forecast logarithm of surface roughness for heat</dd><dt><span>GRIB_cfVarName :</span></dt><dd>flsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Forecast logarithm of surface roughness for heat</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-abd23b65-1dba-48df-80a5-ab309709d5cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abd23b65-1dba-48df-80a5-ab309709d5cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29be5a78-3e3f-434d-b304-0ec21776e87a' class='xr-var-data-in' type='checkbox'><label for='data-29be5a78-3e3f-434d-b304-0ec21776e87a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>244</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fsr</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Forecast surface roughness</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Forecast surface roughness</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fzra</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eaa959a1-166d-4128-b23f-9f94f5d64c17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eaa959a1-166d-4128-b23f-9f94f5d64c17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de74c275-733c-4648-b5fe-d7deedc73e46' class='xr-var-data-in' type='checkbox'><label for='data-de74c275-733c-4648-b5fe-d7deedc73e46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228216</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fzra</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Accumulated freezing rain</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fzra</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Accumulated freezing rain</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gwd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b2e36c05-c2a4-43d1-9810-93eabd4ca6d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2e36c05-c2a4-43d1-9810-93eabd4ca6d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e778ab40-cf4f-4f8b-9804-3ec964b331f2' class='xr-var-data-in' type='checkbox'><label for='data-e778ab40-cf4f-4f8b-9804-3ec964b331f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>197</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>gwd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Gravity wave dissipation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>gwd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Gravity wave dissipation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1bae40ae-dffc-4c06-b58b-d43944ebc26c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bae40ae-dffc-4c06-b58b-d43944ebc26c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6324388-c8c4-464e-83dc-5e6dc1ebaed5' class='xr-var-data-in' type='checkbox'><label for='data-b6324388-c8c4-464e-83dc-5e6dc1ebaed5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>188</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>hcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>High cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>High cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>i10fg</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f3e74420-ec5f-4a8e-bf52-4706d325f508' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3e74420-ec5f-4a8e-bf52-4706d325f508' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57854756-4ab8-4a5c-8117-a560c9b52ee4' class='xr-var-data-in' type='checkbox'><label for='data-57854756-4ab8-4a5c-8117-a560c9b52ee4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228029</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>i10fg</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous 10 metre wind gust</dd><dt><span>GRIB_cfVarName :</span></dt><dd>i10fg</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Instantaneous 10 metre wind gust</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ie</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bb51bd9c-8a60-4dd6-8d56-d16033ff017e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb51bd9c-8a60-4dd6-8d56-d16033ff017e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18de1af7-fcb9-45ad-8b24-dd1a0c6e857f' class='xr-var-data-in' type='checkbox'><label for='data-18de1af7-fcb9-45ad-8b24-dd1a0c6e857f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>232</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ie</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous moisture flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ie</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Instantaneous moisture flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iews</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71cc9741-b711-404c-b588-0870b6062504' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71cc9741-b711-404c-b588-0870b6062504' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b90e40f3-8071-47d7-a14a-5ffe2d6fa10b' class='xr-var-data-in' type='checkbox'><label for='data-b90e40f3-8071-47d7-a14a-5ffe2d6fa10b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>229</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>iews</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous eastward turbulent surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>iews</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous eastward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ilspf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8a99e117-a1c2-48d6-bba8-18bcb5a53d1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a99e117-a1c2-48d6-bba8-18bcb5a53d1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b4b8950-3f46-4821-bf06-580d3b704e68' class='xr-var-data-in' type='checkbox'><label for='data-9b4b8950-3f46-4821-bf06-580d3b704e68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228217</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ilspf</dd><dt><span>GRIB_units :</span></dt><dd>Proportion</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous large-scale precipitation fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ilspf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Proportion</dd><dt><span>long_name :</span></dt><dd>Instantaneous large-scale precipitation fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3298d974-109e-4c1b-8c81-9b14b75a7161' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3298d974-109e-4c1b-8c81-9b14b75a7161' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5520e12-44ec-4c22-b6eb-265d589a9269' class='xr-var-data-in' type='checkbox'><label for='data-a5520e12-44ec-4c22-b6eb-265d589a9269' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>230</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>inss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous northward turbulent surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>inss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous northward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ishf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a379f0f4-7ece-4c70-a007-ed0373fd5c8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a379f0f4-7ece-4c70-a007-ed0373fd5c8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6025ae77-ca86-440a-b450-2394923b70e6' class='xr-var-data-in' type='checkbox'><label for='data-6025ae77-ca86-440a-b450-2394923b70e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>231</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ishf</dd><dt><span>GRIB_units :</span></dt><dd>W m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous surface sensible heat net flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ishf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>W m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous surface sensible heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1048ea07-f239-450c-8263-8f8ccf97ae6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1048ea07-f239-450c-8263-8f8ccf97ae6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ff0a7a9-ef74-4f8c-8603-5e9d8306c7bb' class='xr-var-data-in' type='checkbox'><label for='data-7ff0a7a9-ef74-4f8c-8603-5e9d8306c7bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>35</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl1</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 1</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2d1c441d-134c-43d3-a4bc-9a066fccaf1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d1c441d-134c-43d3-a4bc-9a066fccaf1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea89a450-97fb-414c-a6db-81d090d7ccd6' class='xr-var-data-in' type='checkbox'><label for='data-ea89a450-97fb-414c-a6db-81d090d7ccd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>36</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl2</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b4d6aa3c-952e-48e9-b996-afe3af1b5124' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4d6aa3c-952e-48e9-b996-afe3af1b5124' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afe591d7-3ba0-440e-9007-31f9fac20f90' class='xr-var-data-in' type='checkbox'><label for='data-afe591d7-3ba0-440e-9007-31f9fac20f90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>37</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl3</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-364e35bd-175f-4bbb-ab35-4281f824e0ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-364e35bd-175f-4bbb-ab35-4281f824e0ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4664d51a-5611-4732-bdf8-0f07e53366d1' class='xr-var-data-in' type='checkbox'><label for='data-4664d51a-5611-4732-bdf8-0f07e53366d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>38</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl4</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kx</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b45b364-fff9-46d3-b1e6-0443e67a3ec1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b45b364-fff9-46d3-b1e6-0443e67a3ec1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8d01c9e-d113-4925-aba2-df6a48771cf7' class='xr-var-data-in' type='checkbox'><label for='data-a8d01c9e-d113-4925-aba2-df6a48771cf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260121</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>kx</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>K index</dd><dt><span>GRIB_cfVarName :</span></dt><dd>kx</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>K index</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0207716b-9791-48b7-83e5-b8ceb4f84ea3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0207716b-9791-48b7-83e5-b8ceb4f84ea3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ceb0746b-aa98-49e2-a4b1-8caed48c34ba' class='xr-var-data-in' type='checkbox'><label for='data-ceb0746b-aa98-49e2-a4b1-8caed48c34ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>186</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Low cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 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160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Low cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lgws</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-61b0fe19-d2cc-4e1b-9722-1eaeb0dff220' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61b0fe19-d2cc-4e1b-9722-1eaeb0dff220' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91c22ff9-307f-48b4-9a92-0babadcac9ba' class='xr-var-data-in' type='checkbox'><label for='data-91c22ff9-307f-48b4-9a92-0babadcac9ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>195</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lgws</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Eastward gravity wave surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lgws</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 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1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Eastward gravity wave surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsm</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-317bb4a7-9f0c-4a79-ac95-2155cd8eface' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-317bb4a7-9f0c-4a79-ac95-2155cd8eface' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c690aec-3603-41ed-a133-e7008626465e' class='xr-var-data-in' type='checkbox'><label for='data-3c690aec-3603-41ed-a133-e7008626465e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>172</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsm</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Land-sea mask</dd><dt><span>GRIB_cfName :</span></dt><dd>land_binary_mask</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsm</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 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1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Land-sea mask</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2e950e65-2040-4206-8351-fba8e7255576' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e950e65-2040-4206-8351-fba8e7255576' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2690d435-5d4d-4a98-8088-c2d99dace832' class='xr-var-data-in' type='checkbox'><label for='data-2690d435-5d4d-4a98-8088-c2d99dace832' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>142</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Large-scale precipitation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_stratiform_precipitation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Large-scale precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lspf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe70be50-30fa-4e14-9486-fccc9ffd8a5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe70be50-30fa-4e14-9486-fccc9ffd8a5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b325328c-9cf1-4d7a-a6f8-330a94b1f923' class='xr-var-data-in' type='checkbox'><label for='data-b325328c-9cf1-4d7a-a6f8-330a94b1f923' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>50</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lspf</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Large-scale precipitation fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lspf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Large-scale precipitation fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsrr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3484e2ef-2b21-494e-ac54-df860a23c4df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3484e2ef-2b21-494e-ac54-df860a23c4df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb41c74c-1b78-44bd-8256-f505e48ab005' class='xr-var-data-in' type='checkbox'><label for='data-cb41c74c-1b78-44bd-8256-f505e48ab005' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228219</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsrr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Large scale rain rate</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsrr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Large scale rain rate</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lssfr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-57b3ae8e-8ca6-4bc3-b55e-8e3681cc838f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57b3ae8e-8ca6-4bc3-b55e-8e3681cc838f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7636e64d-bb84-4399-8a0e-3b8dd491fadd' class='xr-var-data-in' type='checkbox'><label for='data-7636e64d-bb84-4399-8a0e-3b8dd491fadd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228221</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lssfr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Large scale snowfall rate water equivalent</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lssfr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Large scale snowfall rate water equivalent</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9ada0a2-ba7d-44c1-9c0e-a309f8c52586' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9ada0a2-ba7d-44c1-9c0e-a309f8c52586' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e60ddb49-3d64-4571-980c-eb0d0f4aaeaa' class='xr-var-data-in' type='checkbox'><label for='data-e60ddb49-3d64-4571-980c-eb0d0f4aaeaa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>187</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Medium cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Medium cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mgws</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d6678fd-7769-43a0-8ccc-4e9c036ac87e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d6678fd-7769-43a0-8ccc-4e9c036ac87e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f248e374-3a3a-468b-b1f9-64316418ddcb' class='xr-var-data-in' type='checkbox'><label for='data-f248e374-3a3a-468b-b1f9-64316418ddcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>196</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mgws</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Northward gravity wave surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mgws</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Northward gravity wave surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mn2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d1d95ea7-9aae-4387-9e36-0dc4dcc6bc59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1d95ea7-9aae-4387-9e36-0dc4dcc6bc59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f46fd5ca-a543-42de-b39a-12dc176bf3c3' class='xr-var-data-in' type='checkbox'><label for='data-f46fd5ca-a543-42de-b39a-12dc176bf3c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>202</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mn2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mn2t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-57b40649-bec2-4419-9b9d-3dfd40691c17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57b40649-bec2-4419-9b9d-3dfd40691c17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b35e2f93-608a-47cd-9f8e-0bd008ba27a8' class='xr-var-data-in' type='checkbox'><label for='data-b35e2f93-608a-47cd-9f8e-0bd008ba27a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>msl</dd><dt><span>GRIB_units :</span></dt><dd>Pa</dd><dt><span>GRIB_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>GRIB_cfName :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>GRIB_cfVarName :</span></dt><dd>msl</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>Mean sea level pressure</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mx2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fdbc11be-81c0-4153-b9a5-b7331e741090' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdbc11be-81c0-4153-b9a5-b7331e741090' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-602f574b-f208-477d-bf6a-5d60d4c509a1' class='xr-var-data-in' type='checkbox'><label for='data-602f574b-f208-477d-bf6a-5d60d4c509a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>201</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mx2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mx2t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d40b7ba9-6d8a-478b-9bdf-a90b58f394ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d40b7ba9-6d8a-478b-9bdf-a90b58f394ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-619488ce-1a28-4bd7-9ee5-cc49833a3547' class='xr-var-data-in' type='checkbox'><label for='data-619488ce-1a28-4bd7-9ee5-cc49833a3547' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>181</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>nsss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated northward turbulent surface stress</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downward_northward_stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>nsss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Time-integrated northward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocu</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4020b49f-19a7-42ed-80b8-92f77239e006' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4020b49f-19a7-42ed-80b8-92f77239e006' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db8e6521-5896-4705-9beb-6e572cdcf9d5' class='xr-var-data-in' type='checkbox'><label for='data-db8e6521-5896-4705-9beb-6e572cdcf9d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151131</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ocu</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Eastward surface sea water velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>uoe</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Eastward surface sea water velocity</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocv</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd304fc2-3ac0-4fd1-90a5-a53905b5fa2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd304fc2-3ac0-4fd1-90a5-a53905b5fa2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e902536-6bae-44cc-86e7-cd3126b421b5' class='xr-var-data-in' type='checkbox'><label for='data-8e902536-6bae-44cc-86e7-cd3126b421b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151132</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ocv</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Northward surface sea water velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>von</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Northward surface sea water velocity</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>par</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1aef762c-1804-4cc9-aca6-52fa0338f644' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1aef762c-1804-4cc9-aca6-52fa0338f644' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6dcaa6c-33cf-4c92-96e7-e251e0c9b357' class='xr-var-data-in' type='checkbox'><label for='data-b6dcaa6c-33cf-4c92-96e7-e251e0c9b357' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>58</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>par</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Photosynthetically active radiation at the surface</dd><dt><span>GRIB_cfVarName :</span></dt><dd>par</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Photosynthetically active radiation at the surface</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pev</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0c714488-07dd-4175-83bc-75cf2ef4387e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c714488-07dd-4175-83bc-75cf2ef4387e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fa3f44e-f6eb-4f71-9010-03a0d991b955' class='xr-var-data-in' type='checkbox'><label for='data-7fa3f44e-f6eb-4f71-9010-03a0d991b955' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228251</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>pev</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Potential evaporation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>pev</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Potential evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ptype</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6b7581c0-70b5-4898-a2d9-768ae0bf5316' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b7581c0-70b5-4898-a2d9-768ae0bf5316' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e6e4e89-a1cd-4b94-aea4-6d3f2642438c' class='xr-var-data-in' type='checkbox'><label for='data-6e6e4e89-a1cd-4b94-aea4-6d3f2642438c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260015</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ptype</dd><dt><span>GRIB_units :</span></dt><dd>(Code table 4.201)</dd><dt><span>GRIB_name :</span></dt><dd>Precipitation type</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ptype</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(Code table 4.201)</dd><dt><span>long_name :</span></dt><dd>Precipitation type</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aef2a71a-3387-4e43-9a08-5b1f66191a7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aef2a71a-3387-4e43-9a08-5b1f66191a7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eade02b5-d3f3-4cfd-be29-06c336c65f56' class='xr-var-data-in' type='checkbox'><label for='data-eade02b5-d3f3-4cfd-be29-06c336c65f56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>205</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-de5e4fcd-c741-4bbe-8c82-840246fbdac0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de5e4fcd-c741-4bbe-8c82-840246fbdac0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eb6200f-56dc-4f22-9c97-fbb4ba2e601b' class='xr-var-data-in' type='checkbox'><label for='data-3eb6200f-56dc-4f22-9c97-fbb4ba2e601b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>33</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>rsn</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Snow density</dd><dt><span>GRIB_cfVarName :</span></dt><dd>rsn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-3</dd><dt><span>long_name :</span></dt><dd>Snow density</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6919c2f-8407-48ca-9c75-ab5832c99ceb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6919c2f-8407-48ca-9c75-ab5832c99ceb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5430264a-47af-418e-8ab9-edd73bcbab92' class='xr-var-data-in' type='checkbox'><label for='data-5430264a-47af-418e-8ab9-edd73bcbab92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>141</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sd</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow depth</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snow depth</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3fcebd12-1652-428a-937f-540d285b35cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fcebd12-1652-428a-937f-540d285b35cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c68793ab-9f7f-4520-a892-485f780a924f' class='xr-var-data-in' type='checkbox'><label for='data-c68793ab-9f7f-4520-a892-485f780a924f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>144</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sf</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snowfall</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snowfall</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-796280a9-653c-4ffc-85f6-e86581651155' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-796280a9-653c-4ffc-85f6-e86581651155' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0b2e815-b5bd-4448-884f-20c4d2cd7d26' class='xr-var-data-in' type='checkbox'><label for='data-a0b2e815-b5bd-4448-884f-20c4d2cd7d26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>235</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>skt</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Skin temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>skt</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Skin temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-56ed2c60-2ac2-494a-92a2-8b0a6b25a3fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56ed2c60-2ac2-494a-92a2-8b0a6b25a3fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faa336e5-f61d-4ca5-8779-b7967697e914' class='xr-var-data-in' type='checkbox'><label for='data-faa336e5-f61d-4ca5-8779-b7967697e914' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>147</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>slhf</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated surface latent heat net flux</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_upward_latent_heat_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>slhf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated surface latent heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fee51575-03c5-45a2-ad53-ff832fe28b24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fee51575-03c5-45a2-ad53-ff832fe28b24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bd33ae1-2542-4154-94fd-4b5f29772dea' class='xr-var-data-in' type='checkbox'><label for='data-3bd33ae1-2542-4154-94fd-4b5f29772dea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>45</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>smlt</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snowmelt</dd><dt><span>GRIB_cfVarName :</span></dt><dd>smlt</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snowmelt</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>src</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9205b419-7233-4b4a-a879-bb9ef1098049' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9205b419-7233-4b4a-a879-bb9ef1098049' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a53d241d-9bf7-4657-b088-63b84eb3a57d' class='xr-var-data-in' type='checkbox'><label for='data-a53d241d-9bf7-4657-b088-63b84eb3a57d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>198</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>src</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Skin reservoir content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>src</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Skin reservoir content</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e7f28a22-7fa2-4d6a-a662-3e72e4dff414' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7f28a22-7fa2-4d6a-a662-3e72e4dff414' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be186a07-a207-4092-9caa-43a60413d616' class='xr-var-data-in' type='checkbox'><label for='data-be186a07-a207-4092-9caa-43a60413d616' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>8</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Surface runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Surface runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-189c6cd7-00c0-40de-8c4e-f38a222ce192' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-189c6cd7-00c0-40de-8c4e-f38a222ce192' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d7ea1b5-1917-400e-8514-9d91ad2dda18' class='xr-var-data-in' type='checkbox'><label for='data-6d7ea1b5-1917-400e-8514-9d91ad2dda18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>146</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sshf</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated surface sensible heat net flux</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_upward_sensible_heat_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sshf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated surface sensible heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-66a35b14-0a20-4d6f-8055-0b01996490a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66a35b14-0a20-4d6f-8055-0b01996490a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eef8ec09-6c67-43b8-95cf-b7831311feef' class='xr-var-data-in' type='checkbox'><label for='data-eef8ec09-6c67-43b8-95cf-b7831311feef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>176</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net short-wave (solar) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c345174-d180-4eac-a926-b1c1c7990493' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c345174-d180-4eac-a926-b1c1c7990493' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38e699f2-aa1e-4a1f-aea7-9538bf36079f' class='xr-var-data-in' type='checkbox'><label for='data-38e699f2-aa1e-4a1f-aea7-9538bf36079f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>210</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net short-wave (solar) radiation, clear sky</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net short-wave (solar) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c59948a-471f-49b5-b2d3-4abe11cf21a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c59948a-471f-49b5-b2d3-4abe11cf21a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfb711c9-5c72-4679-9ebf-5e91585408b7' class='xr-var-data-in' type='checkbox'><label for='data-cfb711c9-5c72-4679-9ebf-5e91585408b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>169</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssrd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface short-wave (solar) radiation downwards</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downwelling_shortwave_flux_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssrd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface short-wave (solar) radiation downwards</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a0edc35d-4421-4907-9b2b-9683ef933366' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0edc35d-4421-4907-9b2b-9683ef933366' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7223cc7e-991b-4e6b-af92-259e71f7247b' class='xr-var-data-in' type='checkbox'><label for='data-7223cc7e-991b-4e6b-af92-259e71f7247b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>9</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Sub-surface runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Sub-surface runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ae9df472-984e-44c6-af08-e13cc68d664c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae9df472-984e-44c6-af08-e13cc68d664c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c2aec12-b511-46da-aa7f-eab776fbd6a9' class='xr-var-data-in' type='checkbox'><label for='data-8c2aec12-b511-46da-aa7f-eab776fbd6a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>34</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sst</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Sea surface temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sst</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 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1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Sea surface temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71428f56-419d-442d-9e45-40077196ab9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71428f56-419d-442d-9e45-40077196ab9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf8205a0-b8f6-472f-9cba-55cba4b43248' class='xr-var-data-in' type='checkbox'><label for='data-bf8205a0-b8f6-472f-9cba-55cba4b43248' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>139</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl1</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 1</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ec021033-a9bb-426b-bc51-72c3f5664c2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec021033-a9bb-426b-bc51-72c3f5664c2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0950d5c5-792a-4e41-9a2c-e2133a749fa2' class='xr-var-data-in' type='checkbox'><label for='data-0950d5c5-792a-4e41-9a2c-e2133a749fa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>170</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl2</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 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1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d78dcb46-83ae-47df-92b3-5762e8bbd057' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d78dcb46-83ae-47df-92b3-5762e8bbd057' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db7647bf-9cd7-4ac9-943f-b421bd843d5a' class='xr-var-data-in' type='checkbox'><label for='data-db7647bf-9cd7-4ac9-943f-b421bd843d5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>183</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl3</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-496bb793-77f4-422f-91fd-314e2984ba28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-496bb793-77f4-422f-91fd-314e2984ba28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-179218c6-f471-4bc6-82e3-c5ad7aa6fe7f' class='xr-var-data-in' type='checkbox'><label for='data-179218c6-f471-4bc6-82e3-c5ad7aa6fe7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>236</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl4</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d730cb9b-4ba7-4e85-8680-7819f50a3eb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d730cb9b-4ba7-4e85-8680-7819f50a3eb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3214c1c0-034d-4cad-a0cb-31896d0bb8a1' class='xr-var-data-in' type='checkbox'><label for='data-3214c1c0-034d-4cad-a0cb-31896d0bb8a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>177</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>str</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net long-wave (thermal) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_upward_longwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>str</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net long-wave (thermal) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7fa137bc-2612-41c8-b479-d2d553ec3618' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fa137bc-2612-41c8-b479-d2d553ec3618' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db36b9b2-212e-4b12-8c07-3594d2c55791' class='xr-var-data-in' type='checkbox'><label for='data-db36b9b2-212e-4b12-8c07-3594d2c55791' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>211</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>strc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net long-wave (thermal) radiation, clear sky</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>strc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net long-wave (thermal) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0294010e-5f30-4db4-a9d4-6ee3d2f98c54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0294010e-5f30-4db4-a9d4-6ee3d2f98c54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a681a71-c67d-45a3-bf2d-2ee6dacd31ba' class='xr-var-data-in' type='checkbox'><label for='data-1a681a71-c67d-45a3-bf2d-2ee6dacd31ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>175</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>strd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface long-wave (thermal) radiation downwards</dd><dt><span>GRIB_cfVarName :</span></dt><dd>strd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface long-wave (thermal) radiation downwards</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sund</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd1119ba-4b8e-4a2c-8388-13d7a53d4c08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd1119ba-4b8e-4a2c-8388-13d7a53d4c08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf6caaca-f6a6-4a6d-9e16-8a66ff57cc95' class='xr-var-data-in' type='checkbox'><label for='data-cf6caaca-f6a6-4a6d-9e16-8a66ff57cc95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>189</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sund</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Sunshine duration</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sund</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Sunshine duration</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf39951f-52b3-4066-bd6e-fb61ce1a581a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf39951f-52b3-4066-bd6e-fb61ce1a581a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10b9ae13-fb24-4324-b1f9-bdcb28160cbb' class='xr-var-data-in' type='checkbox'><label for='data-10b9ae13-fb24-4324-b1f9-bdcb28160cbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>39</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl1</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6f5069cc-e64d-4a07-a70c-0247ebdfe1aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f5069cc-e64d-4a07-a70c-0247ebdfe1aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fde99bc5-b764-453f-a215-3d18ecc8bdb7' class='xr-var-data-in' type='checkbox'><label for='data-fde99bc5-b764-453f-a215-3d18ecc8bdb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>40</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl2</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5445d33-591c-4d12-a3ab-a44466a144f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5445d33-591c-4d12-a3ab-a44466a144f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c51bf9e7-c832-4b93-9244-43a7928d75d4' class='xr-var-data-in' type='checkbox'><label for='data-c51bf9e7-c832-4b93-9244-43a7928d75d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>41</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl3</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f2cf340a-5977-4896-b030-ee67055d166b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2cf340a-5977-4896-b030-ee67055d166b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e7a5fd5-8649-48fa-b553-c67035250706' class='xr-var-data-in' type='checkbox'><label for='data-6e7a5fd5-8649-48fa-b553-c67035250706' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>42</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl4</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42b204a7-fabf-4129-9ef2-e60ecfdb477b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42b204a7-fabf-4129-9ef2-e60ecfdb477b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c1745a2-bb24-488f-99c2-9d3ee61d75db' class='xr-var-data-in' type='checkbox'><label for='data-6c1745a2-bb24-488f-99c2-9d3ee61d75db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>164</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Total cloud cover</dd><dt><span>GRIB_cfName :</span></dt><dd>cloud_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Total cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4eaf8857-e4f3-4cad-885f-08d76371cc98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4eaf8857-e4f3-4cad-885f-08d76371cc98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3941398-872b-49ef-b36e-08e8063534b7' class='xr-var-data-in' type='checkbox'><label for='data-b3941398-872b-49ef-b36e-08e8063534b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>79</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tciw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud ice water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tciw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud ice water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-36d13aca-5a0a-4e44-8a98-c016e3f8727e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36d13aca-5a0a-4e44-8a98-c016e3f8727e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca307c41-f4cd-4f9f-8a3b-ef052bbeed31' class='xr-var-data-in' type='checkbox'><label for='data-ca307c41-f4cd-4f9f-8a3b-ef052bbeed31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>78</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tclw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud liquid water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tclw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud liquid water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tco3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-170fe159-b55a-4970-ba67-3bc152e9a1ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-170fe159-b55a-4970-ba67-3bc152e9a1ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60b5f8ed-2cc5-4718-834f-40e352ef8265' class='xr-var-data-in' type='checkbox'><label for='data-60b5f8ed-2cc5-4718-834f-40e352ef8265' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>206</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tco3</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column ozone</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_mass_content_of_ozone</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tco3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column ozone</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcrw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-877c760b-a606-4e89-a7b3-d8f4f87d7e1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-877c760b-a606-4e89-a7b3-d8f4f87d7e1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-158868b7-f951-4127-bd97-0441b88ac9bb' class='xr-var-data-in' type='checkbox'><label for='data-158868b7-f951-4127-bd97-0441b88ac9bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228089</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcrw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column rain water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcrw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column rain water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcsw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-92accef0-9a54-4f3e-84ae-fa47de9fdb62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92accef0-9a54-4f3e-84ae-fa47de9fdb62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f803fae-5e96-4dd5-9c6f-4972e48e0580' class='xr-var-data-in' type='checkbox'><label for='data-2f803fae-5e96-4dd5-9c6f-4972e48e0580' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228090</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcsw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column snow water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcsw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column snow water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e9d6e8b8-49a0-4418-872b-741a94c2f3fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9d6e8b8-49a0-4418-872b-741a94c2f3fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-350d1d2b-f586-4fd4-b64d-c14a0f53c51f' class='xr-var-data-in' type='checkbox'><label for='data-350d1d2b-f586-4fd4-b64d-c14a0f53c51f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>136</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcwv</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-056eb36b-6643-488a-8198-a51a058203a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-056eb36b-6643-488a-8198-a51a058203a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-665b25b3-b3fc-4436-8329-d39e920e8976' class='xr-var-data-in' type='checkbox'><label for='data-665b25b3-b3fc-4436-8329-d39e920e8976' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>137</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcwv</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column vertically-integrated water vapour</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_atmosphere_mass_content_of_water_vapor</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcwv</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column vertically-integrated water vapour</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-977c09a5-749b-4868-b179-7e3a3c7b64a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-977c09a5-749b-4868-b179-7e3a3c7b64a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d8005af-5ec7-464d-a245-03f0f85d2605' class='xr-var-data-in' type='checkbox'><label for='data-8d8005af-5ec7-464d-a245-03f0f85d2605' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>212</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tisr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>TOA incident short-wave (solar) radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tisr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>TOA incident short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>totalx</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4eb27327-b609-40d4-ac09-5ad17e362fc2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4eb27327-b609-40d4-ac09-5ad17e362fc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fdb3047-44a8-4f4a-8906-2339e9cb186b' class='xr-var-data-in' type='checkbox'><label for='data-6fdb3047-44a8-4f4a-8906-2339e9cb186b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260123</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>totalx</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Total totals index</dd><dt><span>GRIB_cfVarName :</span></dt><dd>totalx</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Total totals index</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9a0403e5-48dc-4378-aefb-fecdcdb6d640' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a0403e5-48dc-4378-aefb-fecdcdb6d640' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10b275e5-6939-4a7f-9a57-ed6dd609d418' class='xr-var-data-in' type='checkbox'><label for='data-10b275e5-6939-4a7f-9a57-ed6dd609d418' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Total precipitation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c1584352-e35c-4fe1-9bce-564b752c647e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1584352-e35c-4fe1-9bce-564b752c647e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72cc0642-55c6-4820-86af-95683105e2d0' class='xr-var-data-in' type='checkbox'><label for='data-72cc0642-55c6-4820-86af-95683105e2d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>238</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsn</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Temperature of snow layer</dd><dt><span>GRIB_cfName :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature of snow layer</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9137f236-267a-4f40-a677-17cb074588b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9137f236-267a-4f40-a677-17cb074588b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0f9e621-ee64-4200-90fd-600b644eca0d' class='xr-var-data-in' type='checkbox'><label for='data-a0f9e621-ee64-4200-90fd-600b644eca0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>178</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net short-wave (solar) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>toa_net_upward_shortwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c6edeb83-8cf4-4a93-9e5c-f0b208894b68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6edeb83-8cf4-4a93-9e5c-f0b208894b68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c04f4766-f0d1-4ece-a540-d9973c339da7' class='xr-var-data-in' type='checkbox'><label for='data-c04f4766-f0d1-4ece-a540-d9973c339da7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>208</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net short-wave (solar) radiation, clear sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net short-wave (solar) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45647650-9932-4489-ae4f-ab832cf0169f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45647650-9932-4489-ae4f-ab832cf0169f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17ee677e-2541-48dd-95a6-b517e72b04a9' class='xr-var-data-in' type='checkbox'><label for='data-17ee677e-2541-48dd-95a6-b517e72b04a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>179</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ttr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net long-wave (thermal) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>toa_outgoing_longwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ttr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 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1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net long-wave (thermal) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aedaece5-3de1-4ed8-a3af-fb0cbb35a791' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aedaece5-3de1-4ed8-a3af-fb0cbb35a791' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8f0d149-8698-4e2f-8c0b-429ecc754bfb' class='xr-var-data-in' type='checkbox'><label for='data-f8f0d149-8698-4e2f-8c0b-429ecc754bfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>209</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ttrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net long-wave (thermal) radiation, clear sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ttrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net long-wave (thermal) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uvb</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84a29eb8-4c6b-4355-af42-86d1eeb9baf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84a29eb8-4c6b-4355-af42-86d1eeb9baf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f8e2fc5-3642-4f5c-8f24-495ecdfcba13' class='xr-var-data-in' type='checkbox'><label for='data-8f8e2fc5-3642-4f5c-8f24-495ecdfcba13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>57</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>uvb</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface downward UV radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>uvb</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface downward UV radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vimd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7225b934-1def-4765-83e4-0d12d78ce5de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7225b934-1def-4765-83e4-0d12d78ce5de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a065f09-0c53-4a1a-9a92-4a87d969c090' class='xr-var-data-in' type='checkbox'><label for='data-7a065f09-0c53-4a1a-9a92-4a87d969c090' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>213</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vimd</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Vertically integrated moisture divergence</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vimd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Vertically integrated moisture divergence</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vis</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e66c8d44-3798-42e2-8898-3d3869911b76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e66c8d44-3798-42e2-8898-3d3869911b76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee47a936-b761-466f-b684-0bcea74c5059' class='xr-var-data-in' type='checkbox'><label for='data-ee47a936-b761-466f-b684-0bcea74c5059' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>3020</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vis</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Visibility</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vis</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Visibility</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f14516ab-834c-4317-81b8-ef2860c2c963' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f14516ab-834c-4317-81b8-ef2860c2c963' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb76289e-33cb-474d-91ad-e449ce747980' class='xr-var-data-in' type='checkbox'><label for='data-bb76289e-33cb-474d-91ad-e449ce747980' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 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><label for='section-e3060d70-b3bb-4e40-9052-cf8684106005' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1c7349db-4c8f-4f3c-9d91-4a826100be10' class='xr-index-data-in' type='checkbox'/><label for='index-1c7349db-4c8f-4f3c-9d91-4a826100be10' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2910b6c0-c581-4723-a3d1-0324f0e344c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2910b6c0-c581-4723-a3d1-0324f0e344c4' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
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-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 958MB\n",
-       "Dimensions:  (time: 21, value: 315258)\n",
-       "Coordinates:\n",
-       "    lat      (value) float64 3MB ...\n",
-       "    lon      (value) float64 3MB ...\n",
-       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
-       "Dimensions without coordinates: value\n",
-       "Data variables: (12/18)\n",
-       "    cdww     (time, value) float64 53MB ...\n",
-       "    dwi      (time, value) float64 53MB ...\n",
-       "    hmax     (time, value) float64 53MB ...\n",
-       "    mp1      (time, value) float64 53MB ...\n",
-       "    mp2      (time, value) float64 53MB ...\n",
-       "    mwd      (time, value) float64 53MB ...\n",
-       "    ...       ...\n",
-       "    ust      (time, value) float64 53MB ...\n",
-       "    vst      (time, value) float64 53MB ...\n",
-       "    wdw      (time, value) float64 53MB ...\n",
-       "    wind     (time, value) float64 53MB ...\n",
-       "    wmb      (time, value) float64 53MB ...\n",
-       "    wstar    (time, value) float64 53MB ...\n",
-       "Attributes:\n",
-       "    GRIB_edition:            1\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-524328d0-a617-4af7-8a78-a1a6d7de09f9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-524328d0-a617-4af7-8a78-a1a6d7de09f9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span>value</span>: 315258</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d0cc7db7-f3ea-473e-96c7-647331ea5065' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0cc7db7-f3ea-473e-96c7-647331ea5065' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3e111003-570d-441b-8bea-b9187ea09e5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e111003-570d-441b-8bea-b9187ea09e5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb470e05-4412-41bb-a6b4-7055449851d8' class='xr-var-data-in' type='checkbox'><label for='data-eb470e05-4412-41bb-a6b4-7055449851d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[315258 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c5e2d12-94e6-433c-ae30-66db6a8c1c00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c5e2d12-94e6-433c-ae30-66db6a8c1c00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-287b7b41-5435-431a-bb95-5d1aa73ab40f' class='xr-var-data-in' type='checkbox'><label for='data-287b7b41-5435-431a-bb95-5d1aa73ab40f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[315258 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-b009a770-b4e8-4e08-8ae9-9480f04d3927' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b009a770-b4e8-4e08-8ae9-9480f04d3927' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a66cc098-28a2-4b1a-830a-a56ed32f4dd8' class='xr-var-data-in' type='checkbox'><label for='data-a66cc098-28a2-4b1a-830a-a56ed32f4dd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bc5b76a2-c23e-4eff-9840-8a574533c586' class='xr-section-summary-in' type='checkbox'  ><label for='section-bc5b76a2-c23e-4eff-9840-8a574533c586' class='xr-section-summary' >Data variables: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cdww</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d242acf-9ced-402f-b329-ea97b3bd7031' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d242acf-9ced-402f-b329-ea97b3bd7031' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f57571ae-bccc-4978-9a29-52eeb5fdb4ac' class='xr-var-data-in' type='checkbox'><label for='data-f57571ae-bccc-4978-9a29-52eeb5fdb4ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140233</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cdww</dd><dt><span>GRIB_units :</span></dt><dd>dimensionless</dd><dt><span>GRIB_name :</span></dt><dd>Coefficient of drag with waves</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cdww</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>dimensionless</dd><dt><span>long_name :</span></dt><dd>Coefficient of drag with waves</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dwi</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-56b95ac2-ad82-417e-b262-20cc13093254' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56b95ac2-ad82-417e-b262-20cc13093254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4290daba-ad32-4f6c-bf39-97a7c3b04cca' class='xr-var-data-in' type='checkbox'><label for='data-4290daba-ad32-4f6c-bf39-97a7c3b04cca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140249</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>dwi</dd><dt><span>GRIB_units :</span></dt><dd>degrees</dd><dt><span>GRIB_name :</span></dt><dd>10 metre wind direction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dwi</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>10 metre wind direction</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hmax</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42c0fec8-1fe7-41af-8a1f-7672af55726c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42c0fec8-1fe7-41af-8a1f-7672af55726c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abd21177-a004-4bf9-a123-c349a837d224' class='xr-var-data-in' type='checkbox'><label for='data-abd21177-a004-4bf9-a123-c349a837d224' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140218</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>hmax</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Envelop-maximum individual wave height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hmax</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Envelop-maximum individual wave height</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mp1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1dbc1637-de95-423e-894b-c83246ce9fbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dbc1637-de95-423e-894b-c83246ce9fbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72de101c-c18f-40b1-8a30-b7fdb18f178c' class='xr-var-data-in' type='checkbox'><label for='data-72de101c-c18f-40b1-8a30-b7fdb18f178c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140220</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mp1</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave period based on first moment</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mp1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean wave period based on first moment</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mp2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3addde0f-513d-40ce-b7e3-e6918536719e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3addde0f-513d-40ce-b7e3-e6918536719e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d10f4a1-d623-4c45-ab42-d8a2147e649e' class='xr-var-data-in' type='checkbox'><label for='data-2d10f4a1-d623-4c45-ab42-d8a2147e649e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140221</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mp2</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean zero-crossing wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mp2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean zero-crossing wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mwd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f78b9af8-c3a3-446c-afe6-9446c1679cac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f78b9af8-c3a3-446c-afe6-9446c1679cac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d6e5e3a-96b4-4fdf-ac5a-63eec1d0f8d5' class='xr-var-data-in' type='checkbox'><label for='data-5d6e5e3a-96b4-4fdf-ac5a-63eec1d0f8d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140230</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mwd</dd><dt><span>GRIB_units :</span></dt><dd>Degree true</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave direction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mwd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Degree true</dd><dt><span>long_name :</span></dt><dd>Mean wave direction</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mwp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ad7ef53-f1cd-4e39-b433-d5976e8035c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ad7ef53-f1cd-4e39-b433-d5976e8035c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0728d0b-e344-46a2-a60d-bdcce8c11829' class='xr-var-data-in' type='checkbox'><label for='data-a0728d0b-e344-46a2-a60d-bdcce8c11829' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140232</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mwp</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mwp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phioc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d0cae3f9-97c3-4b87-82c3-febe9a0783b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0cae3f9-97c3-4b87-82c3-febe9a0783b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c847303-7f93-4268-bce8-86c00d6a2f6c' class='xr-var-data-in' type='checkbox'><label for='data-4c847303-7f93-4268-bce8-86c00d6a2f6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140212</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>phioc</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Normalized energy flux into ocean</dd><dt><span>GRIB_cfVarName :</span></dt><dd>phioc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Normalized energy flux into ocean</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pp1d</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8714cf85-896b-439a-8cf7-6a8dc1cf27ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8714cf85-896b-439a-8cf7-6a8dc1cf27ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d27bc99-f450-4f40-851b-75ece611ee69' class='xr-var-data-in' type='checkbox'><label for='data-7d27bc99-f450-4f40-851b-75ece611ee69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140231</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>pp1d</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Peak wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>pp1d</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Peak wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rhoao</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f83918b-088a-439d-958f-71d4be20c9f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f83918b-088a-439d-958f-71d4be20c9f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fd31741-3556-4e85-976e-398f91139c74' class='xr-var-data-in' type='checkbox'><label for='data-1fd31741-3556-4e85-976e-398f91139c74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140209</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>rhoao</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Air density over the oceans</dd><dt><span>GRIB_cfVarName :</span></dt><dd>rhoao</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>kg m**-3</dd><dt><span>long_name :</span></dt><dd>Air density over the oceans</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swh</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fdb10a2d-7ba2-4902-bac6-7eb0eafce537' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdb10a2d-7ba2-4902-bac6-7eb0eafce537' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4b7d977-c481-4977-b43b-0127e836377a' class='xr-var-data-in' type='checkbox'><label for='data-f4b7d977-c481-4977-b43b-0127e836377a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140229</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swh</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Significant height of combined wind waves and swell</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swh</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Significant height of combined wind waves and swell</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tauoc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6ee11867-1f05-4f33-a0da-20c36b6a7356' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ee11867-1f05-4f33-a0da-20c36b6a7356' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e3c9faa-d934-4c6e-a8e3-5e7ccf5ac83f' class='xr-var-data-in' type='checkbox'><label for='data-8e3c9faa-d934-4c6e-a8e3-5e7ccf5ac83f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140214</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tauoc</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Normalized stress into ocean</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tauoc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Normalized stress into ocean</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ust</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9e7e01bb-79c9-4f88-a8af-4a43cdf5dc55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e7e01bb-79c9-4f88-a8af-4a43cdf5dc55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b65c38c-f98f-4205-bc78-d45eea6eb06d' class='xr-var-data-in' type='checkbox'><label for='data-2b65c38c-f98f-4205-bc78-d45eea6eb06d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140215</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ust</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>U-component surface stokes drift</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ust</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>U-component surface stokes drift</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vst</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c79f4a51-6b26-49f0-bac5-509a17ac2729' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c79f4a51-6b26-49f0-bac5-509a17ac2729' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-028a3f06-ee24-46fa-a7f8-5b999a702673' class='xr-var-data-in' type='checkbox'><label for='data-028a3f06-ee24-46fa-a7f8-5b999a702673' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140216</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vst</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>V-component surface stokes drift</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vst</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>V-component surface stokes drift</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wdw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8ebadb44-d4dd-4874-b0af-2e75e7e808fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ebadb44-d4dd-4874-b0af-2e75e7e808fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de32a6b3-c4be-4195-8117-247ae2144a78' class='xr-var-data-in' type='checkbox'><label for='data-de32a6b3-c4be-4195-8117-247ae2144a78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140222</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wdw</dd><dt><span>GRIB_units :</span></dt><dd>radians</dd><dt><span>GRIB_name :</span></dt><dd>Wave spectral directional width</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wdw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>radians</dd><dt><span>long_name :</span></dt><dd>Wave spectral directional width</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9723a926-ee06-4ebb-943a-1c5c99b4ea57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9723a926-ee06-4ebb-943a-1c5c99b4ea57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-881c6c65-2500-4d70-a50d-2a5155f33bbd' class='xr-var-data-in' type='checkbox'><label for='data-881c6c65-2500-4d70-a50d-2a5155f33bbd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140245</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wind</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre wind speed</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wind</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre wind speed</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wmb</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-73e0e9a2-05a6-43d9-b53d-c7ad6fbc8905' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73e0e9a2-05a6-43d9-b53d-c7ad6fbc8905' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcc43f9d-7078-46f4-9118-e36ed5ed4b52' class='xr-var-data-in' type='checkbox'><label for='data-bcc43f9d-7078-46f4-9118-e36ed5ed4b52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140219</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wmb</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Model bathymetry</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wmb</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Model bathymetry</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wstar</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32e4dbae-469b-473f-b416-ccbff9326798' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32e4dbae-469b-473f-b416-ccbff9326798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0c1dfcc-6e8f-4e1b-9afa-60a765d92541' class='xr-var-data-in' type='checkbox'><label for='data-f0c1dfcc-6e8f-4e1b-9afa-60a765d92541' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140208</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wstar</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Free convective velocity over the oceans</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wstar</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Free convective velocity over the oceans</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-01a44a1c-83d6-43bf-b801-f7d768d9e3e5' class='xr-section-summary-in' type='checkbox'  ><label for='section-01a44a1c-83d6-43bf-b801-f7d768d9e3e5' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-001f4653-f173-470a-b86b-840e919f30f6' class='xr-index-data-in' type='checkbox'/><label for='index-001f4653-f173-470a-b86b-840e919f30f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n",
-       "               &#x27;2020-07-31 00:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5a94b7b8-a8b9-4380-99d6-1fe7190bce5d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5a94b7b8-a8b9-4380-99d6-1fe7190bce5d' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n",
-       "</dd></dl></div></li></ul></div></div>"
+      "id": "af588355-687f-43b1-a487-91a5f78e074b",
+      "cell_type": "markdown",
+      "source": "The data can be visualized as demonstrated below. For example, the 2 metre temperature in degrees Celsius on **2020-07-31 at 00:00:00 UTC** is plotted.",
+      "metadata": {}
+    },
+    {
+      "id": "5638d36a-f0ca-442f-8879-36411fc61a75",
+      "cell_type": "code",
+      "source": "earthkit.plots.quickmap.plot(\n    ds_sfc[\"2t\"].sel(time=\"2020-07-31T00:00:00\"),\n    units=\"degC\", x=\"lon\", y=\"lat\",\n);",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 700x600 with 2 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 958MB\n",
-       "Dimensions:  (time: 21, value: 315258)\n",
-       "Coordinates:\n",
-       "    lat      (value) float64 3MB ...\n",
-       "    lon      (value) float64 3MB ...\n",
-       "  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\n",
-       "Dimensions without coordinates: value\n",
-       "Data variables: (12/18)\n",
-       "    cdww     (time, value) float64 53MB ...\n",
-       "    dwi      (time, value) float64 53MB ...\n",
-       "    hmax     (time, value) float64 53MB ...\n",
-       "    mp1      (time, value) float64 53MB ...\n",
-       "    mp2      (time, value) float64 53MB ...\n",
-       "    mwd      (time, value) float64 53MB ...\n",
-       "    ...       ...\n",
-       "    ust      (time, value) float64 53MB ...\n",
-       "    vst      (time, value) float64 53MB ...\n",
-       "    wdw      (time, value) float64 53MB ...\n",
-       "    wind     (time, value) float64 53MB ...\n",
-       "    wmb      (time, value) float64 53MB ...\n",
-       "    wstar    (time, value) float64 53MB ...\n",
-       "Attributes:\n",
-       "    GRIB_edition:            1\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc..."
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_wave = load_hplp_data(leveltype=\"wave\", gridtype=\"reduced_ll\")\n",
-    "ds_wave"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a936cb8e-b821-46a2-8fba-e1619e12f097",
-   "metadata": {},
-   "source": [
-    "The data can be visualized as demonstrated below. For example, the 10 metre wind speed on **2020-07-31 at 00:00:00 UTC** is plotted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "44680327-cd31-4720-b075-55d1f031489a",
-   "metadata": {},
-   "outputs": [
+      "execution_count": 12
+    },
     {
-     "data": {
-      "image/png": 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dKBTKuc+OHTv40Y9+RH9/P88++yy7du1iy5YtWCwWXnjhBeLxOC6Xa1Zf7XxMXOfE0LbP55vX/u9617vweDw89NBD/Nd//RdWq5VbbrmFD33oQ7MK64l1fr+fQCCApmn84Ac/4Ac/+MGMbcPhMKOjowB8+MMfnrF+wjLq9/vxer2Z34Vy33338aMf/Yg//uM/5q/+6q9wu908+OCDnDlzZl7lmEwmgBnv54TlcWK9yWSaEYlgYhuz2TyvshVFydpHluU5jz+dsbGxnJFhJt6FfO4Y80FVVT75yU8yNDTE97///azQa4Ve2wQjIyN8+MMfZvny5fzd3/1d1rpC6odc4SHnW6/4/X4effRR/uIv/iJjGQbDP/3OO+/M/K6pqcn8Lctyxv1i27Zt9Pb28p3vfOe8wy0WKXKlURTHRYosMl6vl7GxMVRVzWqURkdHaWhoWPTj2e12Nm7cyEc/+tEZ68rLyxdU3lVXXcWf/MmfzFg3tSGdSltbGw6Hg5dffpnnn3+ef/zHf0SWZa6++mqeeeYZJEli27ZtWfdjvky1mM/3ut7ylrfwlre8hUgkwgsvvMDXvvY1uru7+c53vpN3n6lifMJl4Y/+6I9mxOwFaGxszPjrfv7zn6exsTHvNYRCIXRdn5cF9uc//zm33nprxl0DZo4yFMKEz+zEtU0wOjqKJEmZDoHX6825DeTvnEztTEwN0Tc6Opq1T0lJyYyyJyy7+cr2eDz09vbOWD48PJx1XefDl770JQ4cOMCDDz6YFd8YCr82gFgslunATLjgTC9rrvohl3/wfOuV//7v/8ZkMs0I7VZTUzPjOz558iTHjx/n9ttvzyq7paUlay5BkSKvF4puFUWKLDJr164llUqxb9++zLKenh56e3sz7g6LycaNG+no6KCuro5Vq1Zl/pWVlWVE5ISlcsIKOxsbNmzg9OnTLFu2bEZ5+SytsiyzdetWfvSjHxGPxzORDa677jpefPFFXn311axoGAth9erVCILAM888k7V8QrTl4z3veQ/3338/YAwbv+ENb+DWW2/NuGlMEIlEsn4/9dRTyLJMc3MzLpeLZcuWcebMmax7snLlSioqKnA4HKxduxZZlunt7c3apqmpKSNe1qxZQyqV4sUXX8wcR9f1OS2fqqpmWQc1TZvhp1zIM66rq8Pr9fLSSy9lLX/llVdYuXJlRsitXbuWI0eOZN2Tl19+ObMuFytXrkSSJHbv3p11bXv27Ml679esWZNJYjG1bJPJlDcJxtatW+nt7Z0xcfE3v/kNNTU1eTsjhfLAAw/wyCOPcO+992b8uhdybYqi8PGPf5xYLMY3vvGNnBbdhdYP89kvHo/zv//7v9x+++04nc45r//UqVN84QtfmOE2dfr0aaqrq+fcv0iR1xpFy3GRIovMjh07aGpq4tOf/jR/+Zd/ic1m43vf+x4lJSU5rY7ny7vf/W4ee+wxPvCBD/Dud7+b0tJSXnrpJf7nf/6H//7v/6a+vp7S0lLMZjNPPvkkLpeLXbt25S3vrrvu4k//9E/50Ic+xJ133ondbufxxx/n2Wef5Re/+MUM39yp1/30009z7bXXZhIn7Nixg1QqRSQSOW9xvHTpUm644QbuvfdeEokELS0t/O53v+PFF1+ctQFfv349P/zhD7FYLKxatYq+vj4eeeSRGW4o//M//4Pb7Wbt2rXs3buXn/3sZ7z1rW/NTMr68Ic/zEc/+lHuvvtu3vzmNyOKIg899BBdXV389Kc/xefz8a53vYsHH3yQaDTK1q1bCQaDfO9732PNmjV85jOfYefOnaxatYp77rmHv/iLv6Cqqopf/OIXdHR0ZIXoms6uXbv41a9+xbJly6ioqOCnP/0pR48ezbIe53rGuSaQvfOd7+Q73/kOPp+PTZs28cILL7Bv375MwgeA3/u93+NnP/sZH/nIR3jXu95FMBjkG9/4BldddRVNTU2AIdDPnj1LQ0MDsizj8Xi47bbb+MEPfoDFYmHZsmX8+te/ZmBggM997nOZsv/wD/+Qu+66i7vvvpu3v/3tnDt3jh/96EezCrk3v/nN/OxnP+Nv//Zv+fM//3PKy8t55JFH2LdvH1/+8pfz3rdC+MUvfsEDDzzAn/7pn6JpGgcOHMha39bWVvC1fe5zn2Pv3r18+tOfpqurK8sX2ul00tLSsuD6YT77/eIXvyASifDOd76zoHtw44038uCDD/KpT32K97///ZSWlvL888+ze/duPv3pTxdURpEiryWK4rhIkUVGlmW++c1v8vWvf52vfOUraJrGunXr+NznPleQFWe+VFRU8P3vf59vfOMbfPnLX0ZRFFasWMG9996bmeBmNpv5yEc+wgMPPMCxY8dmTTHb2trKd7/7Xb71rW9xzz33IIoi69at47777ssrjIGM+J06+chut7N161aGhoZmDeFWKJ/+9Kf56le/ygMPPIAgCFxzzTXccccdWRa96XzkIx/B7Xbz85//nPvvv5/S0lJuueUW/uIv/iJru9tvv51Tp07x7//+7wiCwFve8hY+9rGPZV3fN7/5Te6//34+9alPYTab2bJlC9/4xjcyIvRDH/oQZWVl/PSnP+UnP/kJJSUl3Hzzzdx1112Zcr7+9a/zxS9+ka997WtYrVZuvfVWrrnmmhmW66ncfffdiKLIt7/9bXRd58Ybb+SDH/wgjzzyCH6/n7KyspzPOFd0g/e+971omsYvfvELfvKTn1BdXc0nPvGJLIHV0NDAN77xDb7xjW/wqU99CqvVyq5du7Lux9GjR/nwhz+cte/HP/5xbDYbP/nJTwiHwzQ2NvKlL30pK3rGxo0b+eIXv8gDDzzA3Xffjcfj4e1vf3tOX+0JzGYz9913H9/61rf413/9V+LxOMuWLeNf/uVfuPrqq/PuVwgT6Y/z+ZNPuBUUcm0Tk9v+8R//cUY5Gzdu5IEHHlhw/VDofqqq8pOf/IRdu3YVnIHSbrfz/e9/n/vuu49//dd/JRwO09TUxFe/+tUZE2CLFHk9IOiFjLMWKVKkyGuYzZs38/73v58///M/v9SnUqRIkSJFLjFFn+MiRYoUKVKkSJEiRcYpiuMiRYoUKVKkSJEiRcYpulUUKVKkSJEiRYoUKTJO0XJcpEiRIkWKFClSpMg4RXFcpEiRIkWKFClSpMg4RXFcpEiRIkWKFClSpMg4V3Sc4+d+fII1b6681KdRpEiRIhedw4/0XepTKFKkSJFLgqvEzoY3Lrlg5V+x4jgWi9ErHGLgSeFSn0qRIkWKXHxMl/oEihQpUuTS0DcKy2OV2O32C1L+FSuOk8kkklngsW/HGOtVL/XpFJmD7Xe42f1Q6FKfxmVPg2YD4JwYv2TnMPGsJs6lSDb/+OripwBfCP/9/3uGk/936d6TIoWz/O224rO6QliMZ3Up6+/XAyW1Em/4kJ1kMlkUx/kY61UZPqtd6tMoMgfJiFB8TgUwTPRSn0LmWU0/l1bVAcApae5znNj2tcjdvkfnvc93tT9c9PNQIiLhzkUvtsgFoPisrhzm86wKqQuLXJlc8eK4SJEiF4f5NAS5tr3SBXOzZX4h4duTQma/L9r+M2vd38f/6LzPxyuddxFFLiKnpOgV/w28FpirHmsSzEXRW+S1K44PyP6cy9uUsnnvN9c+RYoUmZsrweI8XwG80LIWQywHit5kF4WFvLfT92kSzLOWNdt7n6+jOZvYnu9xCunMTj3exPZTt8l3PucjNCeus0iRA7I/o8VOSgHAdUGPd8WL45NSgHNyijalLHPz8gljmBS/UwXvbNsXsr4Q5jqvxeZCCvqpL+nlxHwq0tlcBKaXMx93gsuF6e9avuc1dbvMNyRaZ2xTSKdyMd6J2e7xhRTOiymKF8JUsbwYVuXXE5fDd3m+5zDf/Se2X+h+C912+rK5fp8vl8OzLXJ+FNoW5do23zYNmM/7vObiihfHEyxUeF4swXoxhXGhx5utgzCXmLocBXKhFp5cwjffdlea5SLfc5/+vHJtV0inspBjF9IRXMi7cyFcNS61KM7FF23/+boVyFfSt1akSJG5md4e5DJQ5tt2+vKLqaNeM+J4KhM3/Xwb+9c6i21hvxKYr8Xlcm6sCxG807efT9nnc16LWf5sQnr687nUbhmLxZ+JP7kgk/guBdOH2y/nb6pIkSKLT652qhAD29T1F3v0/YoXx8tVL6WKlnXDwbiR02/89CHkfOsuFrOJ+FwvwtTt5/OizCWgFlsAX45W5dcSuXrhr2Vmu8bp71mhYvlytBhP50oXyFOfxZUmiOeaszKb9Wu2+m+2/YpcWcw2qljsDOZnPoI437py9cInd77ixfEEhVQ2U/2S51PWbKJ6MchX5lzLF3IuhV57rvtU6PEKvc9FipwvcwnnfK4YUyNJXM5cCQL5UjT+00XmpXKPm82NCWYaHi629avIhSOX210hI1dF4Xxl8JoRx4UyXyE617rFIt9s50v1IZ3vNReF8YXh9dWwTghXAbM5jdmiYHck0TUBsyWNMJ4cU5JVtHFLgsmsIArGfmH6QQdNF1AVkXRaoj5aRkc6RGPKiZA2cSZlRtDFy14kX2ouRt2zkHf7cv8eMiJ5fJLrXEPK+SzL83WhulS8Huv9uQTxlTihu8jrUBzPl0vlw5jruBfiXHKF5ykykwvSSRE1RDmNpWYYLWVCT8uGHhTAvqQHTZHQ4lbQQddEtLSJ7SkZc+UIIyQBgarqMRIJE6GgnXRaIhE3o+sCSloCwRCL4aCNWMyCLKtomoiqiCiKiKJc/M/f7YmxfFUPqiLicicQRQ1R0jCZNHQdkgkT4ZAN2aTi8UbRdYFY1ILLnciUMTbqIJWS0VQRXQdVFZEkI8FMKiWjawJLWobynEEXwIxUK4cGKpEHKmmIejAlrIiqiKiYkNTsezRhcS6EQgX31DJz7XOhrccX6ru/XAXc5cJ858RcCfdzvud4JYrphbbDs+2Xq31Z6HGK7fjiUBTH41wI4TnR0M2nQb3YTL3u+cbavJI5n+c9Z6xQSUWyJpHdYSR7AjVuQU+bSAfcmEoDlO7Yn7fs1Kgb0ZJGdkymH1XjFhJ9lcieMLa6QQDswMnjNZjMKiazyrmz5VgsaeyOJA1NMxuoVFJG00EUdcxmI0BuIm5isN+LqglEI1Z6u31ArndVR5I0BAFkk4rVmkKSNOIxM62RajpLe4iEraiqhK8sRHlFiHRaQlFFlLRMLGpGUUWuuvok4iyuYoIAVlsaqy1Nz7lS9r3SjCDo2J1JvN4oFZVBDh9oJB635C9kHJsjRVV1gFRKIh6zkE4bGTNEUUcYtyw7dBOgoyUseAKlpJd0ctoVATFboFoOrMNypnnOY04nn+vGbPVBvnWLIZAvxDd8JQi2Ipcv85lPcLmwmMlcLoQRbLG1zGut7S+UK14cN2g2SgoIhn+hshM1W/Q5LT/5LEjtSWFBw7mXQmxfaSHNpp7vYj53zRpHLR1Dc0RB1NBlBc0TQrPFqQHiaQlLxWjWPslBH4Ipjbk0lLfc6JkGYp31qJHxcxU0TCVBrDVDRM80oiWMYdln9vuRJOOFV1WJzjNVM8rq6qjAUxIlHjMTj1lIJmUEARzOBIIAFkuaZSv6cHviNC4dzuxnNiuYzCoVlS5uvX1fZrmuG8J1wkobj5lRVRGnM4FsOkYVoKsCStiJyRvOOpdEbwXSkhFkZwwdAV3XM+4QEzz/u5XEY2ZEUUeWVURRJxq1MCHUU6MmAqNOznZUzvZosjjw6tLJ67KkueGWw7Nun6zNZ2mG1IoTyIPlSGE3w9c+jaU8wNhLG0gOlI+/ZxFkbwjJHqdGt4AugCYgpM0ISQuKbkZOmy9ZJ3mxvtuiEDbIN1l66rKKqgDekggmk8qZU9VZ20qSit2RRFVFVEUimZTJ3Sl9fXM5T16cLazkay0T4kJHTee6B+cT096lFXwaC+aKF8eFcqFe1vNp8BYqjKful0tg59pmMZhvNqZLxcR5nu8zV0vGSKw+BoKOPFCJELWT2PHKrPukjjeTGi7F3tSD5EgQ76kidHAFokkBdGRPGCXoRrSkMJUGMPsCWKqH0BUZQVbwbD6EtcYQasOP7SJ8ZHmm7InGQlVnyxus09zaT1lFKGMhns7oiJNI2EIoaCORMDE24mT9prPU1I0yOOCdsf2EmH36ibWkkqbM8rLyIJu3txM6uIL4uRpM3iCmkhCyJ4y5bIzY2TqiJyYsrjqiNYlkTyB7wkTLB3C7Y/R2+4iEbePXBen04ldJ6ZTMqRPVyJJGMmkyrNqKBDoIoo4o6jRhx1QaxFLpR0uZcHUY4jpY24OlYpSxTXtB1LCMd3Bca09ga+xlUJEorxhBVyXUiJ0xXcCpS0SlNKI5jckbZkKWB/etIt5Vl/McF6t+mu+3OJflbnr0n9lCPF4JPrGFkDWhWNCQnTH2JuKsT5fP3FYrQbQkca07wg2OOCbf2IzOn6OnkQrNRptegoBOes1B6hpGMuv37G5GVSRiUQuplCm7/AUKwwt//3VKfRFEUUPTRIIBe1a9JAg6JpOCxxszXLtCNsIhGwvpBFzOInkq010UC8keeKWRTygv5FoLmWd1KXndiOOLxcWwDk0/Rq5jXkwr1eXmszzXxzlbxaVUDhLb+dKMofWoKiDWpZCTJnr6PZRLMrb6gRllu1a2Z/221Q1gq5vcLj3mItFXSXLIhxJyoqsSojmFc0UHzhUdWfsK5jQkDReC2Ru7yYlrJpNKTd1Yzq1SSQlRMeF1xxF9hnAOBW00NI6QTJjY+3ILiYSZoUEPjz68EdCx2tLU1I7i9kYnDzOOf9jDwEO3TJbv95Hy+/Kco4CWsKIlrKRHvRzqts1yPYuLrgt0nK6edZt+gF4rbftXI9ljmN7wPAATDhxqzEair5IxfwmyK8JZ6ygtshVBVol3V6OrIu2mAMstFqwbj2IBFEVES8mkR72MqiqnA9Ca5/gLidccENIL/uYKTdKSK2JNIQmDCsmoeDHIF3rtgDxMVc0YS0QbgqgjOeII1f3YLGlSYx62n63DUnUIkzeI7IpzdcTCqeM1VPcsGy9LZ+uOU5T65r7/9qXnsLeYqXrLbgDSQSeJgVKsVcYI05btk3XG8OM7UaPnLw5y3X9rfR/ezUcAGBrwsG/P/F2FJthkcVA+xT2s80wF/X2l7LjmBGCMMmnj7lqJuJnWlX0ACOicPllDz7n5C91LKZLnI/7m6ypxpQrn2RJozZfLTRhDURwviNdqr/B8uZxC1Mx2fFNpgHO2AbSUidRwKSBwSorgWnMKx7KuGdsPPHQzIHBA9uP2xKitH8FSO1MYA6hxM5Itlf/YJWFMJWFcq89kLVciNnRVQhA1dEVCjdnRZvWt1WlcMozbG6O2zmhkg0EbDkeSEb+TwJgDXYeWVsNHOZWSiMUs2FI2jvY6CQXtWCwKoyOz5acXSMTNdORw3YCZjdR8XG8u15BWB2Q/FjHF1jMNJPsrSAdd6GnDmic5ogSbT2IyK3g1AT8RKtIOSncagsAHDPR7SQ54GOj34h9yk0plD5kX2sBfyDplIclgJqyokjOGkNBB1Ci9eg9mXzCzrRK1kRryMaCk8UVKcbSeRXbESQ6VoqdlNEVGS5rR4hau0kTUmI1XRsdnoOagsnqMquoANnsSuyPJ0YMNDA6ULPh6pwplmy1F26azObe3lAewlAeyljmcSWrqRihZ182tFiWzXI2bEc1p4xpEbYbVGMBa7QdaMr9NngjpYBW6KqAlLShRG0rQjRJ2oEbt876+QlGjNpJDpWgpE+a+yvPqwOxNxmC8A11eEWDTts7MJNiRYScnj9dhd8ZxORPIJpVUykVdg1FP1daNMDLsQpQ04jELmja/mLWLHSZ0+nfm1U3zFr+Lefz58FrUHrNZpKcvu5Bc8eL4nBhnWLoIDijTWOyc8lM/+HwxPC9kj/lCfvCXWihnIar4rp10jRh7qY3kQAUIek5h/MIzK4iYhrFYFMSUTChoJxS0c/xIPYKoUeqL0LapE5PJsMRKthS6BoIImgbBgB25vwbRnMa2pBtRzn5XdVVk5HfbUEKTIrVVdaDZo8QrBlECLjRFZtd1R3E4kplt+vu8VNcEsso6fbyGYNBBOmV81qW+MNGaAJomIJtUvCUxIEa1rtDf6yMamTl0DhhDwPNIHDNxzlP/n85l9Q7MQTJh5rnjZbQppVnLPdftpjyHq0pXZxmKInGus4Jk0jRjfS4K+aan37NrTN/l2fSfFVT+bEzvmIiixvarT6KqInZHEssU8Xdt3IgeMjTgp3HpEC5XgjfkKVd2xJGX9CB1lKM1nsMfskDITDol4Akaola0pHCunZyY6XluOcFA7ndmzfouTKbJ78U85bzme70zfodh4KFa45xscVxrT2KtGTJ86xURTZXQUyZkV2zy+F1LwNyFShRRVgkeWElquATPxmOYp7hTpINO0AWUmBVbjeHTnxwsw//kVagJa6azFdqb/5zPtz5ONZwj3dCDGHFg6q5DHvFxahTGXthccBmFtDeCOUXltuwJxr7yCFeVjVuQVQldFY37EbUiOxKU+KJce9NR1LgFyZYkFjPx4jMrx6Pn6JhM6vgE2vyjn3MlyHq98Fow0s2WLChXu3FKilIuiuxgNsPO+XPFi+NLzdRGrhARkY/paRIXUkY+cp3jXJnF4PIY4phPZedceSbLNSGZkIlErIRDdhIJE9GwFf+wm+OPr2WdxYbsjvBqQEcdvyemx9bS3DJIU/Pk5Kyd156YcZzOMxWcPF6HromMDLt56tH1uD1RVlkteLceMiZkoSOKUFIag1LDSjy2u43ahIuR5tOYSoPIjjiCpMF49ARzhR/nynZiCRtKzQD5bGSRiIVQwBDpmirS212aaVgcjiRl5SHMZgWzJc3wkAddB6fLCIX2youtxGOWWd+3C0GuztLlaj2eIPvcdMoPNLJpq/F+nT5ZRSJmYXTEWVD0jAvBbBauuVI2T733mgZuT5xc2Gxp2k9V09A0jMuVIDXiYWz3BhzX7M68U9OZOskTjCF8vaF7fEKoTjhko6/Hx9iog1DQhrc0gsOeJJE0hPiEX/tTj7bNeQ9my0RX6HutxW0EX2kjmGuloCGYFFoUB660CaFncqKnZ+KPZ6/NW7YuGp0p1/vdLA2YLpqAUX2jqFWDqIBWMob89HXzPm4hHVo9ZWb48asxlQSRXVFkTwhrtR9BMDoJ8XM1oInGiEFaRk+bEGSFdNBF5Zt/B4AFEU9JlJWre7Peqb6eEoIBB/4hN7GYBV0vTCxPT7ZSyCjNVNq49O3eQpjP852ewGQ2kT19myvJ0HE+FMVxHnJZcufafj7LL0Xvdq60w9N73wvxg8xFq+rAqxdmUQM4ZB5iW3WaSHk/bZKG23MEmz1FNGohHrNw8lgNkfDM4cf6VJrVU35brAqqlsRXFqHnnI8Vq3oz62KddYQOrOLmOx4HIJmUSSZMRMJWTh2vRtVElJTE2g3nso5x6ngNnR0VuNwxWlr76WyvxO2JsWptDwC6JmTErqYKiNKko27J9gPEABsQ3L+S+Nn6rLJNnjDm0iBK7maaWFcNoX1rOGgeoqZ2FJc7gdWWpKwihMORwGJNI0k6mibQ211KOiWTSknU6DbU3jJCPVUsX0Bs49eTJSYXS5f107qiHwD/sJNE3MLZjgrURYgTPR8RN5/vb/q2uSa/TBzX5Jvpo/7042tIJk1U146xfuPZzHKzL0jFrc/y1BNrUNISbk+MluX9VFRORmLRNOjqLKe324fDmUQUddJpiXDQjqYLaCrc/KZDpFMSx4/WsW7D5IhNb08ph/c3FXyd+Tjfdzbrfi3MYA2AoGVPnl1IHTo1JGjBk7iPrTf+TTCtjELmpBQSbaBVdUDIwanp7iCiiqP5HLIriiArCLKKZEkhu8MIkm5Yk8dRExa2bG8nMVDGyP41+K55FQB3uISKpgFWrjHq1qceW4MgCCiKlEn6k4+52roJcr0nuXz5z6dDM9vzu1QRbGYb6Zur7ni9UBTHechlWVtMK9fFnFwwH9+y6Rmcpp7nfNNkTidfz/OA7MfljrFidQ/ptMTyuAnPUv+kZWYcpzOJ05lkeNA1QxwLgsZgvxdZVlm+qi+z3G5PA1BbP4KiiAz2e3B747iW9CBIKqMjTkp9ESwWBbNJxWlVEMcntO3bs4Tnn15BfZOfxiXGfWhd2UdT82AmEkRl9aSQ1RQxy21iqjAefuxqREsKc8Uou9pX05EUZ9yL6OklKCEnVY40w6Rxrz+ZWTc44OHYqVKSsp+Vq3ppXDKMlpJJDpahhmwMRSJ0tleyMlINCByd8twiczyXxaLQRvdKwumK07qin3DIysnjtdgdCVat6WVowE0iYSYeN5NO5e74zZaGvhByfWOLlRBkatn6kJ2efVE8G48BkBpxc90NxxDk3O5qgqSx0WniYDzJilW9eEqinDpeTetKowMhiuAri7Ck2bAgj4446GyvxGJNEQoax33lpRYkSWN40Etfj+H3Px8upM/phWah2RgXI4vjRBmFhBedGqc713c9a4x8FTixZsY+OjpIKrqkcq6sn9Id+zGXhhCDLmzuKLYtRyBpQpdVnCs70BQRNWYhPeZha30c16rJyYv+p7ehBKa3EufhUz8L5yOWp967fPXjXM92eqdmtu0vdB18pYV1XShFcTwP5lshL8TifCmsdPk6ArlEc6FD4TMrDx2lfBjNHqPRpCCoIolNB1CjVq4LOUn2l+MpM2RcIj75WkbCViyWNKZxMdrbXcK5szPj3S5tGWTZin40DY4crKevpxST2ciytnFLp5HAQtawWA2xHI+Z6U+pnDrYSllFkM3b2o3IEZZ0psyNWzozfz/26zbQBTbZzXh2GT7L6aCT8LEW1JATe8s5HM3ZVuaplI9HPwAYTboIbdiPLeihsbMRQRMYFNKgg3v9cYJDZYTSGqPtFSRTMt1dZShpOfNsjpxQqbJpmMrHMJcGkOoHcAJNS4dJjZ7DXBri1mnHdz10O8J4trcLWbHlqrRnq6wvd9eKJc3GhEaXO8HmbUbDHI1Y8JRE2bisk5PHauhsnzlhsdAIDvmufbEEWyGWKgGB+o7l0LEcXVI4TQJEDffak9iX9uQs1+QNsWF1L6Ko89Jzy4mE7XScqc7Ex57qH1zqi1LqM9xRDIu0mVG/O+sMZmOx6sTFFsHzEateaXHE7YVm6jnm+zttThLzjuEaKaMzVtiIoC6naZYkwyigygiqTH24lMRgOdJoCWLYhRhxoHoDqGWjaN4AmjVpTHaUkwimEay1QwQPtCLaUsiOOFostztToSEFz+e9WkzLMhQmaKd3VuZb107dt8jcFMXxBWQ+jWGh6xdyzMVkqmV5tuOdkowkGWbfGNbaQZLNDQQ3HMSasKFW+BHGk11IjgSSI0HKX0qssw5z+SjaqIfgqAeTO0LIHCWZcKMoEtGIlXDISkPTEKmUzEBfCRMNa8eZSmSTypLmIdas7yYRN+Ef9jA0UMKjDxsWZZc7ga88yGC/l1jUitmsYLMl8Q95+N2Ta7juJiPMkZY0oSkSetqErgkMhE1sEN0clUc5Y46wdNRJeXkYkyeCrb4XkyeaNXHHcngVcl815lWnGKvpBUlDidgIH2lFCTvh5hcAiHuC0HYIIMtKHtq3CjllQQas09YBrElUokTGsFSNIKbNaEz66U1NNKLGrJhSZsySTrMs0jE+lyxXRX6hBfPUCvnKsDzo1NaPMjzkZmzUSUPTcMYv1+FM0rxsiONHaunqrDivo+T6fi6kJXM2iyGAoMq04gQV9IMbiLnjqGUjCDEb5vYlyANVjKw5jGvNaYL7VxHvq6AlZc7UB8EDKxi2G5NAW1f0kxgooztoor+nhFhsMrHLXJxPPbaY9+9iClodnYQrRLCqn7grTNoWR1QlkgEX5pOtLNOtF+1cZmOstofeNYdpfe4amlVj0mo+wRXfuJ/00rMAHD+6EtO5eoSoHQEBKezG8dzVAKSWdBK74VmkkVLkgUrMHUsQYzYitzwFsoo4PuHZ03YKMJIOJYd8aHErgqShawKCqIMOatyKGreALtKWzi+U87o8itaCQx1OkG/OzlR/3cWKtjBxr2cTuTo6Sn0Pim8U64F1CMy9T6Fc/nX34lAUxxeZC+GiMZXzddc43562aItTctV+9LSM7jLcFbo6y1GCTlL+UkxLuwFICypiSkY0GxYm99pTmTIEOY0SchLvruZQSAEEZJNC2+YOvN4YwTE7vvIIJ6xpznZUZKzCJ4/VcvJYLW+4bT+btxuWqsCYncEBL8tXTrpaTIQ3A8NHWFclwoeXTV6DJY0SdqI5IlhtaRpKgcZnKUmLWbPnAWx12ZOP0ATSK04h1w4gBT2UdzUxvLQD2azgaD2LudRww7AlLSTMSVb2NDMWsdC/8limiCWSCWmuCvTAJjiwiUZnkr6VR0lbEoSqJq9LTlhY++wNBCsHkBSZsdoeKhJWEq4wwyjolpSRxS1pRUiZWJK0IIVcCDF7ZqLXfCrBuUTEbBaLy9F6bLWlWNs26Qur60bkkcCYncYlflJJiYG+me4AU7+R+aZjn2+juZjWyNxWKJl1L+3iTEpHLfeTru8htusFrNYkthe34e6ryZpcCRDvBCcNAAwcXwuAa/zfYnGhMp1eSFQ5TbBigJh3DEEXGYuZ0SUN3RZHN6cwuyMknGF0QaekrxZzwoaoiURLRkmVD1OeskNHy9wHmsb5iqFc96Wis5mKzuZM+VO30SUFzZYYd51QEKI2iJshZUK1R0je9BTWw2tQSwKklxjflxqzINmNSDyS34fl+IpMea5fvRFdVkDQ0ZxRYtcaI3CCpONadQZzxAmaRFxMZybsyc4Yks0oLznoI7hvNVrCuqht73yjzcwWkWEqgqwge0IIskrKXwrjiVVEawLBpKCGHYAw5zegOSPEtxm+25bjKxCS2VZ2xTeCGLMhxgsLHfh6EcRTec2J44vRyF4I94rFZuoxCznfhU4cnOqb3KaUYSkfxeTJ9nKtrQhjKgnhWNqdSUUsO3LPdA8fbUF2Rwh7Rqhdc5pbgfZTVQwNutFVQwQ7XAliMTMrVveyYvXkJDslLbJn9zJ6e0qpqzdianpLYuMhzGZy+EAD5V3LqHrrU9iX9DD28nokewzJlkCqHcBqS9Pf50XXBNyeGBbNRLi7Gtea01nlyH1ViEEXCIZgCY14KbGliHnHUE1pXIMV6KJGImVCHzSDLpACJG+QDvcwuI3GRRoqY80rO+hMSAX7l5E203BoAwBxZ4ixDQeJm5PELHEO3/qbzGYVAR9DqFiiDhyqRGR8SFJzhdAtKXRLEtUdBkklXX8N8bYubGEb8a4a0OcXh/RKZyLU16MPWyj1hamsDgA6DU1+XK44sXNVmH0BVkbzJxaZq/Gar8C7FEPyE8dsMQsQLIdgOWfGw49NWKLyZblaTC6lO0ShNFYZadO7Blzo6IzWdRPzjjHmDiDa41jrNtKx+RXkwQrEqAO1ZgTdHkOXNBBVdAEkxYQu6IzUn0NOmbGFPNRFfEgHNmEfmz2+80JFsI6Obk4hpnK7KOQqV0cHWUF1h9F9cY7LKro1gW5JklqeHb9diFnBlgJbCsVjvBvJVScQp/gKm2MOrC/sRHOHkEaykwgJiglBMVw3xLgd5y/fhG5JImgiQtyWmfDoYOq7p+No7cC1uh1L5QgVb3yWkWe2kh71LuAOzc75te06u1aNkB7xkuiroPKtT2aifKALyK4oKX8J6GAuHzMiG40zPFCGlpZRQi7UmJX6c0vRrQnQJMSkBSniwv2zO/IeObXiJGLEgfXg+rzbTOXKGO1bXF5z4vhiMF14Xm5Wr1xMj75RaC96rh7y9Exa8XO1JAfL0BUJeTw9shJykVxjVHqCAMnBUiyVozPKSg6XZJJjCD0lBAN2PN4Yza0DNLcOEBg1erlWq8Lxo7U4nUlCQRt1DSN0d5Wxam03LlcsI4yHh1zY7CmczuSMY4ExPB5sPU4VRnKOkm0HAVCTMmrUTlLQqa4JMPbiBtKijnP7AYKvlvFCp2EHq2vws2b9OZSaARRPEEVUiKkm5GWn6QHkpBn7WCmJsBMp7EQyKQhJMxWiSMTnJxXWiZQblmeptxr7nk10KkZlP6+Z6ePYIm5sz+2isSpM14ALTVRRTWnkpCUjZnIxtQHULAnkJhtInXg2HMNaPURw/yq0RP4h3VbVAbKeEQgTdA1c2DiUF4qJ72OzzUKkqgezJY3TmQBNRJQ0BFlFdiSouuNxQoeWE2tvzOybS8gt1M/vcvRRbRnXUO25P6lFjW9+ofyuLwQHxCDDSzqIbhmFlAnMaco6l9B0egWmuA3PCi+WI6sw1Q6Q8HWjyUZH1RJyYY7bMMftmGN2mmxgT9ro67swYngqmjlJ5C1GB3rqnISp6KJKumqA1IrTyH4fmjNCqsyPaFYQww6EmANBkRCSFoSEBfOpFlBFpJAbubsOAQGlfBghLYOgI4ZdoMgICOhymuTy01iOL0fQJKTgzMl10xFTFsgj5CcFnED01FLMvgCWKiNNtxqbrL/Od6LsYuEtieJoNuJRj5SUIwhk1SeSK4KlfBRzxQiBPeswl43haDGs7aIlCaKGa7VhqOlKmii9et+MY8jdtaALiAkLQsyO+UwzAgL2F3bM+3xfbwL5NSeOF9PnNtdkuUL9lqZvP9W6Ot9A5ef78U73E57+90LOKQtBQ7SkEM3pTNge0ZxGkFSO+E2EQgIro9J4SmJoU3zYm88h2RKIlhSmkiDhtEBJ+WRIqekpkGMxMw5JzFieV67uRYnaqKweQ9NEqmrGOHygkUTcCMeWSsn0dvvGh74nWdt2ltr6UcIhK76yEI5x4RxtrzMSddQPIkoaqg5KbzXmpl5sS3oI7l0DOni3H+BWR5yxUQcD/V7iXTXYGvvQTCmOHWxEEjWc7jjplEyNbmdUF6iMuUmuPIHmjmA5tBop6cA5UoZiTmbEsckdyVhIJpjeABbayE+IUlGTEJPSHFtnl9uetCLE7Fj3bkDuqybZfIaym15AS5mInmgmfq62oHMAMiJ96nEmrml6RXs5djJNviBNS4dmLNenRKewLz1HrL2BXEOd0yc0FSJoLkdBnIuJ69EsCZTKIcOXdFoHbL5uJYXuO9d5XQymPkvVHSK6yRjCdoyWwt71tKScREpHCJcPES0dxVvdSnL9Ear2bsYe9GCJOnN2WAPj/3IdZ7ERUmasr2xCTFqyhHG6uh9BkUHUiO16MbNckdOYT7ZiPbgu4zdcCPJwee7jKyasR1ed30WMo6OjlYxhLxvE5BvD7AsgmtOkRt1ETi6dtXM/vd2bngRpOoXWU3PVaYExB888tZpkwoTdkcBXFsax8jRafTfxmIWSyiCiqCMIOuZK/3i2TQPRkkZLm4h11hE5sRRdlYicXIK1dhDJlshYmZX63qxjdnVWn1en8/UkkF9z4ngxKXTGeaFlTLeyzreMCzlZ73w6Fd6th7DWDKGlTKgxK7oqoaVMjOlp1nni2GxJmpZaufX2fUSjFmRJJa1IiKOlJPoqCe5bDbrIkDWBd8thZHeYlCqgqiLJpIloxIp7tJzoUClayoI+Hl925v0wGsbnf5e7whUEnfZTVYSCdirDhguIqoQQXVFkZ4yxFzcRbQ9QunMfOoJh9Y7YES1J3OtOED6xlKh3BEtCJJ4w4XLFGVJFLP0eHM4kjU1DWKxpnK4ko34nVqLomsiYOoYecOEKerD5AgxbBhEVmRJRpGmoFsuJZdiCHjpynvUkUxvK8xEBUy280627E+UKCHQOuWFoI54th7DVDeDZdNSYBDNLQ5PrWPksyJdPRatjMiszQrI5W3M/EUHWGHjolszvfNbi2ZYttONzIZhu8Z9gLst/s0XnWEMPyfWHkQcqEVKWGWHA8jGbhfl8YgFfSKZek46O5gkxUNOdsd6Vdyyl9uha4p4gfc1HGV7agSXsxD1USUlfLW0PvzWvoLwUEQQEBMznGkg1dhF+46Pojji2564ivnM3Ul8l5rNNWds7nrxhRtzmS8mEIE7XDJBaaYS9dE/bxlwaQrKkFu2Y5rJRNoTc6ClzQdvPNim/1Bdh647T9Pd6ObR/CbufX4HZnMbljmN3JDnbUc6ati5criR93V76en0g6ATGnKxNzIzYFDm2jMgxY+6MaEkiTohkTUCN2dCSs59zoQlqJurt13pSkKI4vsxYDAF8sVNoho8uw1ozhBJyMvrclszyg+PXIskqK1pdRI4vJTXqJRa1I5rTmHxjeDYeQTQrRE83kBwqI7h/FWrEkec+xI1/MoBOiS+CkpbYvP00Fosxm1lVBcZGnex9uQVdFzL3o6Q0zLadU32Fs0NUWSpHsTX2kuirRDSpWHxB0kEnsfZGtLSMZE0SKhnCl7Zjq+6nBHjx2RUsHanHUuXH1NCLo2IUUdRQ4xZKyyb9rs0nW9AbepDSJkL2OKBjSlg5Z4+hizqCc9SwCownD1GjdlJDpcR7qjOpZqdXWgX5IxdALleIzESvpHHMrp4qtKQZR/M5Kt74LMNP7kANOxd0vMtlYp7DGae+0c/woAdfeYilLZMW4sCoA/2URry3Cmfr2cxyTZEY+e1VWUO0s1mLZ+NihFSa6gebq5OSTxTPtn56GavONcO5ZmNeYp7QXzC7+Dtf94kLLYxznXuquZ3khsO4AKm/guqhaiwxO4MtpzOTa5e+vB3PkBHmz3SVDWE84vhczz2f2JAcUWz1/QiySnLIR2rIx4zoH4KG79qXMVvSIOo4nrweMWHL2kRHJ7FpP5ozgi4r6OYUujkNmkBiy15QJNTKYeI1g1n7pZadwXJy+aznfiHRBQ21JIBS049aNoJaMgZS7ljcU/FsPIatvh8EHckeBwT08ax9elpC9kQQrUn8j1+NGss9Qc1ILz6AaEuipUwoQRdoAkrUgSCqaEkLqVEPaX8Jeg73lKlMtM2HAhqJvgoqfAFjtFKRSKVMjPhNjPjBWxLJpHKvbRijujaAIOoIAkROLskI4VxoSQtaMrf7yWxidj4COdffhR4nX1kL2f9C8boSxwuthC/EQ1qoCChE9F5MYSxICuU3v4CWkgnsWZtzm7WJSkIhK5FTSzGVBLHW92GtGUJ2xkDQ0HWwLenB5AtgLg0ROtzKtZ4wg4pKNGIhMObAVx6mvmGYnm4fQ/0l1NSN0tw6gKKISJJRhqKIxKJmHM4EhhVZoKviLDcsH0ZyZE/KC7yyluRQGb7rdyM7jBBdnrZjpPxeAEae3YIWt2DyBUAVSY+5sQVd2K7ZA4Djyet5Q8BDfOurJMuHsZ9qxXSwDSFuw7LxEKP13WiyUbGllp/BMVqKfnAN9qgddAG1Yhh966vomkAyYiPlL0WyJhDtCWR3GGvNEO62EwReWUeityr/O5icfKcXQyQ0VoVxOsyGMJoQQj1LONVfQWmghOSmg5Tf9CIjz24iPeLLfFMTQqqmxnAT6evLPZR6eaCz6/rjgBEbejre0ihsP5D5fe5sGZom4OxsQZ2SCWwxJ4rlEkzn+zynCtlcVuDZLMP5hPNcgjpfubNZzc+Hiy2MNUeU6A2/M6K9pCVjsqpvjJ5qo3NlC3ho2L+R0p5sN5OAOrOswtsVnciqwzQ0DWMeNwI4lnURPtZMathHetSD7AlTdsNuAFKjbhJhO+byMTotYdS0hmBK49l0BNkZZWx3G+VLukj0lxM93gqa4a4GoCuyEWlIF3CtPJOJc21/+hrkaZPlLhY6OslVJ0itOjFjnRh0IYZdCIqMGHJj6qlBiNnRLUkQdCK3PQpAOugiNeRDiRkdBUE0XAEFWcXZ2oFkS+K7fjeiWcHasIv6tb0gqiDqpJuM+PWpEQ/+3+5AssexNfQju8NYykeI91QhWZOU7tgPGOEM450Nc17XumQFgZeNUJC5Wk6nL4DZrKKpAlrMBpKKKKsIZgXn8k6iJ5vGhfjidrIXK8X5+ZYx2/6uuftE581rVhzni+V6PpndppZRSMV2PgJ4tnTU5+O/vJhI9jglVxmTAEaf38w+JQJyZIYriWiLU+0w03vzc+P+yAqpgJNoRz2pgTIjbFrSjGvNKcylIdxrT3HyeA2iKLF6XXfWMZ3OBMtvOEY4ZEVRRF56dgXRqMXIsLeqF4czyfO/W4k+HmHBVx7CXDbNf7mrhkSvYdGJddSDLuBedxJEsFQNkw46MZeNosateDcdBYwZxBYRTIdXs7KrGUEXObflAJGyEfxP7WBZwgsYk1z8Szrx9tUQqJkMH9f88lWcjU4Oa50NmykTDFcPqTSA2R3CbFEQxuu5CdFvbzuKveUs4SPLSY94OSCPZD33qbEzF8uaPEFGCA24WIqNA5sOTj6HlR04I36U7jrkIaOCnxDGucq5vCbnCex+vpWq6gBNzTN9iv3DLmzBUmwNvagxG8cOGw1dmzJ5DedT8RcqDufaLpfgmsr5vAdTn9fE8ytEGE9sn6+sifO63BMR5Dq/M44Rym580RBQ+9dhbm+m2aLT33qCgeUnqDjTQu3xNTnLaRv/XaggnjpXJbr6MHX1I2ijJQwKaVRFxJu2YyoJ4lrVTuCVdSSHfCR6y7HWDmMuDTG2ez0vH6niqutexGQaH5FSjeyFo3VnKYnYMXlDmMtGUYIukgPlTBdZoYOrCB1aQbMNpPAl/H5N6Ywwtj+3IxPpQkiZEfJE0hGS2a5f4SOteaPuJPsqES1JwyhR34/S6kBMWEEV0bxBhJiN4JkGYmfraE25IeHlVHCqA4eOZ9ORzC9P24mCxPFsmCv8OFcabl2ipCO6YqRDDnQB1ISIIGpUvuXpGftFzzSiJU2gC2iKjBqzogRd83KFe72mi57OFS+ORWsCya6gJiwwxR8qX8Dt8/Fvm9i30ApuocPEhUaQyDXRbrEoxM/55pv2I0k6w0Nuhst7sWpuEvHJoZw2pQxEFd+1r2DybCPlL8HWMEA65CDwwia0iVnHkgqCTvh4C5GTS9HTMp2yMcvYbk/i9sQ4dbwW/7ALXRc5eayOq645gSxr7LrhGE89tpZwyMGrr7SwfecpWlf2cfxIPboucOZkDZ6uZuxLupEccUwlQWRHHEuln+SQD8maJDnkI3hgBZ62E2gpM6lRj5GmVActLRHYs5amviXApOBQJYWRxrOEDq7IuD6AMZu6or2FoebskEadcSnT9JySopjMk5n4ZFlDnpaq9+kn1iKgc/0th5FKQ/jGLdZX7VtFOhDCY4oiRh3sS0eY2qgtlkiuqRnOWH8nxFHbw2/lwO2/AKD0YBvRm54mvaSL2iNr6bZpYE5Qk3LM2H86s03Mu1gEAw40TeBcVxmqIlFVM0Z5ZRCHM0FZeRjKDYGnhKUL0vmc7zUvpME6HwE69d2ZELf5OjiFumfkm5x5KZhvhrGe5UepWHsKIWbF+uom5IFJn89gVT9ywpIljCfKmXjOTYKZU1J0nnW1Tm9NO+uWDPPCMytJxLN9Rrd6BcxlAbS0jHvdCSRnPLPOs/Ug10zTgu2nqpEknbLyEJIjhiCAc2V7pkMeCVuIPbc9ezheF2mPQes8znqxEdLmWcOSzcbAQ7eAoM0ZjlJLWkgNW0gNl6Fs8tB1ui7rm3NhPMsJ49j073FIUrN+L1OzJyymGrsY8Q1TeXQNZxSVVtWBLqp0egyDghIxXA0RNURZNYw144ztbiM1XII+bcK2Z/MhbPUDWcscLV1EO+oR0DHZE1irjPctfLSF6KklLLaV+bXMFS+OvZuPwAq/ER8w4CLwyvqsoc+pjVAhmcFma4Tm20Bpjii+q05j8oYJH20hHXCjhJwzenGX2yx9KOycnnt6FTZ7Crc7TklphGUrejl1vJZRYYjmijiSI45oSpMOuEn0VJPoC2KuHMHkjlLx5mdmlDfw0C3oavaEj8MHmmZsl0qZOLRvCa0re0mnJZS08RrrmsjeV5rZdd0xQkE7PecMUaNGHIQPjweWF1Vs9f2Utp0glTQZ8ZeXdTE64iR+rgrvZsNSbD7VDJpIumqQ0h0HSO3TWdm7JHMOnTEZMezAufoU1toB4iEvUsCDPFhJ5FwNTBHHoYPLGRASMOXS9gRVSl5cRihoNyZXiBqWqmH2+AU0baIi10kPliPVTFo3PRsNf0ZbxILDeZJbgeNH6uBMKehiQZbkCZGSS9TU1AyjOuyZv8FwkWisCnN20IGUMiHoIvGN+zP79K45TC+wF7hjuJnq1MzvpNCJeRfa71gQdCyWNE53PJMOeoJzZ8s4eayW5tH6zDC12RfA5BslPTIZ9WS2emC66Mo1+XHCn/uUHMZS6UdLm0iP5A/dtZiWnEJF+Skl/3HzvU+QXyjr6FTXjnIylUaTVDyDVSy1wil7ACFtRgwb0Rvma7yYLeThXOJ7PuK8Im0joYFuTxDf8TIA7sFK9D3bqD/UxumtL3F0+RG6T9cZk7WmfOsHZP+cWdemdsBEWwL32hPI3jBpUeHgvqYZwtjtiVK66yTJQR+lO/ehxqwEXl1DhynAlqvOIE7TgrGomd5uH8mkCRAQBM3oEFaEMpGBnK4k5+o7KD2zcsb5LdZQ+8XmfDrf0695trIqXtqJZkkQuf0RAJSqwUwHKrXyBKmWDjx+H5E3Pk5N1MGIriO7I5QkjLj3oi1pZPgbR1eNB5jye0n2587CGXx1HZHjLZRe8wqCqBPvriZ8ZFnGSCg5o1jKR4yEKavP4Fp9hkR/mZGN9kwjGaEsaIjW5LjLRoqz4gh1qg1UESFlRow68lro56LQCbqzMfWeC+YU9S296OtCwDULLrMQrnhxjKyCLqDrINkTIGo5X+J8H/ZifPC6pBDftB+lwfDPkkZKSFqSiJYk9t5a0t5wJn4vQDrkQLKmQFQRZY1dpxuJnmlEtKTQVYl22zCplEw8Zslk/bkccHsMv92lI/UckEdIxC0k4hbGRlx0dRoTCJYt7yeVkjk94KI60IyWNKNGbbAakv2VRKwpPG3HM2XG4yYY8aEEXNhbziJaUqAL1CVUImEr4bANVZk5Q3ps1MnLL8ycHJJKmgiFbCxf1YvNniSZMHO0V2F1vArQcTSfo2qgDuHJJqzlfvqEFMmBMtBFnLteAcB8qgXLoTUICFiPrCFd301826uMaWZK+o1QZgIC9md2EbntUcxlAdJlAdIzzsbgwIiUZXmZaCTHRiZEhY65bJQjsTSaNjUeZzmBl8uRXWFqq8eQe2oRNIkeXz/erYcz261c0wNrekiNeBhNCthHy9HbWxHSRoM6m0gudKgcQNBFVj95C2es4YxAQBFhitX7ofJ2/mBwGT7FlqeUublQAtliSbN15ykcjplBejXVcGXZvq2D4KsOtPHIFZGIBXPZGHuCWsEW5KmCLV+HoN4TI3TzkwDEOupIj5TM20Vqtgx5+RqjQmeYTx2VmpEqdxbhPOGnPtByCn9DJ2lHHGvATcIbyr39FFL+EtwhB0rYyI6pp00FibKLYYE2dyylZbia/pXHGGkwfFBDlYMc3v480nAZ8REveusZKluNen74iZ2oURuiOU1bsowKrdDvQafsBsN1I3JiCc+f9pLL2hcK2gnuXY1r7UnCR5YhL+0iubSd0YONHN7fiN2ZwGpNU1oWxmZLY3ekuP6WIxza30hfjw9dF+nv9dHf68sKm6mqYt73cLHSH19J5BPIue6BmLTi+tnbSC85S6LtELozSjroHPfz3kBqqAxBTiO7owiiSjrozow6CqYU1vp+POtPEty/kkRPNYKszBlhQo3aGX7kutzrIg4Gf3kzgimNd9sBkr2ViPYE7rWncK05hRpxIMiKoTsUCV2R0VImdFUkIYhGlkNL0kgSE/Bi6q5FqRjC1F+NuWNp3nNaLLe+XHWUtXaA1PLT2G3eRTnGbFzx4viVl1robFdoU8ppVR14x5dfyA/4lBTB3nwuM/ShKRKibAyrCFEbwyeWoKVMLBmrRNAkrPvXo7nDtIdlZFcUwaSgxa0gapTu3ItjWRe25nMk4iYkWWN1UsZsUTDLRuSDdMANCEZIloTFmCkL4709BS1lIjlUhhqxU8iwyVyN8AlnH6VlYdyeOA5HgoG+EsJhGzuuMfy+xka7Mb+6lFTShChqrG3ror+vhKEBL3t2GzNo25SyjFg0LCc2o8HttlKebKa+aZiKyhA2WxrqBox/U5jquTe2u21G73k28bTnpWVUVAVpbBrGVz7IytU9KJF20mNuTN4wKXsa274NiL21NIxnexJUExFJRwOk0ZKsIbFWfzU9XU2c3byHs+xh3SNvphkT6FZOPn0NifWHUD0htJSMZDPCBsW7K+mNCaRVEZcnRretA0nWiEUs2FSRrTtOM+J3MuJ3YdVSeDcfYRcwOuJgdMSF8/jqzPGVsAtLoCrzO5GoYvQFE6U7J4O+R8804Gg5RxVATYDwmtOYT7ZgObYyE8O0EHeLvr5ynFEbkRwuERNC+hQRdOu4wJRnzoz478rTvGtgxayuFZeC1eu7cgpjVRVAh4aKKJI1RenVezPrnM4k4Wkd1POxok3cw7MDTlqfuwZ70Iugi7Sr+b/b6aLkz8Sf8F3tD2c9zlwT4OYSyfnCTk7UHT+1dNGmlNHhO8eqtecwmdNEgjYGUg7sTf1Z+yS8IYSYFXmw0kgZnJJhPG28dW8biU0HADCXjWXmBzhXnWHoV9cje8KcChnD4hdLlOX9PlJW0q9swv3KJjrLe5BsSWR3BKQU4vjolZaSSQdd6JpA1duMzk9qxEvUvZU2PKDMPYEqMVCGrW6AASEOzBxRmHgmqZEYollh34CZ5aV2fOVhdE2gt2fmxDlJUhFFnXR6uqEh+1p9ZSF6u32zthFXqhX5YiAgYO5cgqmzCc0Vobesn9Swz2jvAV0x5czUp6fNxDsaiXdMJhSaCFl6vuhpE2PPT0aRip5ciuyKousYYVcTlizXQMjWT7ophVI2QnzbHmpPrCTYlz8zKMwczTmtp1CrR41JkxEnYtxGT+sxBElFS5nREmbj/7QMqoSuGdFERElFVyWWxSe/Ab19OZ1jHvQqG9x8vndmdq54cdyS9OFRtII+1tlEwWzWh6wGRNDwXfuKMXN0zIv/+BKUoIvmhAddUjmjKlM2NSqi0yQhZPQAlVC2JWn4MWNoIJfY22hyINnjyE4j/BeihmRPYK03Gh8tbkVXJEyOOM6V7aALpANuw+o13qCby0fw/OZWRF3KGrqW3WHMFSPYGvpI+UtJj7kNFxWgatp5lFcaVp8Rv5NzZ8sp9UXYtuMU7aerWLfByNhTXTvGkYMNLGkexD4uQFJJmbExO9X9JVTKTWwQPOzXAwwPeUDQKSsL0dvjY2zUQSJuRlFEdF1AlnVESaNFd6ErEqnR2TMnebxRVFUkEp6wzggMDXhoWmqEIhJEHZM7iskdJXq6gZEjrbQCmiNC5I1PGLvoUHZ2CX5nmPi2PaQau/CmbGiixuioj5HGs5njHXrjr3ENVFB/ZD3SSCmO316Hbk6h+EaJX/UyatzCiJ6mZflkFkBVFcavbVJM1jWMUtdgbKNphtWm1Bel1BeFVqOzENy3injX9MQbAqmhMgYeuhlbUw91Ha0E6jtREwPGiMQ4qeVn0JxRbC9tKzhg/wSzCVvLhiNMJP9uHKrhXOkAuqyhxixIduPZ/yaRYNdw9nlPda24FD6n+15pAUA2KWy96jThkI3hITfRiJXWZBnWmsEZqcGNcH0NmQlVi0VTVQQwgTVK14CLZotOM3bak0LGN3WxfJ0X7V6LKm1qKeayUa5fdg7JeYwq52QaeIcjDUxaiP1Pbce79RDOY6swd9ej+PxIYyWYOpoy7+MpKYo0cjX2pd2Z7F8Aokmh6o4nMr+ViJ32FzeyJOFGGI8xO993ejamtg0xT4CodxRT0opzpIyuiCVzrhl3idESo/M/nmPhgOyHQ1VTnplO9HQj9qXnMPsClFeEqLxttyFIUmZjslfahBqzoYScpPwlpIZLAYHEuVrsDQPU1I0SDNoZHvTmPOeJ0Q2LLU1VTYCnH19DPuGtqhKqmmuNwKMPb6Bp6RCqKtLdNfnOXcrJ3pcT87EeTyAgIIVdJGILc0e4kOiKTHps9jZ16vUJaTPyUDnIKsNLO9A8Y+jmJGLMgWaPoZuMsIFCWkaMOEAX6VVNyGkzKXuMWG03mmlSF1lf3YB73UnkvipDDFuS6NYEmiuCkDKhi4bWQZVAUhmJ2LEFvUhhJ0LUQWPIjTc2dzbF8+WKF8c1ZWE8vW7I+eGfn4n/jHMEU0kACwKiKY1oTWGtHQBdoPTxmxEQ6BsXnIIqGxY6SZmj1MLR4ja0uI30SCFb68iuKLI7gmBKg6Bj8oZA1Im85TfIw2U0jnkRIg4S2wzLWHrMjckTweSJ5CzRP+zi+JF60mkJSdKIx4xGYrDfS1V1gLpGP6oqcPpEDStW97KkeZCBvhI62iuNsKeWNG5vlNr6Ucy1A5S/6UVuSMv4h124QqWcO1tGTV2A2vqRjI+crhsuE6+8uIwRBNqU0mlnpSNaUtRUjxjle2JUVBmZhGJRC6Kk0X22nGjUgsc+81kItjje7fuImBU0XwAAy+FVlCMTLhtGEI1+hSdpY6TJaKzH6rupP7SepD3KcFUfujNGuGqIU9FTqLUDoEiYO5Zgbl+K+f/exikpiowODUbjPvjLG5DdEURbMpOiGiB0sBWTN4K5fAQ1acFcYlif491VOJoNFx3PxmM4V5whZE/heuh2o8KYvBrq2w1f6oZzzZwZLpnhy63U9tPd0EGitypT2U0f9p/uWuEYD22XLyxbzXO7YOVRglUDdFX0oUSsjEZNnDtbxuZtnQB0jNpJd7kzx5nLfeNiTsxT0jIvPmv4VUqyytYKBc+1zwMQj5k521FBaecydMVE/sHD3MxV3+S631m/B1w0Y6c12Zh3AtdCk2QsxIIMOrYl3ZhKgtgb+0mHHJjc2dsqEdt4Bx6OHGygrGNFZp3/yaspHT+GPFIGIzPFlhq1Ez68nPCRZZhKA2jj4lG0JbFU+nEu70R2xii/5XnC4xZ2QQAx5ELylyEPViAPledMf1zIfZlK3BXk5K7fIY2WovqMjqvjqetoD00Mb+vInjDWugGcrWcJHW7lxbOTMb8PyH4EQWdLiYCWMhE50UzSX4K+WUYQwP/kTtCNUGKCOY3siDNWMkzT1UZkAv+wi2DQhi1uIhiw07apk+6uckb9Tmp6W7KOY7MmKdcE4jEzug7X3GjMl5CkyWvq7vIxNOjJK7ANBM52zEwqMXGcoovFwgTyawlBlXE+/EY0T4jYrhdAAPGsHXP7EsN9TxeMeNm2OLqoETCn0c0xhJgZy+4txLe9asSZHvMa2Rc1Ac2WwPnU9Zlj6OgzOry6qNJV3ovLrqK5Q6jeIIm2Q5jdpRR9judAqeshUj9pcVj11M1YYrlf2PlYTxTfCBXXPwtActCHlpbRUmYix5tJ9lfgY3K2/QTTG5iLOwtfQAk7UaYkZ4gD7IcWCyjlfkZK/AjOIBPTFWNna4n/bhuyK0LZTS8B8OrLzfiHPAiihq7l6/UKDPSXMNA/OdyRq3JVFIlo1Ep/r481q8t4/NcbcDoTlJaF8djTVFklTLJKImEmFLJSWRUiFjXjcsXZsKUDTRMQpTN4JQnBZKSmFs1pBFHHqwqYzdk9IocziaIItLT2kx4zLOhaetyVRYDBh2+g5Oo9RsMUGbfgqyLJNccYTFixRJ3o40HlzVMC51vDLsq6ltCeFHCx1kgXe8tTKPW91J1chSXqpH37i2ieENZ9bcjuMKbSST8+XRdJj3lhDEKHlmfccQRZI7hvDccc/WzfeRJ90EfglfXGvQs58Www3Fgku2ENDt/xMADNT12HNDZzuHWJSSbnGyfoIGgzJuvlEnI1NcOoNM5YPhVrxMXSPds5k4SBja/iWNpDhTNBRaUh8tSYNROb+aeMN649Eq2qY9aUyos1Ma/EF8btidHX7QMBLJYUdkcKQdBxOBPY7Uls9hQORxKLRSEdcBsW+nM1oIuUMH2wOZt8GfEKrV9m8/WeWN4INA+4eCMNMyIf5Np+tmNNPUcotB7Ukb0hbE09OJZMpqCdKozHdq8j2V/JVGtlj+ynZ5qgyiUictaNupg18VGN2UmPlBA53oytoY9EbyW6IiOaU+gImNwRzOWjeFacJL5tD6buOuSBSuTBClDkmQ0tOuHf/zkAYtgBaTNnFBMlfbWUdjcgINATsiGG3KiuMKlRD8mhUgJuP46qBOaKESxTUtwD0NDNuhIrFZ5UxkdUtCVAFUmNlGCrH8AFCPJ21IQZLWVivzZuXY8DQQv01XHiaC02W4oSXwRvSdQI11YaZWzESdPSIartKmPZmYBJJEwMDXq4/uYjDPR7KCmNYrEohENWolErVdUB6htHqG8cYc9LLYz4p+ePK4ypYeWmcyUIxMXqeOcSyBfr2JcDYtKKMCJnPnfLwTWI6dyJRqYj/+qNaM4optUniK09ihjwYD7TnLVNrpEgQZNI+X2Yp7p5oGNpC8LtC7+Wgs75whZ/4bHu28hSpZqObUYA9GM3PoEl4sQ9VIF7qArXcDkCQt4GYXrc4om/m9yTw4M2awp5pAxTdx1SeGEVzKXkTBJauxoJ9xiVW2jf2vHwNgKGqHbx6MMbs/bJL4zPB4FIxEYkYqN0SkV73NnH9TcbLh0OZ4rQoVYitiAChktEXNSQJAG7XScZsFDmSmN2x2aU3n66kvLKIG53AsmSQjQrJPoriJ1pQAf6qjqxddcgSBquuNOw+o6L4bQtQdpmDBHLcSsjjWeRUibKupoo6a0DJsWFnnTS27EUTdIo62oibZkcWo5v3kdZfS9qUmZs9/px8TCJ7DLeM/PpZqKnmwBjEmE0aiUWE7EoMpIjinP5WVKjbvSUjKl8DFGVMn6a0Rt+h+2Fq1gxVpn1XkshN+6f3YGOjlo5hFoSILnmGN4th2GLMYEvErHQ8fJGlowZ5zU9ecd08rlWtCcFdGscx9KeGetGn9+Mrsg5Qw62KWUzRPJiYnck2LbDcI1oXdGXsaKN+p1omkA0aiUcsjM06CUWsbAsXD1nRqsJ5koTnU8g57rW2SKGQPbzmOiq3JBZYtRNbkeK3GM+kywkxrRoTlJ2y/OIpjzDcYB/yMW+EZF1Bbo2nJdA0EXiXXWZnxMhIFP+UlL+UiLHocUskGrpILXsDPGrjIm1loNrMPXVIEaN59Zigcjzuzh99XOgC1hOtBJv6iLctp+Rhi5aX7gGQZUwn2pBqe0jKaVxtHShBN0oQSdpfwkm3xinTtSyYpWhVM1mhVRKZs+BChRFwmxJk0rKRCNW2pQylKAL2R0hXludmTjVRh5XhfD4v7MQQcfkC2CrHSQyVkbkxMwxDF0XOXG0lqrqAL3nyohFLZT6Iux+fsJyr3Pr7UZUmcrqANGIlUSisLTH8+FKEMiLxfRrfT1dOwCqhG33FiNd/LTQcrMhaBJSyM2SfYbv8/m4eQkISEHvgvcvlCteHAN4hqrY8PDbACPFZLRklFDFEO3bXwTA8eR1YNcRUhbaFQVdkbMEca6K29y5BHPnEnRJQS0dI13fQ/T6ZzGfacZ8ZqnhY6OKkDblDEF0uTHj/BYYmuVCsCpZTm93KaKoY7GmkBu7qRB10CGZNKGkJRIJE/19pVRWj2KaIoyjETMvPrsSddzdwHFkPQlrAtGaBE3EWjOItWYIwaSyShVRYzZ0VWTME8Ies6LbJ4VtecdShpd2oNgSlHUuoeb4aqQcwklAoO7ousxvU8KGt6+GWLkffcwDmoD9+EpKepaiCxqnxRgT3e3QgVUoURvuVWdY4vfROeAFYO/LLRnLjKXSj66JmRnLkjNBcsyNwxkHXaC6t47eq18i+tw1NAcmLW0TFY6AgDxYiTxYiTRaQnLlSeJSGnNpEKczifPGlwjpcHzfeoJpO2vk3AHicwnjqUIrcusTM9YDlN/yvBFfNAcHZH/WUG0ri9ew1DX4WbG6h97uUro6y4lGrFgsaWIxC7l8MduUspwW4nyWslxDyVMnOear8GebCJlPJPf1leftsEzgcMTziuPzSbqiI5DoribeXYVoSVOyfdIV6MjBhkyIxFwsZqQRQVKw1vdzPKbSNNwAev5ICmLcjvWwMY1XMydRfSOkl54lufo4zsduYtl4tIiJUUVz+9JMxBVTVz2eqBtNUgjf/qtMmfE9awnuXZMV9zdyooWzsp+z7bndEKIR41uaOMfoaSP8o77RDLnHdfJdPemRkrwh/toUHyPNJ1i+qpeecz4QdOoaRhgar08myujrKaGmboyGJj8NTX6efnzteDi3+XMlW5AvlAX3cr7mXJzvfRAQMPXUzb1hDhZqELlU9/g1IY6nIugiztEynKNl2EJuzm56lehNv8usn6jShvoqkE8uRQm4ACHvcImgykjDZQhxK+VxB32rj2alsRSDLsynWzBFc89CLZKbqY2cnjbljGeci+6ucmy2JNfeZPjXOZwpqmoClHdOhnXTEtZMLOlIKL9ICDL5rJstOv6Gs5l1SUc0pzDOhYBA094tjNX20Fs2iHVfG5LfR6qxi8SWfVSBEZ6ntwpz+Si2ugE0VaQ7Zgj66Y1N7Gwdkj2Bz5UgoOpETiwl0VNFpeJCdUYIbjasQYp5cvJdY1U4Y2HsGnCho5O2JohbFdy7dyIgcCYJ8ateRqntRxAgsekgHUAHsDNQzfqoIYajeaJVTBdc0mgJaoXRYCohB7I7Sry7itChFZnrmi3T49T/czW4hQotSVJZ0jxIw5JhXt3dQmBs0rUoFpsZBnDq8WYr/4A8zFRRPbHPdCGgo3NaiiOIlswk3FwUIpInaKwKzxnpIxq1ATNHUM43G6GeMhM/V4vvupezlh89VD+rMJ5gIRk8cz0HtyfKjg0n2Q7AKTrbK2hKmOgKhmkcaszq4E/tuIgpC2J/DeeiJspvGUSpGmQUCSllxh7y4hjxEVt1AvPx5aSbutBscSLWFP0NZ5H8Puy/28VpaeZ9nX59+dYtNpIrQunOyQgqib5KHM3n8CZMdJ6pxGJNs25DF4cPNE4Tx3Bo/xIOH2hEFHU0TVyU0KCz+SFfaWJxIVyJ13mlne/lwBUvjuNbX2G/2kvt0TVUdLRkrSvpq2Ow+TRxbxCAsZfXo8WtaEkTtfV+rNeP56L3l3DqhU20picbFc0WI9nSTnq5EbdSSJqJWgwxYt2zkeTaI+jWFJonTGLzfnzA4MPXz8hiU2TxicctPPGbdWzbcRq3N071SA2LMQ3Sd66RhCtMyhZjyd4tObdRJYWkI4IpaUXQRERFRtRFBARKe+uR0mY6thn+20yJcubZcBx323GUsJ1YexPxc9VZGR0naFUdxuTSg4abi43xGf32GP5Nr2L2RCjrqWfpK9uzxPuEIKqs93Pgzb81ZvyOUxYqQRFVnN11hGv7sT+7kwGv30ibvd7o6L3g7Sf23DYaq8JMSsvssqdjOlefEcfyuC9qsr/CmI0/T6aLjemNrySp3Pwmw4J5/EgdQwOeTAcpETcRj5t5+YVWopHC4slOPd7UYwmyQsmOvZh9Rp3R11NCd1cZgTEHAw2niEWtrC1V6bem0JImytI20g09aKVjCAlLJsTdhoffNqsluZCJexPMJxZ1rmdVcCQeAHTcGw9jbzSipeiawGO/3kCbUoYP6J72nGYTv7N3PObu9ISCDh77VRtNzUMsX9nHkvFU35oGqB2oMZsxryBhIT3mQUuaSWHC7wpgKglSXjlCOuBCrh6gq8qIXMP4d5EOOXAdXYX5dDNquZ+0qGGL2ZECngVHwbhQkR30tIxkmwxB6Gg24izrmkBN/Sh9PaW8/HwrkTzvvq6LeSJVLD5XonBcCPO5zteS3/Ficzm/L1e8ONZcUQjAaF035pidhCuElDZhD5TgCJSy/LnrOOUeAVXEGXUSuf03JPoqSE3JOmYuG0M0pTmlRWlYc4bUMmPmsDRQgZgyYTqwFvO5BtSyEZJrjqLU9CMGveiJJNKol6FhLyn/zPSO58tsVrXXO6oqE3t6F3FJKdhnNBcTH6chWKDu6NpZt/c3dtK3+mjWMtdQBeVnl+D0lyOOZzYSVDEzuW8CMWaj5IWrGUmQl1NSlBZZRrfHEGI2xKSVZZoN1R4hVhYAVaT22BrEPBZKUROzhPGy/kYczhgHnGMEVhrZ9YSEFfe6k6jjqb5dw+Xw3FVgMsRVfdRM9yzWxwmxJQ9PvpdDAx4qqoJ4tx5i8JdlmWdyPinUbfYk1ywN0JdSaWqe/F5XrukxEp8APedK6e4qJxgoLMb39HOa+m0ZE1NfzNq2pm6MmroxRv1OSssMJ4bREYcRbg8I+72kx1wkjy6lVjQR32l0uA9e+yTNhzbgHPPlFKfTl80mlvN1TkIFPqeCkVQcLWexNRjCWE2YCe1bPecznH4vRXOS6zd1YakaIRqxIIo6NnsKNWrl4LGaGdbN2dB1kc4zVQwPeigpjVDXMEIqKaPva0OyxxHNaURbElNJENGcIqCJSJqI2WdMnNM1kUDQTnTvdQiihrV2EGvdIJHjzfgw0r2LvdNDJc4taKbfkwtZR2sJKwMP3TLuYhUFXUCQVSRbglf9AsoixcOdD1eyi8VicaVc50LPcer1zZVpeC4u1ByTC8kVL46llJmlr2xjaEk7w02dWCNOEo4ofSuOU3tsDd6+GuRhY3hSdRvWIGvNkBFwGgi8vJ7U8NSg6ZMNilo1ROXpVmoGG8AChH3w0jWZRmciN7quyAuyll2pFNoQFJoudervQoTU1H3PRxhPkC2QZ/+IKzuWUda1BF3UiJaM0bv6MOGKIcIVk+JN7mxArfBT3dVE/6pjoIKoyTjDbhokEVHNbeE5JUWx1vYTmch+p0hGBshxxJATFBlVVhBTucWxWTXx1j03cLKmkxO1nZyu7mJDx0quO7eEo3VnGPaMEr3lKQBMcTstr27DESiFBfTrziQELC9toOSq/VRUBVGTJkJRM47lnUSOtVCoWJ2OKGqs23CWqpoAAC3AmZNViJKOLKs4XXFOHK0jFCxcEMPc75jszj+9zSUL7H1lKfGohVjMwnqtFNGaRI1MNhSdgPDwDbjbjmGrH+D01c/x6MMbaZNmfi/TG5iFTFAJqAuf2DJd+Jkrh5FdEVyrJlNqT828VUgn54DsZ4PuzYQTDB1u5UTYSDyxymbG03aMjVs6ePThDcz33YiEbUTCNpYuG8CmyahrT5Ie9YCoY630k/KXEDvdiBJxYKnyY6sfILBnLYmeyYQFOhDrbCDW2ZC5B1Ofw3zjQV9so4WuyCiByfiuaUC5gKnWC+H17mJRKK9n6/H0NnV69J1c74ouaGiOKIlNB1DL/UgDFdhf3I6gSeiChuoNQb6JrYvEFS+Ol+zdgjpowjOYnbVltLab3lVH6Fl7ECHswnS2keR6Q3SY2pvoPtSaqzisB9dhPbiO0qYezm7eQ7RklN6VR9AklbQtTtwdImWPIQ2Wo5b7KRu30g08dDMLFQPnw9QIAHCxw8fNztRKs/D0qYWXeamQVBlU8AxV4hmqNCbdmaKoV+1BNaUR0iaEwQqix5azpqcBdIF21yixhm6O3Pg4YsyO7ZXNM8KxCbKCd+th5N5qLMdWoEsqsRuezazXHDGEhIXhJe3ED62eUelMDL8LCKzoW4rlwBpO73ie/Usn03WvPbeMtKgStyRYe24ZslWGacP22gzHikmmV2zJgXIGfn4TZTe9iGSPU1KahtJOnMs7F/xNOJyJjDA+d7aM40fqFy2N+mwCL9FbRaJ3EGvt4LTllQReWcewPBlwXFdk1Iic5VsrSSobbLaMyO7tKc065vTwZnDhfAFzWXry1Q2iJYl3yyFEk4quCST6Kokca1nQyNVEgqKhR65BS1hpGC9fW70X0aygpWY2OXOVP/WZ7d+zlB3XnORcyIRXF3CvOYWaNOEoDWJv6QJJIxwy6hr7ku4scTwXFzspzWuFyy0Wcj5hfiEE6uuhE3CxNUXkhqfRSgNZy9SqIcJv/yVoAkJaRvYU4xzPiZw25cz/MdbRgL2jAV3QUGr6Sa6aFAhNffVYZIkzipFX3Mhfnj0pb/RsHSsiTqIlY6RtcQRdxNFfgmPER9/qo0hBD9YD6wjufAnZmX/yxmKQr/JZrFnh8ynnfMRpIdan2ax7F1IY57Med63fx2jDOaxhF97eWuxBLy5/OTHvGKd3GokjxJAT3ZxGHqhA7qshvmUvpr5qdEHjXMhmhFZrGkWp6Ufyl6E5okRv/B3SQCWIKvbndiLoIroiIZ+rRakZQCkbNjIT9VUhRh3osoKQllGqBhlsPYVsi3K8v4rlwzUZF4uuARc1NWPELHGsaQuC7mD589dSWxPg5ZaDDHvGONxwmtv2Xoecxy2ja8BFfQH3K6uh0UUSPVU4V3Tk3Ha+rhU7r52c8Nrb7bsowniCwCvrQNSxVPop2X4AAGvtII5lZ9nUV8ne5OS3LphTlJaE8Tv8bK4Zo7Q0QiJhQg16COxZx+H4LP4z45xPwxMQ0gXtn8uv2OQbRY3asdYM41x5JhO2zf/kDtSoI6vOmfh7dsu7EUOaJWdJxE3oqhGb3NbYi3NFO339Xh7/TQvrkhUwT9E99f0JBe2cOVVFqS+CvfY0Y6MOSkqN64ueXkr0TCNm3xj+8cxzRS4Oc6WbzsdrQVi+HgRyPi5EplP7i9vRTQpi1I5a7qdHSFLjSJPYcAhEHVN7M/b4ymKc4/NF0EVMvbWYemszoudYwxmSu57LpEnWUiZSox5SvXWYO5dk9u3ze8Hvpdmio0ppwmV+XGE3ax57IyebT5DYtB9LwkrakjTi5ar5Z6pfKBYqGBdiFVoMcbqQmesXy1qcSyB7B6oZbThHwhVmYMWJGftYjq5EFzVMvTUIYQeRO4xQUOnGbmN5dx2JtoMIKTO2l7aSru9FrzBCdKnjk4TaHaNocRu2pl6UmgGEuBXdFcV6oI0VQ9PC5pxcS8oa4+jNj6PU93KQvaz7zW2ZyXn7bcP0rTJ8i9kANcdWU572sPPURn6+xXCnOFfWx9KhbAlcSISDqZXg9Abv+dNetpY7KfVF8D911bgwWYDV2JHgd0+u4bqbjhAYsxMMnH+jM/X9meu9b1WdnCKKljCjpWUiMROaKuJdcxrXmtPcOObGH5NwlxzBbk/hCFuJhK0M9nvZ/+pSVGWiDpgpjAuJ3HChO4WCOUnJVfswl+ae5KcrM33FZzt+SWmE1pW9uFxxdF0g3VdJYtRDyfYDmMvGGBp0c2LPUkZHjPdr6vXNJz3xZOSLcg6cNN4rmz3JhsoU8UgELS2T6KlCT5tIDlQUVOZcvJ6HwhfCQtJNF3p/CxWfsw3VF5k/uZ7PfO5tob7GU8sUE7ZM9dnhdwJOzg5Da3szod9/iNSqE1SeagLOLyrPXCxIHMdiMZ588kmOHj1KdXU1/+///T8Aent7eeihh+jv78ftdnPTTTexadMmACKRCD/84Q8JBoP8yZ/8CY2NjYyOjvKVr3yFt771rVx11VVZx/jSl77ETTfdxObNmxd0YfkeSntSoGfQQ7SjHCUtUedWsVYPY63yk3TFssTxBJqkcOhNv85aJp+rw9y+FBQZ+3AZQ3pqxn4XirksYLkq9YVUXFO5FO4Ml4MLhWewmg0Pv42EI8zpHc9jD3oJVQ7i9JfR8tLO7JntMpz+1a0IioxuUohe/wyaI2rEX02aSbd0GBbgsB3b/jZ0UUcerMA9HpJKiXiJySroYD66gobkpOUr6h0lVDFIRXsL5oSdtoffyrn1+xltOMehN/0Kb18NtqCH0p4GRFWmZ+0hAPpWHaUPY6LeBNVjkyHCCg37NZd1oE0pI/V8KfENRym70YjWMfrCRlJDZZn1s723ZnOaJS2DmYgEwYCNPS8ty3us+fqmF7rvxHeTHvMy9KsbpghEH5IjjmSPM+IaJjDmIByyZUTffM9jOucb2WEuRFucspufR5ySWjh8dBnpUTfWukGSQz6SfRXk6tBMF8ltShmn3D1cc6PRCVNjFo4fq6PnnI/V67uo39gHGEl5Tp+YOdltoUw9PgAhiIVm2aHIRedCTSKfS0RfCZEjLsWxF6ODMNszbU8K6KKK5gmh2WNGtJeoY14RXya0Sa4EKwCSI4q9uZv2jnqanrie6M1P07F1N83cfJ5XNjvzFsfpdJoHH3wQk8nEO97xDqqrDZ8uRVH40Y9+xIoVK7jzzjtpb2/nf//3f6murqampoYXX3yRZcuW0dTUxCOPPMJdd92VKfOJJ55g3bp1OBzzf5DdaYFwsrAeykQDr0YdnDhqWM7OABvllTQnPAg5JnfVlAU4etULALQ+fw2WiBNRE+mMTZvBJF0ccZyroZzrAzjfhvdyEKkXi1zW49NXPU+kzLhPCTVM475NjPRUIggzK4BluhUkQJPhqTfQnhQy6aZRRZA0hJgNaawEIW0mXTVAcvVxpNESLPvXIXfXotT0k1p5ko7kWTRJxRp2o8ppEp4Qw00dlPY0UNpTj/LKJmzd1cR3vkygqp+wCgPXPE3JYBVVJ1YgVI7gd42RlhXqR6roKxkiLSsE7RFsQeuiCWOY8h4drGKLPYyvLIKlYgRBVkj2ZacYzoXVlsoI40P7G+nrKc27z2zW1UIa5/lOKJ3Yfm8yBklgbH7fw1zfmChqrG87S6kvzMF9SxgdKSwL55AYn3Mba+0AplUncTiTWcvDR1uItdejqzIpvy/nvtM7EhN/bzQ5MsJ49MUNpAbL6Rlfl4ybiUXNPPvb1RQycnC+nfbzZa5JeZe79Xgxk65cyVzOz+i1jI5O9PbfoJmyg6m6f3bHvCJUTK8HNFsMW2035rJRbHXGCKt96TnCqoSgCejahZ8fMG9x/OKLLxKNRvnbv/1bzObJCA2dnZ3E43Fuu+02TCYTFRUVHDp0iIMHD1JTU0M6ncbr9eLxeFCU7Bup6zqPP/44d9xxx4IvZLYA+7mY2phqCWtGGE/dP1DZz4mtL+PtraVp/yaEKUHnF9vXZrKS0ykpjRIM2tHU+Wexm1qZX8mV5uXQYLYnBSwbD2WEsePJ65ACJYwBItA+bb9c716zRedMyIX5eCvIKpLfx3J/NYIo0A6kVp5AKwmgW5JY9q9DrRjGfKYZy5FV1PrC9K04hiVmJ1k1RENfE1WBMg43nOJkczvo0PrctTjGs0MCxF1BQhVDpGwxUikRXRVBhqSUQhM0yoIlJE410lVAhsSFvd8Cr+5uYdeaYRzLunAAgw/fkBmuz0co6Mhk8xroKyFfRrtCON93Z/p3M5ubz2J8Y2azQkVVEEnS0ebpXz1bZ8BUGsC79VDWsoFf3ATzSA2fS3wNVp2jgslJd1OPf9YfoWX5AFZruuBUxZdaIBdZfC6HZ3o5ulZczM7WQq9dkBXsS7oxl48SFlPsMCvYbCmElBktZWL0GSMtj+IbIdF2EM2kYOpoQrPHjFHSBcwRkU0K/VWnqS9XiPtGkexxLINlpIZLCR9pRYsbk2xFawKTN4TuKoM3LOjyCj+n+e5w8OBBdu7cmSWMAQYHB/H5fJhMkxbV6upqhocN/8rt27fzwAMP8Mgjj/BHf/RHWfvefPPNPPzww2zbto2ampqFXMe8mPqC5st6BaCLRpzaQG0vp60JmvZtJmWP4hzNHZpp+kufa4ggF5I9hrVugFtXn5nc/kQ1miqyYnUvRw424C3voT8GHJ/0QT1ff+NCuNAV3PR7fkqKXvJKdQKlzE9oaTumk81YTi5HTFlm3T6fmBQA69HVmd9Tp61Z920g1dyB+XQzIiKmsw2klp/GcmwFtrCb5j1GRdQ4MukjevWJTXSV9XGo6SSnrnmGlT1LiYy6EBWZFaIVW7Qqk6lWP6cTtcRJySkGSv2M2sPUn6cwnqty13WR0P7V6IqMo6UL7/b9xLtqod9EaVmY+gY/ne2VhIKTz75NKaOi/DApvxcth3i7mO9EoXF9F4tEwsyru1vYctVpAqOFNGg6um50xGa7L7J7pl9x1VufJPDKWhK9hUdxmE5FZQCAfUoE5Gnh78bPv6wiVFA2vbnIF2ljMUXG5SiiXgtcDnH6L8azvRzfnYWek+wO49l8GD0tE+usp0cMk0yYUFWRHdecBOKYSgIMr9yL7Alj8ZeBqJNeehbz6WakoGfOY2QhaDS39rNsuRHlZvRYM+nulaRGPTmTZGkJK8kBK7p1/obD+TIvcayqKgMDA7S2tvLNb36TSCRCa2srt99+O8lkEqvVmrW9zWZjYMAIKO/z+fi7v/s7NE1DlrMPu2LFCk6ePMkvf/nLLHeLhTBbrNp0fTdK+TCWI6tpTc0e3kgTNMwxO417N9O16VWivhGO3vwYAEtf2UbEN0K4thvnr96Y8a+ZXmFP/TBzVvKiiq2hD8+GyUgaEyxZOoTJbMwgb13Ri9misgSobRghFLQz6nfBibK8H8FiTGq7GJXa5Toc1u4Zovy6F7AcWkNXew2ggGSMeCxmZSgFPdj2bQAg/IYn0F3jYmOW3rekiywdrqNutJIe3wBha4xgSzshewRb7xIah2vo9g3Q6K/BophxJu2QtLPqsTcw2tiFKil5U2O3JwVcebJpzZUUAbLTQrcdXk6ipxLfda9gKR/j1inbm80qoqShH12BY3knouUUgqwy9nIbbcrsFsd8nc58ovVyHxoHGBt18fivN8653dJl/TQ2DbNv3745t1XTuScIp/yl8zq36Z2FsVEnvvIwzcv66eosR1FkHM44qZSMPB6Xu6w8SDRiYWw023VHFDWqa8aoqRslFLKRTsmU+CIoSgfCy1uYHjUI8k8KuhBMtB9XUli3omtFkfNFtCSxNfbiWHaW6KklRNsbQJOoYmon+iQA7h17aT9dhTpop6UijsVjOP5bjqwCDFcLpWqQmGpBF3RiJaOYKsaIRM1Iignt9BJ0axzXkg5s9X24kjKHDzTQ2+0DBNoUo34q1Lh4oZiXOI7FYmiaxrFjx7j99tsRRZH/+7//41e/+hUuV27/xQkLB4AoiohibsV/2223ce+993LgwAHa2toKPqdzYpxhScu6kfkEstcTpX9pFzZ7Ap7fAcwUrcZQ+kGGl07a98wxOym7EcKptLueiG+EoebxtNJz+NVN77lO/K3ZY0TeZEQP0BQJNIGhR65DsibQ1h9G14RMrFezRSUek7HZFcxmlbLyMGXlYdTGEWIjZSArkDaBqBHWdIRDK/B6I2y/+hSv7m7GPzzP3hwXv7d/OVgZpmKr70dLmuhqr7mglgHVEkdzRTD5y7G/tA3dEid27Yu4WzugswXInz7YrJpYOlRPv2eYzkojY5x4vIX9jWcZqm+nPFyCRTHTNeBCEzTar32KpCvKYMNZnL+5lZYphvC5xEC+ymm253ZA9tM2Vsbgr68zQiYmzEiOGCZvGN9Eh3DX3sz2ocOtOZPpFHr/ZwslNd+K9kIKjoW/4zpVKwxRvGrVKuB43vvfqjpIR0qY7pU8/MROtGSOERBBo1Vx5b03U+9Hd1cZmiZSWz/CshX9HD9Sy8o1vQAc2NtEMilRVROkqsZIunS2oxxZ1nB7Yrg9k2eUSslUSiZMZh1TRQDueAKAzt9tJT3mncd9ubBcCZ2ry5nLrW5/PT1PXVTRLSmEuDWvVjH5RinbdghpsALLc1fjHSvJeX8GH74BU0mQY0qMZcv7cLgSeE6uRDy2GkdTN6Gbf4s2UopSNYhuSXESMMWtOEfKsI66kAVIOMMkbvotaVMa2hsY+d121IiDcqB8xhENLtWzmrflGOAd73gH9fXGhLY3vvGN/Nd//RfXXXddlhCeQJIKC29WXl7O1VdfzW9+85vxir8wtt/hJhkR8OrZE+S8OQ5bzyZgE1QA64xlgXErWRtTGmB5C6vLmknZY+iihnuwClPCSqTMT3pTEosuoieaSbhCyNUmhCkNehsOAkI6+1ymnZta7kf1BoHtJPoqsI6nstavjiGIGrCSSNiKrgtYrClAoL+3JBPvtQIr9iXdiBEHYsyOHZHYeEYZzRGFdyRAKAW2s307dHVUUD4lCUebaMXlShCLmVHz+DUvVtKOCRrXWrnl/SV517eJ1gty3IViKm9GV5bjrJk5WWn684WZz7hQlOowiCbkXgfgQHOFUaq2Y1/pxTliRKtwOnJbUkNRY7nX7mZ7lR0A91I7LZGt6GxFqBDQgHqM9OobzJOpscUdAeTuusx3MtVaXLHORNNd2cdsIvc53EJJzt/Zk8Wmjii5gSpEsQW7PUlZhWF1EKMO5LIquM54xwNCuqB7OvW7LfS5ZH3rU5i+//Rrm6CQiXBTme87Pf2cdTmNbo8TipuQLG9BMKfp7u5m9RoX/+99dTmPYZRhJ7Vse2ZZorcK9zuNd2rqtZrLR5E9EcynK2kS8lvtb6Ekc+0CjfjKQzhdCTatcqIn2gBYX2NBkNYDOrIrhuyOsHmzhCyraJqAIGCEfEtJrF4lYxcFJGcMXRXR0yaQVNKNpfTHL25dMPWeeyVmjJ7Mdl8KYa7673yZqD8vdy5F/Z6rDpjtec73Wc237p+qEabXJXPeH0lFsiURJNUIH6sJIOrGZG8AUceetKCbU+i2hKEHpiAmrLRE7DhDHlBFdFOadFMI2IjcV424wagbc94fScNcKnGry0wkXE1gzEFZkx1n7SAyjYhqM5qkogU2ocsKmBQkfynUTWoMr2QI5JiuI5a7CVw1s86ezvS2x6ubkJ0aRo7IC8e8xLHD4UAQhCy/4oqKChRFQZZl4vHsBx2Px2e4WszGDTfcwL59+/jtb39b8D59/5sm3AkwM1pEoZPzclnNmi2GWAFIoAPx8d8OYoAmqhx880tYIodQ4sY1SmNexICXxFbDGhY5uQTn8k6Gfn0tLXFjqECzJInc/igA/qeuQldGKH/DbmP/YR9Dx5eSHvEyOSFp4l5PnW2eAFzTLGoShvBwo6PT4fKjrjtCVXUAgJOHliPZ4zhazgEwMJ7t+NGHZw7lGj38uRMYzIdb3l/C4w+O5Vw31UJ3uVgXqu54AlQR694NCIqMPFCJoEl5e7ET1ohcVs7Zer4tkhkhZUYgilI5SGzXi5iebmDVsXLGtGx/zgkL8mSUicl3Plgh0LHNCKFmOtmC5WQrYsqCLqok1h0h3WKMhAw9cg0Vb5zMutf28FszFoWJ76Dtzx08/t3cz6oQCk300qo6GMoSqtOT6SxGBJiFl1GoxWJuF6bc31J+i3gKHR0BAdWUIPrWRwAwHVqJ7dQKTkkJPvFPrfz03sMceDCRc/LTRNmh39+dWeZ85BbE6GRdl67pI77jZcic/m6cD7+RM0oevxpA9gY5YzLcIhqajB0HHrqJyYbK+N+z6TC2hn7GRu2UlMYIBuzjFmeBaMSK2ZKm1S4hyKoRY35wqt0o9zVdaOYaoTgfC9Zs9d9icCW5VVyKOr4QV50J5vOsjHLnX8f8/9n7r+BItjS/E/y5CC2htUYCqROpxdWyRFdXd9Ome9fGaDTSuDTSjL0cW2E7O/s2+8I22thQ9QOXuzvDaduH7hkWq7qqq27VlXVl5s28mYnUmdBaBgKhlYe770MggEAgNAJIIG/+za7dhMfxc467n/Od//nOJwqZg+V8P4KG48Qolu45lHUXWsyEK25FF1UETSLo9iJZ4lhWG9DsIcSIFaVrFiFs5fTv32HuxAPWWufAuBUMQYsbEIwKQsrylehcE/5bZ3L2y2aLcemVETzfORl71kI0GkcUvZwamqbFsw6KBAYV08PjmJ66SPEWA2SdXaU4mQAIjMcLm0sVko+OHrh2aW/TdJRVu8FgoL6+npmZGZqbUyk0vF4vsizT2trKxx9/TCwW2yTEi4uL9Pb2lly/yWTixz/+MT/72c9K1jh3GHTiptwEt5D9cTEUi34Rs6eIStweBnsY4/3j6I7wJjEGsA9OAqAlTBsfXcdU56EGUBQR99XbyNYE/rvHiE6VkpdsC4UEuYBAX7CBkRuXWEJHdgdwn3+I7Nw5GN/90TDxmIEvP0s5jD0vc4qDBs9nV6h/6waxS7dRY0Yi452ER/KP5czg86ViQLWRmd4x2bQCqkhD1J5yBs1ySMgVei09TuP2lDCXFhpReqdQBscQoiaEpIzmSPXJ/qsfsZRUsf32PcI/TB1hP7h4A9uGiVG1kTmWdmOakj0H98setFSyVM6cyVen6vYRuXYDMWTD9PgYkTe/3Pa7ELJiGTmas47sTHbpvg2oNmy/e4fwD1LmW0rHLMbxXjRbmETvFErvFKLXjfneKbo1M4/f+aiwrbs1Qv1b31IPRCba8X59diPZy/bTJ8kWxtKZcrCpqU1teMIhE9GIkTWPc7OPhWbKQdkkv0T18Tw2PofJ6TLbDMXaP4Wtfxo1bMXz0aubZlH+bXf17ahn4FYqR8Qk0PXsGA2TvfhbFvAqIstROZX8zFODaFC4o22Y7m1bj3WMxiRdvSt0dnkYfdbCzFTj5m/nL40jCDpTE/V093qIzTey5nGwM1PEForJ7uywiqXcs1com3q/9tprfPTRR9TU1GCxWPjtb3/L+fPn6e3txWq18stf/pI333yTiYkJZmZmyg7PdvbsWW7cuMH09HRZ9+V7kZl/5yK62eULlcn8zRpwc/ZXf8xYHBD0zTBvixOtaAkDy83TnDo7jfer8yCpmNuWsPVPY3CFUFUBg0EDQ2rHKdn2Jv30pj21T8DzyTXmWiY4fXYKo2mLkcmyhmyP88M/vMPHH5yBZL7aym/7MNt1JX1OvF+fI+lzIJoT1L52i5uzVhRFropgzxQCqiPAkYQdnp0gtNbEavcEq92TDHzzGsaodce9uYSF6ckxTE+OEX7jC9iIOalb4li+uIDod4GgIcbNDACEQAhb0W0R1OZlQm98ju3z1zc1yLnME8pBLlJcaJNaqvA7aE5SpZwSlLIga6YY4Xc/A0C1Rok0poix79Yp3BcfAGCYayfeP4a83IQUTG2S3LphR+SdzIV1RAozEHRi/837RK7cIn7yCfGTTxAiFupWmmn+3Q8RVZm1rilCCSOi1030lesc+ewNRqWdMkmNWInONiO7ggTuHcdQ46P+3a8RDdpGVkQHw7IHkxDnLUDX4emjFqIxE+cuTGFfayJygEnvbglUoXuzv9X3HQch1NvzRCljQTAo2AcnMLcv4fzmCuMBIyBUZMM9HTQDZlhzYQI6N66PSGG0hIkhtvwQJGsU1/kH6PYQJnOShbkavv3mCKHg1lpkNCWpa0gR6mRSZOmXb8OGk3f6uSqZS5nrxPOW92WT40uXLhEIBPjrv/5rAE6cOMGPf/xjZFnmH/yDf8AvfvEL/u2//be4XC7+9E//lMbG8lN5/vSnP+Uv//IvSyqbTgJSLtIvPtm4AqqEvFaXdwEvpLkSAHRhK7ZwJEb/4CSnBjbOKgSd5p9+QjJkITzajevsEyRpe32x+eayTAtKGXTb+hzfKL/Yhz/YTN3rNxFNOwnQ+Tqd+HzRqkvqWymLwEHVGqeRzu52RwtyJWzk9bcfMTHWhOqcIqlIrK446fB05HQiK4Ts72cZHCc+2Yc55MDhacDhaWDu+APmjz2i587FzXKlCAvLtxdBE1MmFRtH87lg/+B9dGOC0E9/g9bgJXFkDNPo9ox0u13A8hHj7BOdYtEBnreQLCd6QSULgtK8BOnTxg2s/u61zU1zZKoNgylO4ugI6rwH6/Ur2+4vtNCOSGEGIjbsn75JrykVmlJAwN+4xMyZuwTtQXR7BCFuRFypR+1YYH7wMYx1b9aROU8vmMLUWqMYavzUvnETQYB1r3VDgwwpjdIEAb8FXRfo7l0jevckamQJNXIw/AlKQa5vXug9l/Ld95IYH8aIFc+TIB+kjUr2t5Nlle6+ZRq67xNfqcP51TUkvxukcNWdG3O9B2O9F2O9jzs3e1nzOFDVnaf4ibiBe3e6OHlmBqcryheSl9Pq7tO2P29Zn4mKjDbeffdd3n333R3X29vb+fM///OS66mtreUv/uIvdlxva2vjX/7Lf1lSHeloFdnIJaxyvXilcwYhZklFfPCV9nGzBeeIFOaJfYG2di8/PJZKnarFDWgJA+7L9wg96yH0uB8EHdvABLItxeYVv50vbvSgJLYT1WJCoxQNRy6NeB9WiFlZvz/E1MVbqR8yFuWaS/cJjfgIPRqklOxWUJr2LBcOiyBPJ2Zx16SIitWWwL9uxWhMcmRwEeeFSbS4gfB4F9GpNvRkceeMzO/XbU0wbg0yd+wBbY9PYgmnsqPVz3Tz9MrXZQsLMbZFQEalSN5xIiBsO0KPn3yEafQII1KYbozbQrJloxTBXGj+jUhhRrJOKAbyOMnlQ7bNXqYdczWRJvF7Gd5LjNiQlhrxem24TqRMsUxtS0RGe1j6+ftItjDu9jVIGBh4chLTRp8ynY7Ti1yuTIGbJj8b77zz6DSJY6mwTPHVGoS4AXSB0Gwj2lgH9W99y5IcYmKseVs/jUaFuoYgggBqxIwWTyUFiH95hbS8GDiW2l1/88VRhpINCP1TOE8/I75aS8Kzdw5p+4VsMnFYjuoPKg5aJIv9RC5iandEOH95HMHnxvvVBZJ+Z8p8oggxzqWUKvUkJLPM4rnvMLctsX59iNalRlYKrNOL87WcOD2L2aIgitqO/pVzilbuRqVBFLlGaRleK8XeWjQ/R5Q6MCzfXUDpmKXZuJNg5zPJSF/XbGEWu8Zpd4dpbl0iseYmMtWGtXse0aQQetZLdLoNPSkj2cPoOmhxExFPLYHh46BJnCA3ASmVlJT6nJnaupqlNmwf1/Do3Q+3cWBdB/vADPaBGWILDfi+HUI0xzezYGUiX5sHZTdeDWx9AyGn4+LYSCtDai2GWh+2gSmsvbOEnvShrLtQQ1bSLzefUOsz6aw1LxJuXAPgafOnnPrgx8hJI1OmEEKRrHKloOD4UAzI8y0k2xZB0pk7+mCbxrAcFHJILEUDV6kgzSbx2RvLato679WRn2G1gUmvFefZB5vXnCdH0ZMyokHBcWKMBCDPtO8ws8kk7blMLHKdSAWcPsxA6GkP4dEemv4w5QA9N7OOu3XjSPTYAhNjzdjtUS4PzaFrEpIlhiBA8EkfWtzE6gdv7ngWhzOG9uwIQ8mUg11krJtIhWPqoKLQWM+H3ZoqvcTucZDtjgVBo71zjSNHF4g9PkJkMmX4UGq+gkLO4KWu1ZI1Ql3HIqsfvrp5Gpr/RELn3MUJZDnFmxzOGOte+7Y+Z/f1sHGDF5YcQ+kL7tGV9pzXM5G5GOqyQuzMA5SeaRxsmeka63xIlhix+SYC9wfRYubUQJHhWlMYNWTF+/mV1DVxnSGtfttiXqk2tRIbn1mfDfsvf4zm9hF5/RsAhIz13ty6St0732Bwhln7/BKK1112G/lwGLTGpfQx/d2UtVp812uxdM5jblvCefoZug7xhSai022cVywIgoYaU9AVmQHVvkm0LAHnZn3t908jahKPu0eJD45guXW+Ks9SUCjdPIWxoZWaa7dxnhzDcWKMNcNP2HHOnwe5YnhXA7sRpLm0psVQiNTvtUPgqGUdU50PybrdPsxxfJTIRGqRTAatKO51Hlz+mpM3Uw6UTltis3+l9Cn9Xk4kkzSvOwmP9FD/3tep+hWR0+e2+3m0tHk5c24KxW8nMtaOrkok1p3oOTbLaTQ0BkjIU8Tmm/OWeRFQzlhPn8bsNQ6jaUUa+6VBzk7M9dwJm6Bh7ZnljYEJYlEjwRvnUNZqKjqxK2RaVQpMzR6WVxzc0wIgb7WXa1x1dHkwWRI8ut+BIOj41rfmgyBo1NaFEDxu9CooeJ4XDm/PN/BM8jEj7wylUsokK8dJKFnrZbVrAmODF0OOqA9pLhGZaiX8rH9HPyLjuZMkZP6/GHIt+pnHKbmuZf6d/VxiwsTYmp221TrUhrUd7aWfs9UVxbTaVrBvz13IVBmVLDTRmTaiM22AjuQI0/DuN1h75lCjJtBBtMTRkzKJBydhvhsAa9DNqQ9+TLhujdnaVebf/R2i34Xt968jZRDnclHO90is1rH8t+/T9EcfIYg6HV0efviHd7l/twtVFWmZ7ScfUS62Mcv8vVxntULPUMq3Kef75Vqc0/3bK3MKzRjH9/ZnNNm3hztS4xKSSSXhqUWLGdE1Af/dEyheF3VvfstI/WLeOjPnfr4x7Fl10tE1iXruLpIlRchlw/aTM7/PQlv7GksLbppbfVi6Flj/OuX9LkhJZHcAyRoDXSA214yhxo/r/EMAjLWByl/Kc0aaPB0k28fvEw6To95ulQFK2zyN5++ghi0kdbA7I8iv3+LB3S6Y24qvvxfvI5dcGLCuo0eMO8oMJet3yBKjMYmmisxO15O9NtTWh7h4ZQwYY+WD1zDUBogvNO4od9Bx6MlxKcgexKWSYl3QSDYvoUlJ4ldub7OKFIJWTA9PYJxvRzckSHTOAAJWn5OJHbVuIXPAVWOXXwoJKibw5786T8exCZSjYxhCFuzeevxOP5o7tcgZZsoLM5eJbGF3WDQbu+ungBq0s/Lb19CiZraEgo79xCjGxpVNcjweF4icu0uybRHjk0GsX7zKeGhjWlZwIrCbTcrap1eZdi5x5oyRJw/bOX02pUlUjy+y+rvXN8vlOqrPR5LTpLhYCuBcz1goq121Fox8i3HOxaPMY8t8SHTOgCFJ7Ow9ZFIOd0m/ndh8M64LDzA1egEw1K1vJggSJBV0kdCTPgwX7/A//o//I/Wx7s0682mPc8mZrngqY2ZTi49Y1IDZoqBpIIqQTAIxC1ZRxNAY5Nb1fibGmjCZFXqbV7B0z2NqWCMZtiIakkjWGImeOTR7EDXgRI1YCD3ZGVYqjcNEfqphZ/6iKQ32A4dpjOwGqsuPaEwiiGEMsoaWFAGd9riLjpYEelJG8Tm4w9b7MDWvIJrjZYd+LQXBoJm2du+O62n5cVZwETn2iJbWdXRdwGhSkGQNNbndYW9t1cnw7W6Gzk8xWzvP+csTJDxuvF9eytluuUqSYdlDp2SElzbHhTGouqlNluaQl48UZ5tM6IJG6I9+s3ktsebCeXsIMeigP+JCMUeZdHswzqfMMQTFiGl8S1tMAYGYyxawXJQqPDK1dfkW8XSZgC2EBVDsUQKWeZpGBlncIMfx0w9RumewffIm0vpOp5piqYXz/f2iQ4tme+cL6AkDem0qBE563JnvnSLUtohmC28R4wyUYjZTjUVYjZixNCkIwLGTc5vXJWsM59BjorMtGwlqdmK337aYVrmU9M+VaKLzzaN89v2l1puvL0n3+mYsdL/fgiliI3DvGGgixkbPJjEOPurHcWKM1Y+v0vDudVwXHrD6m7eILzVi/9173JB/y5UroNOEgFACidM4b7IiOyMgpOSgJOn4wyamJxsYPJ5yJBY1mZinFskcA2eYi1fHgFRc9mDAwpzHwcz9EyQSKcfT5lYvdkcM31gd7YvFY9ofBtJTLdvUl8S4cnwfCLL58XFMzwaYsK7T8IOvEDfsd2uu3Adg3Wuj1hGl+X4nwws6Q8kGaq4OAxBbaCo7SlIxeFZdnDwzgywnSeYwhzBcvUVLzMqdW31IkkbAb+FUvGnz98w1YGmhlt8upBKfffSbM8gGlWM52izHYTDTn6IhT2bfauLQk+NOzUJN/oRO21BoAdHRCb/76aa2NA3zt+dwznZt/i2ZdKSwA6O38iPvfChHm1zIRqvURTw96H4kW3my3EKsIxV+zrnQyomEm/ThrdKdyqoXfuf3WG5cxDC3ZaNdbAE4jAKuWiQ+l4CPr9aiHpnmcc8I0lotQtSEblKILTSiaYUT1hQyUagG7MfHaeqfxWxpQ/E58N8+iSCp1L15E1OjB3PbEuHRboYykqHs1YankFZ5v5xqKo3EUrBcWCRtjSuvNuJ7MED6ZCHpT2lCln/5Nr2igfDAJM2nR1AByaSwabulbcmx9fZZaubbSWXI3N73ifp5aq7c4QemlFeEkCX+olEDtfUhnj5uZ/nXA8i2KIrfvpl8ZqxmllfffEIwaObr3x9jKNmAHUhkLYJpeEokNLshPt8H0vQSKezVt67G5qdaMkhQZXqD9Sw87sNxfHzz+p1bvawsuamtD3Dp6hjqsSWS0a0zaXPrCtGp9qqGTlUSMsGABXdNBM9qmt/o2Owx3O4ItXUh7txsJOC35vwu2dfS7amqtI1E54JmiiEoBgQtf/K3zHfu2KkPrToOPTkuBTop0iEgbIv9qjoCaI5givzFjWjuAOJaDVLAiWGqi4Gwm4nY9o+1m6x7aeQjwZUQjd04MmSaWxxd7OSpf5yYK8B6xxxLj0/xrnCVO92P8dr9m0mwdINC+PUvMd87nVPLmd2vtIDLziF/UFENspdZR7aAF00JdGOC+MnHAJhvn0GM2IjOtuAaesyXDxvRdaFoKL+9QHi0Cz0pEWnoZO2zlHAcUG3o/+WnjIoRGn/0OdbuOcIjvTnH3V4T5UoXpErfV652szcm5TzzULKeYdHHiek6OrrWsPVPEx7t2owGoykGdFVEskWZtXqpNaiojWuEnvQiGJKkSbQYN/M3f/M3/Ot//a+ZPnuHqTN3Mc50YP7u3La41q6zjxFNSaIzzdyfc2AxK0QiJkJBM2aLQji05VinJ40oWZqoUNCSM0rL9wmVmla81BpXB3u9GXqeTnmZ8iX8rI/ws77N0KHpue71OJmZqqez24MgK6zfGEKQVCydC3SMD1a9TwG/lZra0CY57uz2cPzULJ5VB0uLLuLx4qFK08gMNJDvO45IYXrrwkReTzkG23/5Y8SEaUe554EXhhxrphiCJqLJCpojiOr2I682EH31OropgRAzIUSs6IY4xskeTCMDqDU+QMc43ovgd2J+cHLbhylkO5wP+z3RqiU4JuIiR794i6WBpywNPuPJmx9j+PAP6JpuQj76kJUjqePV2PlhAJY7J+HxkQI1bmFY9jBE8Ygg+4lykq5UE4nlBuILjZjbVjCM9CItNjMpxjB3RBFNCs2t6yzO11atf+UsLlrMTOhJP7y6FRoMQNAkjkgmgiYFTAoIWkmag90gF/muJPtcNeZjIU1Toc1B9m/DsocrTUncXVvOr6JRQXaGMLctY+1OxQhOhqx010aIAeK6i9ZHZwAIZjyLIAgYp7qw/9rFbP0i7gsPmL03wICS0j5PtUzjtkX5/ccnicdSpNeX0bdwKL+GZq/xUvO7dzjMESvy4UU8LciOR7wdWVl+R5vp7PagA/GleuzHxlFjpj3hGvOzdVy8MsrosxZAQBQ14iu1fPdtN0PJ+oKpofMhV+z1TEysWTF+dR7n0GMCP/yIlV+/taMO2Rmkw6wj+Z0IMTO6OcFLm+MiSCcBaXnvc3TzVigkLSEREuM4TalIFrqggTGB+c4ZZG/KE9Q405mzzmKohifzbsO37QaFEkO0jBzD5q3F2zG7qWVve3qSuulunrz78WZZS8cioRzkOBdBeJ7Culjb1Ra6+U4EMtsJ3D/KbFCip32JgDWMea0Gx8kRAFZXdprr7BeRz+6nbkigugLUu0Is1K2kFBqKTE+Tn8mlvUnokM9OvdIY37vRDBWKBJONYic4fxZPmWZF6++QjngbHu+g/p3rkJQwzLUhPOvHMNlNT0OQ6Nl7AGg1fhKdM8Qu3abr/gmmx7eixvg0ndAf/I4awPg0RYx1UcX77ifUOsMsLrg3iXEhlDumqjGnK/GbKPfe3aIap4QvUR1U+5s/z5Bu5bQ1lKzH5FwGQDKqSLYoelJCNO5N3Gy/z4qmiVhtcc42KtiOLOP9+jxDSVfV2ti5ngkkVuvwfPQqiLnnW82r3+HzOUmsO7EPTqIbW4BXq9anXDj05DiNlS8uYmz0gA66JhFbrAfFRGS0r+o2itUMJr7fJDlfvzMXAaenCadnu42QKWpDnugg2TuLrookgzYEKYmuSsjuIEnfTlL3PIhxpe2Ve1+5gjpTuN9Jhhh6cgqh4wss7ctY2pc3yyWVwlOyHDOafIkx8iGzTOfgNMFjzzCFbazFzCRrvSmFhjHJTKj0o7VqId8GoZRsZdnXy7UbzheBoxykZYb5zlk0RxjN6acmYUX+7DWktTr6Nw6sxuMCYsiO5fololdvAmw68MlLTfQ64yQsPqamppAsW6ZKfrsf/+XrmJo9GAwpG+NsL/JqopBpWKlz4yBrBA9ywojvK6odC/l5fONy5cY9wwrvnX2E9+tzmJo8OE6OELh7HNuxcZ7aFzgaai25rvSczTd3Xe4wmgrRmIHX335MwuvczNJXDRTnA9v9KTKx+puUNlk0x3AcndzhP7EXeGHIsRq2Et3IKrPXAz5zcSzFi74UZB/BVtuWs5zoHZlIa8lb6wIke2cBECQNU9MaTT/9dLPc0s/f475xFbQtL9K9JMa7rbsSAVsNoZzZb+8359HiRhKn7tPQGODLz45XVE/hfunYHTFmTRP0Y0+ljBZAT4oIkoauiqgRK7omIBoVJEsMW3ucZPMKxz99D0ETePTeh0gr9ai160irdXRJBuQ90raUsqEqZL9W7twvJdh+rnpzeVAXw+b7+urcjvrGM/J/6NYI8ZOPADCM96D0pVJKh9/fmm//w/8wRU/nAInbbhS/Hal1mYmxJhqeXEENp7LoPazi/MvnbJMLpZDeUr5zrm+ZGe0nXxsHmXS/xO5xWL9vJfKyoTGAGrGQWKmnxR1hXVJT2SlXnNQ3BhiObWWyKwWZiT0gcx7rtHV46OxeQ1EEHj9op3b8KOhiWU5/pbZfCYZlDyRh6Ofvo3aL8Ee77k5BHHpynC9axW6Ow0oxm8iM31rt45nMHV7m39llSkUlxDj7HSysuZAXmkm2piJaJBIisagRhzOVUrbpjz/ifQE++yhl41htrfHzJtrVJMbputRQ6rs8ut+V955S683VP5M5wYXLY5gtCaIRE6aEGckUR7JFU85fipzS/DtD6KqInjBgUIzYwq9h+/QN5hCIXL6JYboDpSu1MVJbVhCGz6DaAxisAZS1vTGvqBTFCHI54dwK3Zc554v1o5wYz7qsEPrxhwBIS42oNeu0PzhNzXw741IU3RRjTlX5X1f/r/xf/k//CzNeM909CywtupmZamSGCENYN5/rINufZsu5crEfJOllMpCX2C3KNaPInLMms4K4YS4qxE04HSECwOJ8DX0DS8zN1JFO1V4J0u2dN1to6E75Qvi+vEStf8uMolxOk0/mFJqrpSgZh5L1m+VeRqsoAR0GnXgR58au5mDJ9U0vOUqKhww7ExyUq7UqVj47Q0329VJRrkYt34Iw9+0pROMg5ldv4nRFMRpjBB714TwxvnnM8dZ7D/nuRh/Dq6X3r9wj/0xUsviX+y7LOSZ+XsgcK5I9jLltifNtC4RDZsZGWjCZFNo1O4JBwXF8HElKgDlljx+ZbqHx9gVELSUOEn02BMLEjj8h2THP2u8vUbdBjgHCP/oIgJqEjP/uCeILhcP0lItytJS5kG8+lkuMC212i835ShO3DCRtrPz2dUCnP5TqWwDwutbxHH2MtWMFR0LiP/yH/0BbR4T+gSXuftfDylJum8BqEOS9JqAHXRNYjWQg+4mDvimqBqptYlEO9tMUQ9cFkgF76t+SSkDXkN1+HM4oDkeMy40q8YXi9RR6X2dx0/Dep/hunuTGsqFi++JSTvzy9aGQX0Gu991h2HtfgENPjrNRDhEudv/00nZvyDRpzhSUuZx2MlGp4M8cENmDqlxHllyDqzJhL6AlTEQ+fY2ZvsecPD2H80QqNuPykpOm5lSM6AtXxvnw10No2s5A3dUWZvshHKvpELdb4pcb6dA/OsFjDzhydCu9sNMVxWBMEosaMegqls5FdB0e3uvkSGMUc+sq1q5FJp+tbx7HdwtGVl79EnNzqm/Kuouln7+PZI3gPPMEU/MasYV6zK0eai7fw/PJVZKB3XsO5zsdKebtnA+lajwGVNuODXF6fhRKe11O/dnl8z3riBSGqJkjmFDa59DsYfw1HtSIBWp8QMqjvL29nUffLvHlZ8cIh7Ynm9lrsnmQiNdBJ9YvsbfY7fdPK472wimvGvV1dq+SmG9LhXtrWiG5VEvN1btYNhQSNZfvs/KbGrQiGsJ870iyRbANTBAa7SS22MRQnjjDxWRvOTKhFDmeqSVOY7+dYw89OW6uj0C8xCwgZSJNlPORZNi+iELhBbBcZBPkUgRBdkzWvcLcdCNz0w00NPk5eXqGxqYUMdb1VLKBnr4lxkdTzgJDyXoaNcsLsYgVmtjFNDZ78fwOZ4RX3ni64/r8bC1LCzV0hRqxHx/FGzZgC9Vh6VxEDVl5Zew8pKLz7RDigimxSYxXP3qFdGghNWJl/fp5AKYaprjSmipT+8ZNVn71zq6fJfP95NL6Vvv9lTo/shfOzAV1rxAbuofSM4MYtJOYaMfctkQwasJmT7C67OInP/kJw7/5GUd8pUVkeR6OsaWeCJUq23bb1m5w2LTH3ydUiyBXE9UgxleaFezWBM7JVDp2QZWwDU6iRiw03zkPd86zNHQHPYcSqlToSYlkwEFkvJPsEHK5kK0oqlSulMNjMvnWpvKyXgKqF0EjFw49Od4P5CPJkFubXAyVmF+kF+hKBEGuHVd1BL3A6rKb9fU1mlv8RGeasXSmbJJ7O9dxPDldhTb2DrshDPlIcjWPsUupJxiwcuOrAa68mgoF5/XYuXenm3g85ahh652hvsXDkRZgI+eh/f7Jzft1dDpOj7AWMhGZ6GJAtSE7UoJdWXfiODFKMmRFT0o4jo+TTIpEwiauuLYiJYiyimSNprSbJfQ71/gtJSRbGrvZgOY7qis2H3IR5Fz1lNuPfP1fnmuktmcGzRHC1LqMGjWjPennt/FITufdSglyvm9x2JJYPM9j9pd4/jhIJwjVmDs9fUs4etZYv34OdzR1oicGHUhti4xO1OOVIqmCD45W3Ia1d4bIZDuR8Z0+L9kyIFt+7KVDe1qmZvOW3VoFlIuX5LgMZH+cTLK8+SHj+TXImUenlSyqpRz/ZNZd6BiiElKfD4/uddHccp/4at0mOY4tVu4kcJhQaCed+Xc5wiQt6EsxwbA7I5vEeH6ulgd3u8jUAExNNDE10URjk49zlyZQYwZ0awxdVBE0iWTzMkrfFI23ziI0+5gIh+gJNBMe60TXRLS4MRXFwpYiw7Ks4XTtzHYomuKoEUtFmvNiDqP5xmipxDi73UoJbaUh3Aoh3ztJeGpZ/ru3EE0JjHXruM49xtKxxA+BpZ+/v63sbkOqldO/vdBA59Mex4+ModmDcH8g772CqNHQGKC+IUBnd6pvxyYbYHj3zz4ihTkiGPG2LxCq8ZLw1CAoBtBBXmxB0CvX2L1E9bHbMV+NTWE1iPHV9iiO/lWcn7zJhaSNdFLpgNMHC430DSziX+qszJRN0Ki5cpfobCvKuisVvSgDuU6ey4nIk41i92SvlweFGMNLcrwrdDUHc5pcjMeFzY+8F4tJKQT5R7K1oP10NaFsxOZ1n3+E9/oQxhp/KtPaAUc5Gtpy6qwkvnA5joGClIRLt2lq9m/+9mC4i/nZurz3rq44UaMmBDlJ/Ox94mfvYxzpQ5OTmL8bYtUaxHFiDMdKLXqigy+fbPV7KFmPodaHsd6LbIsRmWojNttM7Wup+LvKuoNbwSRUiRiXgt04Ula6Oa02MS4GXTGgKgaiIRvR6TYafvgFWtyIZIuUdH8uX4WDZC+cD5I9jPP0U+JNKe95lyGO//apzd/tjihHBhew2uI4nDFCQTN2R2zzd9+6jd26iJrblrB0LhByB1AjNlwrzfi6Z9DlJJo9hPnuGYzTu4sy8xLVRymnI3ulZa4GMZYcIRynRnB8+iZi2L5JjAFkZwjRFEc0aNS/c53lv3sLXSkx5ryggS4w0zhNc/Ma4bHuFDnO8QzFwlaWinLe8UEjxvACkOMlj5X4Uvn3VeuF5yPIwKYWORdZKrRA53MOykS+AZvLuShfP4uhFK1XtjCqvTq8Q7N10FEtklxMg5e9qSkWri8XLJ1zuM4/BsCz4uDp43ZCQUuRu0DXRVZ/9xrNf7yV5TAxkBK9QWMUR/sKAKZGL1rT8rZ7h2UPQ956PB++RqZWeulv38HgDpIM2hhKGvK+g0oWolxjPvuYL7N/5bRTLjEuthHdTd2lQ8Dz6VXcFx7Q8P5X6Pr/seQ7KxlnojnGRauM4k07e5Y/P8ohIUPJeu4ZV5hpnuDsiUWUNffmb5bORSydi4Se9HJzxsbbbz4B4Ob1frq87egJA/Y/SUVQufHVAL51e2XkWNQQTXFcQ08wNXuQ1mowf/UKks9NGLBxFB2dyJtfVlL7SzwnlDJud2t3XCpxLDQnBGMC98X7mEeOMBB3bCPGQCrpj6gTGD6Kc+gpgqSWQI51HKeeYeuf4enjNrzjTQXX53LewW5lfa6IFDnti3NgyWOlo+SWKsOhJ8eVohhRLIc85yOefSYd4vlNIXJNyHI9MncYrsv749GZHUkg8GAAY60f//CxfWl/L1ApSS5GOMo5/s9XVrREafxhalHWNYEPPzhT1BFDEDRMpiTtXR5s9hjNrXdylrNsEOM0/D4rqQBi2U5y9u03qsCqMy9pLYRShXApi85ekOJKtUC71R4VIvp6wkh0thlT0xr//t//e1bE2qrUmwnJGiHwgw9p3Pg7MtWK4ncQnegsua1KcaU1juvsGIEHA0TGuggMH0eu9VH/xi0AphWF49EmSDwlvu7kdLcf89UxFH9qXAbuD26M3cpQ/9Z1ZGcYyVOL8dsLGGY70EgFVFVaFlAbV0k2eNDcAUwPTuz+gV/iQKGUuZtLhpRDjNP/z56Hhhof7kv3scy3YXp8lPEMJYQmJ4idekTC6yKxWktkspOEpwYtZs7ZjmBM4Dg+hrVnjsCjfmz9M8RXanGPHGMojylQJRuD9HpVavxiYJvf1J/FUycvmbylkOlqJkz7cGjzvSXHxZD+KKWS5HwfNVuLDIUn03hc2EGQS3Woy3VvpSh3kZfMcZJBK3rCWJX2DxOynaLy2WlmC5JydtlpYqz4bax9fgld8OcpqSMIOqKkc2pomuYWX946x0eacboiNGxEGrl/t5OFuTr+4T+WC0aOyEapx27lHs+VMwar7ZC1305ppTq7tIZqiQBfffUVzY0/raidQpuwtG2557PL1Fy+h7U7FURVNCZgrPyEL6Vqj2te+Q5To5fYYgORse7N62rIxuJ8DbV1QZyuCMbGNeJ+O6YNkwsAgytEwusiOtWGLvjKajcNa980kiOM5evLyEvNCLqI6ggQ/sEnyNNtJLvmCY10U7Najy6pCLuIEPASzx/VMq2ohowwtS7jOvuIwINBVmfaQNpuNtXrjKE2eDA5Q+AIMasmmJlqAHkrrWb6WQx169S9fmvzuuKpYflXb6MnC1O9SjXnlRLjQprivTQBLQcvJDnOJI67JYuZH6pcbXLm/ZkOcNU4es3nsFQOQa5maCLJGiO+Wrom6yCjXNvMXPGnM8lVubZXudpe/vWb6AkDm2YNOWauKGq8/wfDm38rCYmF+Rpa29YBGB9tQhJ1uvtSmmKLNc7SopsHw10kEunjOZ1G3QzEqmYikM+GLR8qXXD2KtRbvvjl1WgznylOvrqX6pZxAv/T//Q/8f/4v/+iojYfWZYYOj2DyawgLQSJzbRuxkkdFQNcBu5GY5hu9HKuLYJtYArHsUmcq0YC/uqbjQiygqnRixo1EXy43flOTxgRbl5EOTZGx9EJ1KYgkckOgveOoiVltJiJ1e4RevqXqXv7OkcWnUyMNqOqueO15u6AhvP0M5SADcNi6+ZlMWRHDNhJds0DoEYsWMaOwr3iVR7GiB8vsXenS/nWE+uRSeyDE6xfP4uytnP9HFBt4LWxcmOIureuE/jqEt1Xv6O+McDsdD0+r52+gSVk+yj1jVv8pFjs+WwZU02ZnE+7nkmMDzIpTuPQk+NZRSAYz/97OQSwGKksV5ucLju95Nj8fzEtcLlkPvv5ct2/H4NOsodRp9r2vJ2DiFy2U/nSA2cjn9DJ1mwU1sjrvPHOIyzWVMa7uMdNfKmeyGgvIqC8dR3RqNDhTiA5wni/Os93ARVN3a79GkrW0/wnH2JvczGgbmkJSz25qNRxo1op1/P1p5S2C5XN9VzZJjjl2tqlUVsXpLXdSzRqJBoxIkkamioSCpl5HFE4bbChJ2X0pJRyxrNGCY10Y7fb89YvCDoNTX786zaSqoiaTBFFmz1Kb/8ydfVBVlecLMzV0taxQt3Rcb767DixmBHBa2dlycnrbz9ibKSFr0bqGbKv09zm59zFCR496MDrsaOq1Vs6DO4gybCZ2g9+xGqeMH1DT/qILzWkTqeShu1lZutpmBwk0eDF3jfGtTeeMvxdD8OBErWDuojn0yvUv32DyJVvsd64DICgi1huXCJ+6hEkZdrHBvckJu5e4LA4Xz5P5NIej0hhuskta0tJcZyvnZ3XVmnvXOPIkQW++mKQSERjKMe96fHW3rqG31ODGrQT/ORV/ANP6B9YwuXe0jIr/lTITe+Xl0AvvDksZV5kr2GV+lek78s0oyjVfCIfZhXhpc3xfiK94Fc7E0t6IJSSVKRU5CIn+7kbyyQMkjWCwRUiGTz4i8Z+oxJinMvZrBD6BxexWBMEH/cRftZLdjB3380zuM49RI2a8d06jRY3oeXJ0Gb/5Y+R62qAlNAtFg4wH0nOZ2Of/v2goFSNSb5y5WqO09/T6Qpz+uw0dkeMmal6bLYYDmcEXRMRJY1eewyzJQFxU8qkQdTRVRF0Ee+XF/LW73BGGLowiShqWCwKAMtLLuIxA63tXqbGG7n7XS9+nxWDUSUaNaKqIv2DCzy8142uC9y51U9NXZCTZ2ZIKhLDd/oYGrYRP/6EcxcnEASIhI18+dkJdL005UO+jICGOi+1r31HZKK9YPzqYXkNgmw6fu6EQGK1DlbrmOp7wqVrI6wsuTFMSCje4uYgvd4WAoBN0unNGNdSwIX51nlCP/0N8ZgZnnQzwuEgyC+xNyiHGD8wLfPWO48wmZJ89dkxQiEzNbUh+gaWsNljhK6fYyDg3nZP5tpq6ZwneHQS1aAQvZOyc9dVmYnRFlaXXdhscYYuTAIQX65H8bmKEuNs5BvL5Z74ZSNbdqbXkkqDIWSuNfuhY35JjnOgmGlCWhP8vFCKtng/YWxcI+Fxo0WLR004LCg3vFo28gmStCYgX9SKTJTSviBo9A8scf9uFwtzLpDXdhaKA9d7N/4Igrw1dnccq6mQL1NS9rjL/jvfopF9fT+Om/fKJKRaOOEQsTtirH1+EeMGecvv/Z2a35I1hho1QYH4uj19y3hWHJiGz+IXdCRLDEvrCjajgu+Lfux+J3bA1LRKzbX7WXcLPH7QgaaJrK85GHvWQu+RJVZXnQwrYXjYicGQpLXdh9WW4Ac/ucu9O10sztdSKLtW3nEsatgvDzM+2oTt4fEcz73z/mJzYlj2wHQDy0tuOrtXabp2m9h4F6EnRzZ/z2dPb3p0lGDHPGNJFWFjaRyRwqCC6foQjpMjNB8Zx/fdSZg6UrAfL/FiohzZFTj2gHcHF0lunNy8+tYTkkmRZFIi8awX/3Q7ZJj/ZMuaXneUyPlH6IC67sRYnzI9SvqcnDg9Q2Ozj2jEyOJ8TeoUaKGvYH9ynXBWG9kKkwHVxttdgV3V+bwyU74w5LhcrVT28Vih47JcZg/ZGtlqRLcoFYXCnWTWW6iNQgOuVMeq9L+TvgaCRydA1OClowpQ2nisRhSLpg2Hu9NnpwFYmNse67hQBI5MxwjYGlduaf83XNVykNlLjV4xT/VS7EzTz9g+YSF49gnNHatYVts302Pn/s7p9N3FN5+iqNPeHGQVAXQBNWLddHAblj2bsah/eO1uqk5V4OvPj3HSbKL92l2crgjDt3uIhM2bjnCvvvGE6akG1KTI5Hgz9+/2csEhIzvCnDn/iPYOLw/udRGLlu6MOyx7OCs6MZmSNGlWQjnKDKg2dFHFWCNgbPCCoLOgxbFaE0xPNhAMWMgXZi4RNzD2rJXF+VquvPqU6xPOTTvk7JCamxhtp90VIHrlJpYblxBUefObxpcaiS81UvPKdzhOjBLuWkRebME08pIkH2aUInfKNRcbllc5NTTNQIeX+blaHg53ousiRqPCKdGJGrLujPyTBc0aRh96sPm3wRHGttqI/dpdkppAXNf58tPjJDMc7VafoxlN5nqRti/+7+gsKXrWQbM1TuPQk+MZMcqqpG3+XY5NWC6NVvrefNrjfNfL1SbvliAXQ7VjGheCtFYLuoCpyUN8sbH4DS8ICiWZyPVv2E6wSk0Ykv7toXkZXQezRcFiiWMyK5wamtksd/TEHI1TgwXjT+ayka0WEa7U5jiNg5QCtlRUmumyz6Rzl9TcSb+vyjPd6dgdUbp7V2lqChB8sOXUlm9T5fdZcbkjrCy5iITN3AzD0M/fI3jsIVdeGWFivAmf18bjBx3U1IVoaV2no2uNaNTAV58dJ7leT3Ldze8WDVx97RnHT85y51ZhzVUmhpL16MDaFxepuXIXNWJB8TlQIxb6VCuCYmBu8BG2/mkcCQOJ1Vr0pIzRmMTpivDKG09RVYFnj9tZ89gJh3ZuHARBw+6IIoo6BoNa3ElPF7HcOk/0yk0ir32N9YtXEbTt98j2CJIlztqjI7jPPEVeaUDyuUt+7pc4nChHpnX3ruB0Rfjkt6c3E2QBHI+0oJIj/GoG0vxC18ysTXczWzMMgKAYMI72YXp4nGTrIr44nIzlNhfKN+erLVsLrRv/HZ0l8aFiPCWfEm9GjHJtj40rDj05zoVcR9eZKMUOdEC15f0w1QqZVoggl2L/XGkGvN0cU+R6dwICojmeysLzAqGQxrZSQZM9JrMJcj4Myx7efOsxZrOCroOw8Qnjy7VEjVEcZpXYSHfBvmUS40LZiOw2Y8Wbt+cZaWIvtMa5Ntv5nC0LPXuuBbF+socphw+o/Nl7+pYYPL5ALGpgZdnF7z85QTxuYIj846nvyAKJROqEp6XNh/BdLWhiqvxoC+teOz39y3T1rKBrAk8edvDofifLS256+5d46/0HLMzNY3l2lDORRjwri4hSrrmfek6nK0JL2zpOZ4REQsbmbSQ2b0M0JLEfHUc0JnGdfbx5V1BNab31kAn/d6dIeLa8+J9uPJPBqNDatk5zyzqDx+dIKhK+dRtmSwKzRSEYsGB3xFASEndv9RGLbddq59t0jBKHb0/R9upd4sefYHq4Fc94QLWRvH2OyGvfIBoVlKiJ8LufYfvte0ihwprAlzi4yDUWLD2zGGt91HiaGJkurvBJzzVB0DldE8a3budEtDlv/ZB7bU9fm4hJdM+3s9I/RtweQrfEqG1bwTfWjWGuHaWArClVE74b5OMlHyQjqfqrkG8hH08ZkcI0sPcn1C8kOYbyslpVC+WEfStGPEoh39XSPJf6PvJNKs0SQRD1nOkoDztyEeRqp0IuBZKUxGxOOVilifGdW72sLLkZStYTK3BvGmlv4UKYXnKQK71BsQ1VuV781dAS71ZTnQuFHAlLLZ+NfO9l3RJB9ZcfOzgNs1lh8PgCE2ONjDxp3/bbI8sSKBK5bIF1XaChMcQ3Xxzl4pVRFlonaJ3r3xzr3jUH3jUHDmeYS9fGOH95nInRJkZHWgj6rVx9/Qmd3R7o/grfzVPUN4XwRnYuJe/+6B6yvEWakxETqi5jPD6K89RIzmdS/HbWPruCaEpwVw2i67m9+JWEgenJRqYnGxEEHZs9itMVJR4zEIsZcDhiRCJGAn5rzncABSKN6CJLt4/T8IMvSRwdpff3ryF7UmVaJZExwNTkQbZHiC00sBSFAV7iRYBgUDA2rmHrm8EccBI7N4wheAnF694okS9jZOr05vipORwyBO4OoOwokz8T3KfTzk0Z2mfS6TPpPHV7iJujGCa6UTrmmG+ZxzrWhYBQseyrJjFOKwkz/0+Ow5lcUb4OqilFJl4YclzthbLYYl9Ms/u8nfZyoVhK3nKQ+X76MDLqraHtyCyz97+fy8RuhE5+DbVOa7uX02enefwgRX4mxxp49qRj272ZfSh0WpIrNnauU5BA2MhsjogqhQhyuXFd8zkgFgsqn6uNfO9eF1XQBYQCDmxp7FdM2vS7HlOTqPUeEg9LN0XIxOTkJI3Nfn72v/WxupLalDa1rHP2wiTJpIAs64yNNDP2rHXHvfNzdQwcWyQcNqFpAvFY7ggQwYCNT357hks1Al3nH9LU4mPda0cMORiZMjNwdBFL9zz2Z4PIF+/wSr+Pr8fcefs8veCir38FXU2No/BYJ4KooUYt6KpIbKFx06lXi5nR5Z2WyLnmiq4LhIJWQsGt7Hi5zCwy60gjn3ZdMCiQMGCc7kTYCKPYZ9IJbzhXmZrWWPr5exRyRHyJwwX7sVGs/TMI0hv4vj1NzSt3UFdrQAfHyWfIriCmRi8LczU8fdwO8dS8sVjjDJ2fxGaPMTXeSOLZCVClvMQ4G9NLjpzyR63zgqxiuXMW8/Bp1n/wEab6tc2NWnad1QozWEzWw/b1I/3/7GfIR4BLIcbPywkvEy8MOc6HfKS5lOPyUpyqCpHkfHGRD8OuKRuFwrqImkTn/SFGr30NLzg53gub2Fxj0WhUePsHWw4Zx0/NAWAbG2QomTtFbqnEGPJv6tJmFbmQqTHYCxSLVVxq7Og0kk0raM4ApmeDO357HhEzMt957PQDkFW0SO4UsMXw61//mmDATNtCH532MJO2Fbq6V4GUfaLnyyFcCSNDSeuO8SWJKW3uwNF5TOYkvf5WCp1T3VzX4ePjXH3tGRZLAmN9kMBYP19/fpSBeD31oVpiF+9gH5ygS21nbrYONSkxNdFI/8ASiXUHX99u56RWQzDuxHFiDEhFuVn75JWKnr9SZI+x3PNZx3X2EbKnDvO906nvtvHtjltk0jpvU8sK8cUm4GCGKaw2XsTYyQ2NfuobAsSYpbHNh+fja8SbGzC330KyxJEscWqu3iW20Eh8uZ6lkEhH9xp2R4xvvjgGQG9/Kt7wh78eQtNEENa3MatM589yxoe0VoMQN26MLZ06RWbB6qczjxlWtcZepfI9rbwopDCsRvsDqg3HPlhwHnpy3KlZqFG3/i4Us6+Q40sauY7aSgl+XSiRx/Miw5VEpMj1/IUmXfpZB+wwImh0d65gnOx5obJDFcshvxuhlGss1jcEuHBlbMf1ld+8QX8kdxbCUjWq2bbE2fGwp5ccdISNQCJnO6WGb6sWCoWHK/beZU8dLG/ZC2qmGImeSeLHnuKYaUOPGzdDfGWjVFOafChEjAGU3lR0EdkZQll3F6wrV186OztJJMaQnQK1r92iKWzD/sl7qM4Ak8lk3nEyLHvQEYjHZbp6PATuD6Ir+WIHZ0JAVQXqGlLjRZQ0+rydqICgSTj+y09ZufwNx07O0djs4+FwF/0DSwAYa4JcuDSBWTew9vtLm+TY4Ewt+vulfS31G8rOEIaaIMYHp3NHBoobUaImLF3zm+Q4jcOSJOQlQJJUzp6bIrbQhK7IrF/v3Ty5MDWnNprJgI21zy+jJ+WNeWintj6E0xVFEDV0TWR2uh53TZhLV0e59W3/ZtKdQsg+sfuRvKHw2LDV1dFZ7x0nOZk6MbT0zCKaEnQs7nXqiy1kK0MKKUfScnrzOfJgrwMRVBOHnhyXgzRBLmUHXCjzVTENVrUc9nKh1LrLJcb53kexeL1piLrI6yPn+P35m8TOD9MMRCbbCQwfz3n/YcN+RlHo6l3ZcS0ZtOYlPLBz85cvGsVuBNN+E+NCKIWACMp2DfiEIURt7xSyBEm/DV1JpUyWnUF0VeJ2PJKrml33K9d8bRzvZ6VvDFPrSlFynI2njnn+t//tc072vEEypLJ+fYh2Us8iBZz05+nTpuyLeFj/zdt56+/oWqWlzUs0YiISNmF3xGjZSEEO4P36HK0r25UHA6oNS5sHXYe6+hBvvPtoW50OZxx5qpF+0bgtbJtoiR28+OiCTjJkRV5pZJyd4Tvty28z3zpNXaMfX47bDwpBrramNy1XXhTt8alEA1rchGyLoEYsaOnIEoKO/84JdMWwI3xiXYMfhzOGpgrU1gUJh80E/Da++eIYQxcmODK4yNNH7Tla24lcCrURQ4jwDz/GeP8EBncQ/63TmNsXcZ5+hvXW+R0yrdoo5CRYCMOyJ+XTUsAR76CZmRbD94ocw3aCDKXHms2HfMdp1UzUkS/5QqlpfTP7mYliz15KSt00WU/ZWMORxS5GW1JaMS1RikbqcGMvFsE7N3v5wU+Gt11zfVX4+Dnz+2am6cxEPuG0n1Ep9hvb+hm14Pnd61kldOrfuQ6A8cNTJOKGijZCpZLiNNoen8RTs4ay5k5dEDTEjdTR+aHT0eXhxOlZBgZOYVtoBG0FxVvLJKU7hW0+n6BhqAmghi1ocdOmPBg8No9s0FjTBZKKRCSSWpATHjfh8S5uesnpJBca6cY+MAXAvdvdnDgzs+mQl0wK0D1LuNaL2esiYQujmZJYu+YJPU3R+b0M5VdqvQOqDd1vImhUiJ25hxhwMjbVRb9xy7ZyTNJYFSPUFajnsMyP7yvS42HtsysYGz3Yj41j658mGbBj7Qvgfv0mibkWtISBpM9BbL6FoWQ9BiU1DkRJ5+KVcYDNkG2PH3Rw9dVn+H0WFufrMBoVjCYF1vN2YxPPHGskW5aQ1lIOutEjo/jvnkBXZSRbhOhUG8sLtdvmeCm2weWgEo6Sjmn8Z/GuovcfFlvjNL535Bh2alKyUUhrnA+lOvDlQ7n2nLmcq3L1KRO7yfhWSj+mlxzYl85ied1H1OWneWwAUbW+XCjKhMu9XYNp+907SOHcoaLynWKUkqqz0vB/u4ljDIXHWKZ2qhKiVErfsueB8HdDNDX7OGO0oITdZbWXb84XWyh0QUOrXcd92UfS78BQE6AZmJ6s58nDTgBM5sRmhBJJ0mhs9tHeGMZ/9xj/5N/9E/4//8+vtz3TsOzB6Ypgscbpt6SOdtMRZCxd89SYEsj2CFPzLiRJp7V9DaMxZZPm91m5OtVJbLaF9a8uYm724OidxmhKIi6lTAfWb5xFVww5iTFAy/0hxsc7qbkyzJnzU0SjBmRZ4/7dLgY6vcjmOLqcQNdFWp4eY/70A+zHJrAfm+DG1wOIvpR9tMMZ4aRDxBnSNqJNbEG0RHHbQ0QjJhqbfczP1qXsPPOgkjEkqDK2z1/jXudjuvpXsDStMPJ0kCMxB132JA2uEHP9HuL2MAM3LgMvyfBhQfZ40JMy8YVmZHsE+7FxBFEnvvhTfJ9fxnXx/obpDyz9vBkQUDx1LP38fR6Ylqlv8nP2/BQ9/cuMPGkjHjMyfLuHc5fGWZyvxWBMIsvajqQzO/woBI3mN78EQPE5EKMm1q+fJel3AiCIOkjapu9ItjPcXvuCZKPPpPNBMrVGDai2lCnFc87Suxf4XpLjNPJ5v+8mhm2lmsRyBnahHWO5hLgSIlIsWUr/9Vd4+NbHJFsXMY5X5o3/fYZv3c7nH5/YPJrOTkSQixD3mfRtR1q7DSWYxm5Icb6xl48kZ8d7zleuWKa6UvuRhq6LdETqeaSFGMBdsGyhPqRRyNlxs010gtevsRiTmbf6uO0VeO3CDF09HuyzPdiPjiPV+ohFjQiCjkmX0eJGXN9epHa9lubmZhA11o88prl1nddNCkZTkmRSIhwyIQZSXvauc4/QkhKJ1TqSAXuKiBvCJOIyt7/tJxiwcP7yGHX1IYKGJOb2JRSvm/DTPlqfHme2bQrrqRESHjf9MRcCO73S0xAQ6A/XkXxwisjrX2OxpIj94kINR+qjSEEnaoOHpDnG4onH20yNr7wywuqKk8mxJs6cmyQWM3DeohAJm1C+TW0MTY1rOE49w6kKmDfq9vusBPy5v8VutNCS38XYSCuOURcdR2aJXLvOsCWGoAuYAy4SlihQfe3dS+wdCo2H8Egv0dkWAKT/c5T6d66T8DqJzrQQW2wk2y5eVSWODC4CqTGYhm/dhtGoYjCoOSKm6BibPIwsZ3IPHWt/6qQ14XURHu0ivtQAGzJfkJNY+6ZxfP76plzJHnPpNXg3JLkcrfFmLOMy7zts+F6T473AftiblRqSrdQEFoUyvZXyLNkEWVaMtIwfYXFgDHm+lYHY87e/2wtU4zvns+E7fmp289/BH37I8i/ez9t+n0nfdHSoRlYi2J0WIp8z214cm1dDYzeUrEdZgwHy23SXilyLRa5vIiDg9DSyGheI+8ycBNa/aaD5Dz6j7vVbiOsulj5+ZdMeN8jW956oXeR//p//Z4ydAeoaAkyNNxKJmEjEZRIJA7oubD5X8P6xHW0bgccZ30ie60C1ThKZakdPbNk0jhLHXuMnOtdE6OFRvFJqUcxWKmTPA3mlEcd/+Snz/c+wdM9hs0UJ3D9OU7SGROcMfev1aAaVqMvH1PH7YEmw/JvX0Yfuc+zkLN41Ow/vdXEq0YDjxCg1P/oCTZFIBm0EHx7h5pKBK10RhK7ZqhLj7OdIZ/EzPziF+cEpuq0JJqPypolFGn0mHeLlhTJ8if1DqWNBi5px/+j3yM7zjI80Y3t0umCdd2/18tpbTwgFtkiwKKXmv8WaQPFv0ath2cN5iwlL2xKJlVom6xcw28Mg6NgHJln96BX6/I14M8aQYExgrPUhGlSEqLmgv1GmFrnamuS0/MqMxZxu60XG954clxuftRQUi26xm8QBxe4vlDq4WiiU/jI9kbrCzXyzFGfl/U+wf/Q2bv3FIsjF7LF3N6Z0GpoCm38JOWRcrvBslZhQ5BKgDnX73/k2XqXEi81EMXOKQr8Vih5Tap27mQelbITKWSymlxzb3v2AakNXddZjJiRLFMUSZb1znDbZiGhUEASd6fkmjI1rODvnMRj+AJ/Xxp2bfRRKcpHvmTOvx2aS0D2FfXCC4IOjm/cCXFxsRDQqm2XzJUnJfj+CJtE+chx1qZ1X3/8EeEYAEL1uHtXcpvPeWWaG7lI31UXLs+M4I2b45i1GpDACcGqjnvBYF25BxFDjJ1LnxVgbwPbFIDNqnJb4zuWr0m9cSEann28qYtx403mclg4gQd4Lp7zDgnLHguPUMzRVYGGultFnCSiymT/tSo2GREJGklTMlgQOZ8rcIBrd7jhX3+DHdW4Ko1HF0rVIMmxBlJOIJoXweCc9ig19Y1xJ9jDm1uXNqC4Ak+YAfXFzUS1xtU8x0kqXD5IRfmTS6cNacva7UrL8HmS8UOQ43xFXsZ1Uuce0lWI3CTdyoRSTiTTKJWy54suWQhAytZfXvF187FonfvIxSK9yRLUyKu0+IsCLhuwFrK3du/nvZNhEdLJz2/fLJsbVdLQrBaWku64mKiU81SJKpaDUBSDXN0l/W1PLCg5N4He/GaKhMUBjs49VJYlBTOJ0RdHtAdbDJsa+OMovfvH3+Rd//v+jUBi0kp9f0lLHwA4f2aHVbvk1hpJN+e7cRD4ZIYZsmB4dQ+mcQXOE0Wp9ACSsEU5++EPkuAmBDI1YfLvccZwcQelYIk3PA/eOEvBbOSpbkTPG335Gk8lENgHYC2XLQcNhiFhR7niQHSFs/TOsf3uaRI9cvA5Bw3H6GQDv/PD+5uVkUmRqop6kkrKDlw1J+o4s0daxxv273YSCZuobAvQ3hfEkdCxT/ehJidCPPwTAMdaJrX9mW1PeLy+ghrbmRaYGuZhZT6lmP/m00mlinJ0SuhDPyvd3sTYPmnnSC0WO873cTAFW6geoRNubD5WkeCx2T6XEuBzhXckzZxO1+qkeps4Mk+idJPzOU7rHepledKOrW0Ovmu/6MEOWk5w5P0VDYwC/z4JVFFj77CoDSsoxI/s4K1/MyL1MK17OoljqAnVQFtpKzWQq8dLOlkOCQcFx6inBh4OAwOqKazPz3V5CNMeoe+sG8ZVa5leNNLNlkpFrE1Qu6RA0CdOTo5ieHE1lLNREBAQiQJoCZL6/TEXGgGpDu3+GRMyGV1eJjHeixcwMAebWR4QrjCyyW+QiEtmnAN9XGfa8Uel4SIasBB4MYDnzhPrG2qL1DEte3kwKmDeWsUjYyORYA5KscvTEEqNPW2lsWefM2ZQ9sa7D6bMTGI2pcaNp0C5CWFnavr8VdEaftiCKGrXe5pT9cY4Mn9lmFMW4TxqFTCVzaXrH4wI/MqW0xZkx8Su1tc9Hwg8aMYYXjBwXw24+wH4SuHR4FMhtr1mO7Wa5xHi3drTZdq/O1SZsPheSYkDy1BA//ZCmiwm8X58jsVKfs71qpwLfa1TDzryrZ4X+wUUMBhXDRDcdd84CbIaLKmRCUa3MQ2n4BGVX776cBaqcTV6lKMVEqdzvV62jwgn3EsGecdo61pifr+HJSvFU11WFLiDISSztSwzoLQQylFa5NITFIo4UmgvZjqXp8pna4uxFV4xaMd87TWYS7DGrF2PbIuFPrxV7upJQydwtJYvXYZFfLwJ2LSd0kchYN9GpNmyvx5FsCmo4NS7yrUe3rh+htW2NxYXazdTlNluUDofO+cvj1NSG0TUBRREwGLVNYqwkJAxGFSFgI+r04XBGWfv8Et2rrTgRaNlo61vZw1AGMc6eW/k2ldnI3nDmepbMerM10/nWmnJkYLYDYaE+HhSUTY79fj9/+7d/y8TEBGazmStXrvDmm28CMD8/z89//nMWFxdxOp28++67nD9/HoBQKMR/+k//Cb/fz9//+3+frq4uvF4v/+pf/Sv+6I/+iKtXr25r5y/+4i949913uXDhwu6fcg9QTeFXiuNcvn8XQnYfC2UIzAw1UypKSRMpAQM3XsN2WUcKBEmGfEhrNdRdeIjl24uw8mLZIpcPHVPLKsdOzmG5fgnDfFtJd+2FyUT623dTWaD5amvx9tpGuJxymcgc9wlzhKQxgTXgLvn+9AIQ7x/HefwRlpk2bn4zQCi4/8kwtLiJtU+v4b58F2vXIqIxSXQqNQZ1TeSsz8ldLVCkli0IBoWppilazTqi34m0VouQw/Qjl00vlJa8yDEwyeJCDY8SEYYonJGrGEr9/vk2US+jVTx/VFPu6EkDyYAB+9EHNN28UrDs0ePzNDQFiMVMhIJWBEGnqXUdW+cyNiAek0lMduE4Nr55TyIhIYo6hvEejE8GsBx9wt2RFrq97m3zZEC1Ibv9CJKXW35t8zmr5fBfrI5MgpxWeJWrhDmIhLcclEWONU3jr/7qr6ipqeGf//N/zvr6On/913+N2+3m5MmT/NVf/RVHjx7lz/7szxgfH+dnP/sZLS0ttLa28s0333DkyBG6u7v54IMP+Gf/7J9t1vvRRx9x+vRpbLa9JUrVFmS57HIrwV4cDaaJcTpAdymps4vVtxuYQnYM052otevIi014wmbiJx9Q/+k7OcsfJq1Lpe/GfnQM+7EJAMy3znHU07qn8SJ1KUmycRXDYsuO38qND5ydRGc3dsG7cSLdz0xkuTaDi0ef4O2YZehXf5STBObDTM8IjsExvv1qkHDYXM1ulg01bGXt01cQLVHMrSvYBidAFxEkFdke5s3leuKrdUQn2wEhtwwRNOzHxjH3TUPEwnrAgXVwBFnUGPzyTQwbzkS7nteiiqVrgckvS015srcotJ4cthOww4i9WDuTQRvmjkWUuXkMC/mVFXdv9yIIOqdiTdQBix2jDBxdIuA34XTFMZmTfDNn4c1BAXmxCbVtidDNIRKrdYDOgGqlZfg8wY3xkR0BRg1ZEWSVoWRKPqRPjDfLxXc6ZedCIbPSXGMzV5jWSiIc7cbW+SCgLHK8trbG/Pw8//Af/kMcDgcNDQ2cPn2aJ0+eYLPZiEaj/OQnP8FgMNDY2Mj9+/e5d+8era2tKIqC2+3G5XKRTCa31avrOh9++CF/8id/UtWHSyPX8UMhVEKgCzn1VRp5opR28oVU+l9N0wwl67dNpnKJcSWxXPNBQEDQJCy3zwEQ9ptp6J9mumOC+EITvdYkieNPsdw5C4phW/sHeXGpdCcvWaObxNj+qx9xZCP9715CcwZINi9jWGwpy5ygkM1pNRancut4Xql5czmQ6N+e59SjUyUT4/G4gC6q2HpnCT3pe+7EOBNa1EJkvIvIeNfmNdEUx9y2jP3oOMmADWUttz2mbWAKY4OX9U+u0RdsAFKxnCOvfc3Ddz5EDNkQEkb67p9EDDoQkoWzZ+qCljL5yIqrbGr2oIYsRMLbCUO5qHQMHZTU0N937JWteXfnCpYOM8I8RK/dhK+uYlhqzllWU8Vt/WiZ62NltQNd0HD+8CsgldBJDVlxXr+CgMBShsnksLzGULJ+2wnvNgKcNKBnzJPs6EDZiolCUaSyr2dvVDPrKnVtqMSXq1A9Bw1lkeO6ujr++//+v8dk2lrIRVEkkUiwvLxMXV0dBsPWx2xpaWF1dRWAK1eu8B//43/kgw8+4L/+r//rbfW+9957/OpXv+Ly5cu0trayVyh1l5J9HFDp8UApQjQfqc53bzYZzkeWM7XFUH5Yq71YADLfoxYzE5nooObSPRz/5Y9QnAskOxYIdiwgxEz0DJ+GucI2X4cZdW/eAMDxn/84Raw2BEy1d9GZY1aK12JZLy2Wb6EEHvvtBJVePJ43KcnlWd1vApTyzFASvVMEVI3vFvZ+Q7RbaHETkYlORFMC5+lnRKdbUWNmlDU3Wnyr/7XmJOsrdZvEGFIbYtuXr6KLKmqdF7XOS/idz0EHIW5CDDiQF1qQVxoRQzbQRJSuGWIX72zrg/Gbs6CKCIYkztNPCdwf3PZ7qWOyWuMoVx2F6h1Qbbj1wpuBw4bnGbFiL+WP5K1BDGzRokTfeF5ynI2BpIMRPYyhbp1k0IrsiHDu4gSRifaiUZrSY7PUZ8tVbpup0nb947Y5kj1Os79juXGMDyq53S3KIseiKG4jxvPz89y7d4+/9/f+HktLS5jN27UgFouFpaUlIEWs/9v/9r9F0zRkeXuzR48e5dmzZ/zyl7/cZm5RLeTKJlMJCnkoVxPFYujmQjZxPGjEOFcb+r2zhBu9KL2TzPks1MSMrH12BduRKfQrtzD85/ac/TqIJFl1+UEXQFIRg3YQNMib1lZHNCkE7g/gZHsq0GqhUKKYapkD7QUKEYznid3Yz2Xfo9lDrHvtm8k6DgNCT3tRo2aMtT5M1hWk8w9QvW6Cj46Q9LkQw1ZcDWs57xU0CXm1AXm1AcNkF0LCiGYLo7n9KF0zJI6OoIsaYsxMXNdRJttJrLmRLDEcJ8aovXYXNWJGjZoJPjpCfKEZKiBm5RBjwaCgKzLDcuqZsmVmvrmUT9kxxEtt826xH5tyye9GXrZh/eIVIq9/jRTMbU5QqC/KWg2BB4PUXrsLQPBRYROgXFlty12ji9nE53N8H5HC/Fm8a8c9xfCiEuJMVBSt4sGDB/zN3/wNyWSSCxcucOLECRYWFnKW1fWtFy2KIqKYmzD85Cc/4d/8m3/D8PAwQ0NDlXSrZGR/2GJHEMXufx6oNNRbKQKmUg/+ciFoEqYHJ4leuEPyg9cRRA1BVgk+OoK9dZVk0zLy8s4Yq/sZOUQwJtATBrLjycaPPiV+8gnNQCAhg3H7Vr0ZiEy1Ebh7nJ7GIELchOyt3eirQDJsQfE5d7RXSuD0fGVKcWbK/ne5yDd+ynFQzbXJ2W/ym+/dlfLei5UrhrWIgbr2IGPPKq5i/6GLRKfaiU61Myx7kCSVy+0xal+5TTJoJ2GPYP3ylaLViPGUAkUKOZBCDgxzqQ2w6giiWyPYVhoQNjz0R6Qw4ZEeBEMSXamO5rUYQVYdATRngKart0gqIj2jzUyON+UlLNn15ZOdPkE50Jv7g4z9OqkaUG2oNeskjiwSef0GqCK6JqCJSUStNKqUloNazIQQNaGbEujJ1L251uRc46paZkKVyKhqc56DcOJXKSoixwMDA/w3/81/w9LSEn/7t3/LzZs3EQRhGxFOQ5J2hu/JhYaGBl599VV+85vfcPz48ZL7Mvj3LCRD1Ql/5C7QVV9G1rChHPf5srKK7SV8goJVVGnUdnq3r4hRAIZo3/FbrvK5UMoRYKF3lQuOkwY6/rEdn5r9/o6g9MqIJ03EjW8gXlYw+q3o4tvoVyPIc8UnVjGtjE9QCv6eC+aORURTYuOvGMmAHdESA8C03IRm7UGt3YpDK4RsCJqEGLYiJIyoNT60KwH43wdTtpNaEsmjMhRoxCcoGBtexXTWgLRWvuB4M+vdp8feUIFn7ca4OTbSyDceuk6Z+b/9oyMll4fUmBnCttl+KWNov7VphcZsRwn3l1ImF7IzDg60tBHVWjl2tPKIIyMjI5w46eAf/uOd8zyNUud7uRgSzVv1y6dwGpOIUSv871Jy2CcoFZgR7BwLQ9hQ3T7UhiDG0VbSG9SALchQbG/kW+LIIiAhKK8RVkReeTWBd81OMGApWP+Qbtsx99LtDGHDdUqCjUPRSqPBVIr3qdkxl3eD9Huodr352tkPbI4JyUx9ncb5Yw4MriC8AvKSgBi0bZNvsLXWZvYzLQehAc3ejBixwv8hNS/eF1LvK10+898l9y/zWoV8JROlyuBCdeTtg6BsG+vVNC2S7RpQ/rpeVhuV3GQymWhoaKChoYG1tTVu3LjB2bNniUa3T5RoNLrD1KIQ3n77be7cucOnn35a8j3P/kuU4GTJxUtCuTuufEl7q6lhzuVUV6rd19ZONFa0bGqXlyhartx35PiHdob/X2GU5iXUhlXMD05t/qa6TMTO3SBmiGNdq0PpmQGfA90RxjjRjeXembLa2g1GpDCyK0D92ymbYONoH/JcK8tNUxgb1jDW+RDE7c8uLTZi+zpTa6YANuKDc9A9w+CXbxC3hRm58luMo/2Yng4yWe/HfWUY9wc/QFArmoZ5+18MxaJDvP9Pavjw/72e486d42dLK5A9ZoqPof1CeqzmT65dGZKGBJJiyOmM522dY+70MCd/92NEXUw5wMhB7MfH0FuWuPHlIKrqr7jtf/eXf5+//Hc3+U//37m8ZfZK47al7YptaMoiDKjZ7yBRlROouq4VJi/exPjYh/nxMZTWBaLXvuWT351GSVSePjpXnzRbiNCPbmBbq2Xgm9f5V8zgcEZ45Y3rfPnp8U3nyVxtFDo5GVBtdP8zY545tT+opn1wuWnjK2+j+HpVLWSOh+5/ZuTj/yVB4x+k1gD7z37KmLiy455C5oqp+gQge/Mg8aGUOQ62P2O+U4g+U3zHyVVanh2Gk+xMVEOT7OiBa5f2Nk1HWbWPj4/zd3/3d/yLf/EvEITUBxEEAVmWaWpqYm1tjVgstkmIFxcX6e3tLbl+k8nEj3/8Y372s5+VrHHORDo3eTkhlXKhWo5R+ewUy/XuzBdtYjchsHKhnEFb7jH00Mb/hYQRMWTfVk7yu7B8eRXxxBPUplXEdRe6OQ6SRqJniuXlGrqXOkvuWyXQ0Qn+V78g7XoRGuki9GiATXMKXy/hZ72AjqHOh2iK0xauQembwDDVlbNO07NBeDbIFNCnGjj69es8eftjpsdaYd2NZE4Q/JNf4fzPO6O0FArSngulCqdqLGaH6ZhsL8IEdTUHmVqy8+CHv6H9/mmMMQsz7dOYh09zRLWiygozQ3ew+NwIupByQLWFab32LTFFwvf1RVT1xT9ar4b5jGemBS5C4vhTBua6GTvxBCAnMd4txLCdvuvXmD57B01UQYNgwEp0poWjJ+e4/W0/kPsovND8G5HC+64tPqx4XmnAsyFa40AqXfmSWBpJH5Y9DKm12I5MMempRfG6i473zDmSXTY7MUcmqkWIqxHe9kU1EyrLHqGlpYVAIMCvfvUrPB4P4+PjXL9+nZMnT9Lb24vVauWXv/wlKysr3Lhxg5mZGc6cKU/rd/bsWdra2ojH42XdB6A2eECozmI4HhfyZnVJ/7bXO7YRKbwZq3ivPYMrJTyZ7yL7v1yQvbUYJ3u2XdNlhdAf/xohKaM5g1hvXEJIypjuncR3+xTuS/dYHLqDZins8VspdFkh+F/9YvNv+69/SOhRKo3vTggoazXEF5qZ8JuYvXOMCa+1qIAYjwtMCyltqmTNL2zT3zzf9VyOl6XYn+/HGDpo2Kv4mdNLDsYTUD/Zg7nex8Slb0m2LhH68Yest84xcekGuqTR/ugUjwcfEX7jC0Lvfophrh3vlxfRovuf7GOvUIpNYbY2rJzFVNBFjnz9GgAP3/8tMVcqIUlre27nv1KQr799Jh2npxFT2MbTjslNohZ8MEBdTZjuvmUsltS6lGsuDai2790cqyaeFzHOHg+iOU79WymtcXQmf/SsXP1NDN3FcWIMwZA68s/29Xiefha5sBsOU+5cPmwoa/tttVr5R//oH/HrX/+af/fv/h1Wq5ULFy7w6quvIooi/+Af/AN+8Ytf8G//7b/F5XLxp3/6pzQ2NpbdqZ/+9Kf85V/+ZUllOww6mxGFAvWMVXk9LJb2sBIN8EgSkArHRobCWr5qCpLnPUmFpAHDs34Sx0ZSf8fMmB6cIHrxNu2fv8bMjSHqXv8OJWrD+Gxg1ycD2dBevQ5A3UQ3ykba5gHyexFn/z+NQseqmTDWrRMNW/F8don6t26S6Jxiar4h5725UI5AquZi/bzHyfNGOo3q9JKDlsZ1JjtmiXx1BetIBx26gZlzqRBk7Q9OIydMjHSPotb4MY72I6/WIyjGnOPqMCE7JFS5TpiQe1EtNLaWF+sxPThO/NRjonNNxOZaOH7+Pn6flXBo+0ajkKd/KXHbx+MC+sNjxN/4im5FhplG7sge6r7robN7lf6BRZ4+amdupi6rBp2J2kXkRJJhSgst9zzCIlYD1e73834H2Rs8Y906yiM73i8voiflgtF9sk1MZFnlzs0eVtaEohFVcik6Ks2XUKx8rror1RofZvlVDso+m2pvb+ef/tN/mve3P//zPy+5rtraWv7iL/5ix/W2tjb+5b/8l2X1K/2Rn7/1zU7kG0zFBtl+CI2DQngsD04hRq3ETz4i8uo3iEE7xrE+wu9+RsenbzD35Xlqr9xDcwQx3T+FmNhdjNhMreJwTcoGrPPREOMZZbIX/1y2dqWEydusw2eizufAdf4RCU8NSZ+bZNjCeusCoseJtW8GQ40fU6MX71fnNzIpVY7y7NF3otCR326wXyl3qxVdArYTYwCbr5aTH/xkmwnW0S/e3iyvGOMkeqaxffIWUpYZETzfOLHPG7lIdTECYJzoQR0Yp/7JcX4W89M71syZc1Pc+GoQLSNkYqXEOHOsyKsN+G6dwnX+Hs7aVnjYxJrHScdSL/4aH32Xhuno8jAztUazpx1L5wL2gSl0Hd5QJG58NchwuDiBzLXB3isc1PH2vIlxJlSnn8hrXyMYLrD+zTl0xZDX1CF7/MquAFdrYly/20M2CynH/LEQJxizrdGr2BlTk3nLFEK21jqfDE7L+0pI8G4zppaKDsPeZ9XbW4vmfcJuFtpqJfw4DDgoRDgfTGN9GBZaiJ18hNI7jWEyZcsbeftzEj9/j9XfX6bx9ZsoHfM4f/5Hu2or8/sa758gfvYBCVMU4tZt5coVEoXJsoD/7jHq37qJ9cgkXz5q4tpoF4ZaP4Y6H/bBLc/S2ldvs/Tz98t7qCzkWhBLXZAzx0o54yZXooxcv5USSm0v5mCl/gS50qdm17VJoOsXeNI+inGyKycxfhFQ6eKZeT8Ujs+eOe4ExYgw34ylf4qh4SMMj+m4a0MMHpvnyaNUHJFKiDHsHGcjUhjmWtDiRmpfvU1XmJQz3kIdt4JJhn73Bv7BRzQ2+ak/NYeWTPnHKD4nuiIz1KTw9YS5ZKJwWDXIUDnpPmjPO2II0n7yMboljmGlif5wYRmxbfxK6qYD99D0u7ARirDQe6nkVFg0Ksy6F2Ap/2l8Zr/yKTVKyZWwF+E+q4X9Sjf9QpDjaiH7pVdroa7EmSrX4lNO8oZqHM/sJfI9mxixYr15Ee3+SdYu3SQ52oW1fxpLzyzRyU7k+TaU3imSdWvIa7vTrKZhnOglfuYhE9YAhoB1x+/5NMilLAo7yoTg2oMBLEfHcc3bEXqnWVmz82RFQvi7s5xRGnCeeYy1dw7ZESIZ3B25ykeQq4lcwqoUh83Mcpl/55qHuyXK1c4+mN2fNDFOikkedozSc/MyKwu5F7HMbFgHUZuXD7lI3G5lSaHYv9lE2fT4GJ73P6FvvBuCDYw9DXP19RGca03E53aXWTVX+3dDCu8Ax07OAxB6ojP0NOWUNzmect0dUmtB1HGff4i5bRnjSB9K3ww/NXQg+lxIPjeT4ob/jKBtEqct6ICw+W4lexhrzyzBB/l8Hg4/DhoxBpDMcZKtqYRlgYiBUqPtDKg2NCnO+rqT9W/Obvu+2c9Z6lzPJ6+TAQfJQGnhH9MyptJT61JxmORXuTj05HhWya2d2m0mq71YoPOh0qxgxQh0ufXtJXyCUtSjGzJIcsyCLWJDbfYQnmnF0jVPfKGRxekm6nuniLz1BaaHxzE9HcxbZ3bdmfVnQnMFEBQDsic/2c71risNafTNlJ2hOgdXX0tlgXh0P6X5EgQd1/kHxFdr0CfaqX/3GyKT7QSGS4/7nQvlLEZbqW4LLw6lBIsvNZJJvnqrfYpTrUyZhYj/XO0y5vWavMT4RUG17Q6LJcgYkcKggPVZD/HT9+DrC/QFWhGEEVxnn7Cy2ATqzghHlUZ5GZY9oMhMTzTQ1buKb92K0RzfJLHpOXVEMOK58g1oAroOhsluWgO1hOpXSXTNET57j0ZDEmv/FRp+dJvEcj3oApIjjCCp/NAVQtMgHjNiMCaRZQ0ANWQlMrm3EXr2GweRFKdh7ZsGILHuZKUxykz3BHXdi5iHTyP53NvK6ugg6JvJasSEibpP3mFtF2aS5WiZC9VTyrysxtzdbZKxSrnIfmmN4QUgx2ns9qWlye9+vvzdID24DoIWuBhKCWWUa3Fs7p4FIP6oH0vHIh2t65jG+hn5+ft0nH1K/ORjVvUkkbHusvqS/c6UjjkM0x0IycJByss5Ss4lJNICxe6IIMkKmgaqKuJbT2mH2zrWsHQuIrsChB4fwdY7h7VnjmTIWtYzVoJKTSfKKVMspOF+z71qz3dN0Fhye7Ct1LE3cVWeLypxxCsXxUhyZKIDW980xoY1Eqt1LP38PZr+8BNMDWvEs46bd0WMNzA20kJX7yrumgiepIQs1/HQsITJmGQwWYP32pdoPhf+2yeoffU2au063qku+hbbNuvQBY26ViuOmyJqrRePoJDw1KAlDGgJA7piQLRGuRFWUBIyV9pjOIeekoxYSCyX7qj7vFDsBOQgk2JDvRf3xftI5gTmu6dZmmrm8hDMn3pGQhMJ9o4SuJdSTvQ0+VAdQcS4GaVjDtuXr26rKz0vSjFbSONF1rwedhx6crwtWsUucViI8YuIXBrw8U+u0PDqbWrOPwRRJz70AF/dKnx3mtm7g9TVeHGeGkGLmYjNtVTctlqzjnGsr6Sy+VJXl7IApMtIySCWQIj10QGUlYbNGNDSsgWYxeAKk1itZeWD13Gceobz1AiNkVS6XWm9Zke9mQK5EGnJZ5ZT7gYrF6ksheQWml/5bP/3Arki0GS2X+opUeZ7GPeYGL32FZIq0zPVy0yJfdkr04qDTEhKQV4CrkkEHw5S++pt1n5/GWXdReDBUdxXhgk/7SX0tA8QCo7pQnMk+1soiszSL96l+Y8/pr4hyGvn5pHdAURDEl2DQNBC7NYpQCD4qB/5yjBLQgJtsYMjpBamiZiEMy4z4bGDJ4+ZVNDOsY1/RiZAkJO4zj4m9LSXZNCOsubmMJpZHOhxKGjUXBlGNCQJPunlt3MyZw0KxsYwd/6mi/6BRWb9Eguyh/NGG8Ez9zH73ZiuX0Ze2blp2YxGVcbGsZASpRRUY1NSic17tmlhufdXoj3eb3526MlxNjIXu3IXuFx1ZGK/vOwPO0ola8XqIOBk6Tdv0W+C8BtfodsimNuWqRuLIq/Vw0fvMVk/T821u2gJA4mV0iZodp/qzFFMFe6w8tl259Mi6OjoURnhXiOjWalX1ZAN383TuC/dp1+3oAkJwu3LqfuMCkIit/a9mPNcPpv23aDQHCkHuUwnnscmtVjIxmL3ha9+i+oKcPo3P2EqUvgE4rCHQtqv/ueTITeWZX4IiOYY4CI61U7CU4P78jBq3Eg0jzlCOaQ4jfSiv/Tz97H0zCCIOqFnPcj2MLoiE1toJk1albVafN+dwj4wSfDsY75LGPDfPU73QnfeZ8sHfWwQX88crrOppCfLv34TPc/8f14wNa9Sc/UuS794d8dvB5oUA+b2RdwXHwBg++27/FbxAmDtmUWLdtK52oHz4jjLi24A1IgFz4evMaCmNjdCJLeSBHKHUKtmkqa92ExX6hT6PL7zeFzgOC+jVRTFrCLQmOdFlUJmcy3ExTRcpSyeL3LUi0KoZPdcDGLExuLHV2l4/yskSxzdmBFgfd2N77uTuC8+YP36WRSvu/z65SQzqkLpuRwLI9vkRZeSKJ2zKO0LqE1baUibdFDWnWhxI9HpVuKLzXTO9MFMSostKgaMTwZJHHtG/Ox9uAPychNSuDwnvYNgelPIhGE/tMV7CR0dzRLFfPN8TmJcSsjGl8er+ZFNkoeS9SjrDiRLnGF5laFkA33+RpK3z2F460vap/p31LEbYpxGJulW1mpz3pNYbsC73ACiirltGfeFh4z/3ka3UB6xFRQjNb/5EZONc9Reu4392Di6ImMfnMTzydWSHbP2EsbGVQAs3bO4Q/qmedhBIsaO00+w9c2y/HdvYWxYQ7JFkUwJbEemia/U4rt1Gj0hMkSqz6JlFU0xYG6fQV13cSrelFHbzrW8kI38gGrLaf64X5vLQlrdTDJ8kL5XIew3lzr05LjDoJMrl156Mc60Ja7WIpzrfh2diNuHoAlYA+6cZb8PRHkv7BFHpDBoEr5bp+kQDKnjSSkjpfaawOW7x3Bcvc3nH59EzeGYk8YOQSBoSNY4apma41I3P7qg4fvBRzmz4gWGjyNIKvbjI5gb1kG6TyBuxPrlNWR/DQIC5kfHGZzqw9e0yOy5uyQCNoyCQPPoILXzHc9tTJVjBpEuk6uv6TmaXbZYnQcJSu8kChr22fZt1w+7hjgX9tLeuJS2IfVeAw+O4j7/gFO1Vu7f1yHRyJG1OtAElM45jBM9m2XzYTdH0kXfgyYRm21FNMdpeP8rXMY/LXuTOiKFYa0G//Bx3Bcebl5PBlP1mFqXsfVP47txBm2Xcd9LQWf3ComEjG/djrPvEdaeVBQP19AzrgCf/u4UxyOVm7dVHzoGdyqKTNNPPgNA8tQiBpxIN8/jnOmkge2yTFlpxlDjx3X2CdZP36C+iB1xJvKd4B1EOfA8sxFWO27+XuHQk2MoTlDSg38vQzmFr11HbV3GudRE952L+FoWqFloQ9S2iNr3xSxjtyYV+aCs1TCR8Xd6cXPXhngUkTinSNTWhVhdcW27r6Ag2PA4rnvjJiMfvVp00uYyI1BlhTFLADQR3RJlctm5WW/TH36MJGuoERNaUiaiaXhWHfT0eHCdfQxAeLQLtyChunxodV4i7/2eppEBWp9tRKlIGqldbMe3OkfLk+P4WuYJu7ws943QNHqU2sW2smxld4vdnIpk31uojsMwVzRrhNipRzi/fIV+o8D4xk79IC6Iu0U2IXxeC9yAaoMVG6O/t2G7dpOunhV41oiAgH2tnuRG3OFKiXGhezJlSSEb/vTf8YUm9GNjCGp2CLfiSNejxVLEd3y0CdujUxuyRafm8j0AXBcfsP71haJ9rxw6TleE46fmcv6ajBiQrQqd3SsMjxj2iXjpmDsWcZ5+iv/uCQRBIzbfjCCpyO4gzlPPMNSkUo2LfgckZU7cvMp0OPUu+0w65DIJW23DFA5hVAzI3tTpQKXjPN+Y2C8cNI3wYQoiAC8AOc4O5ZaJvYw+kV1vYKYX49OTyAkTwcZlZs7eYbVnAh2N9kencHh3GvDni5BxGEjB80S2oDcak5iMCjNTDRw/PcPnH58qu07ZHilr0sZsQZaPjOBz+tFsYZSwBYMrBIBrthn/d6cB8N0+iSBpxBaaaP7pJ7iA724cYfRpSst4wWLC0j2PWudFs4cwf3uR2NVbLA+M4O2YoWGij5qFdowxC303rrE08BRvxyxtD08hqjKmcCou834T42LX8qHYnKz2c+w1mZtrnMdtSCJ7a7dlVywXL00riiN7Y3UkWsPMWB+DFx+wNHqMEcKcDNkR6td5MJ87LGMlsWYzv00+28xcmdTG7B5qrt7B9GwQGneGRyx1rCdW61i/PkTn+QcEl9ZJeOqQbBH0pIgga5va0ULPsBv09i/T2bNCMmhFdqTisPi8NsZHm1lft3Ey2kzdW9/Q4UoyRuVOWiVBUjHWr+E69wjRoCJI2uYmgeRjkFVQZOSVelRzHONoH6aRVLjP6RKq72oOYgvVMvjJeyWVL4TnTQIr+Q75HM6zy+Qj+rk20NXcFKS1zrOKQEfVas2NQ0+ODwqcq1u2Sa7FVjqHzxJx+vD0TDL2yte0PD1G8+jgDkL8fSDG1RQSuQT+ypIbgKEL41gsSt578gmJlQ9ep/FHX6A6A0gBZ0n9eHb2O4JeF7HhQZIhG0eiNYzIQWpe+Q7RuLUIxhdSyQIGVBv6f/5jAE5IW4G+vot64Ek9Q8mjWPumUc/ewzTXiuStQUgYWbhwl4UTj2gaPULDRB9Lg6nYyKexI/ldTGfYHlZrLLmlwgt3NRJypFGOJrlcFMq+lg/ljtVGV4QEED/2FNOToyW3cxjxPJ8rO/51epx0zHUTvPgA0aigxSTci61MXriFONKGFrVs3rMboljJvUrrAk3n7yJ56jA9Por7zdI2ltnjP00u4kuNcPs07sv3UMNWJHuEyGQHkiO8EcliJypNQpENV02YxGgvvrHuvDF3/XdPUP/Wt/T0m5gcayKd2CRXP3YDx7FRbEdm0DWw/+Z9pJgNzRIl9Ae/BVnFMNGN5c7ZiuufXnLQETYhKwrNDetM1a5gflY4ln56w1TMXKCUE9VKY+cXq6sU5CLG2X3O1v7mSyxWjTW/kD33efZ+4/FCk+Pn5/UuwEZcWut0Ow0nxljqHQNdIPT4CP0FzMNeJEe+zAmSSiyxexTK+OZyPWRpwZ333nxaHy1mxjR8isgr32D79E3EuDlvHemNTdIeInzjDFosVXZECmPtm8FQE8Q428Y6OwWEsOHQkSlYtoXEmbZx0XeGutdvkWxfwHzrHMaRfsyuIMtHRlk+Msr7w9eYalzgadsEgws92PtnWfbYsPrdm0HpK0G+hTtXBrtC91Ri3rHXyXXKIXXlappN908hemuJXf4O42hf0VjZhXCQtcfP09Y4F7bkpAyqSG/SzhHZCGtWGiZ7Ud+5TuBpL5GxLobltaq3X4gIqY4A0Wvfgg5Gc7ygvM9GLvv7zXbmbYx5nRhqAyTW3OiKgZqrd9DihZ39KhlTgqBjtcVoaArQ1Ozn24lG1gvUk/S5iEy3MnhsgcFjC3zy29Moiryt/d2SZNkVwNy2TGi0i9CjfpZ1GAB0gwKqCJJG0BRhVg6CLhaMJFSKr4QuquimXB5NpaFUspwum43dyIPMdaWU915KwrFSsRdE+XnghSbH+4l8C7y8Vs9vlqY40aiw2juGYA7Dw/w72+dBiveLkFdrgc0UGpkTXxnrwd4zxVnRyV0tkPPevClwx/rRXAGWjz8keP/Y5oTORfSeuVfQkzJaYjsRcp4aQZ5rwXL7HANFYpKqjiB9goYU2G4frYZTjntiwE6yZSOdaeMyrsUWeuIuJprmGGtJRdEdbZnBEDWT7AZdUqmfb6dxvB9TdKvvaVTj22a/i3wLzEEgxmnsZryVkn1S0EV0y8ZJgCYeKAK5lzgoC16fSedhwkS7IwrBFElsGTnKw7kabFdv4xPDMNFUpJbq9ANS4znZvEzNXDutT4/T61ZIW+qmU4xPL5UWaSKX7OmP1jKytMW2jQ1eItNt2bduQznp7tN47e1HWK2pE7CRJ62srxXvc+DOCSxtSwiyxuvvPOKT357Z1v7uoOO+fI/IVAfhZz2kI0dMtUxT+8odEl4nJlnF3LaCPBIk6XOVNBfzybAxRUMPWLEsny6rl5mEuJiJQbH+7ZYYZ/+7XByUOf688JIcF0E+G8l8nveZ19ODv3m9GZhFkzTafflTFD8v7HVmwBEpzBCVE2PRFEdTDKDlz1sPEBnvxGGP0PDeV7w61c71MdeOyBW57kv3Sxpvo+Hdb9CTMvqTE9u0gLqgoZmjKJ1zJE49JvKoHzKcLWVnauETQ45NDXEhxE8/QEgYsdxKOdKkBWd/uA7+859slusz6XA39W9NVLlhWaHGvYScMGH78hWEDc11snkZz6vX8fRMYv7uLEcXO7f1o9INUOaY3iszi73CnkRNycKAakNQNjR3osY+hN/cdxw0rXE2TCE7Efc6R20i00sOxuMCvfFmJm6co/fVW8xO1xeMYLMbZMvN+o5FFrtnsaomjrhU0EW6moPYbUZCG2XSJDmNQmQ5F0HO/B5pm9uln7+/rcxuTiDaOtawWhPcu9PF4nwtpScfEVj+1bs0/fQjDAaV8yYr6oZPxG4hO0NI5jjhke7Nk4ChZD2Oi/cYvt1N88wA9uOjyJEgSX/qfZaTdjmbJMcHn5FsXsY0MoBhvrTNRxr5NtWZJgjVJcYph8mA35bTLKOaZi35xmMpKDdKxfOWOy/JcQEUOlKG/Jq0NNIDoTdgYf32BRyeBgz7EHKn2titk1QlO9DMe5TadZZt/hJSKAsE7x0jMtGB4+QI5y55ePKgg1AoZXtYTEioQTuKz4F9cBJ/7wyWx8dZljUiTh9qvQfdkjpii8VkwiM9m/cd0c0E3/kQANOzIyU9n/Xrazuu9RsFQld/j6CKiCEbhqluxtdSmymldZ7otZukn0C3JFA6ZzFMdaF0zSJoAtJqPWqtl9iFu3jvCoiayNKREVRDAm2thtblJpI90+iPB5FXGkk2rmBYSoVeyjSHcKhbfTqoxPcgwTjZg9I9TbLBAyul2aznw0E0rXjexLjYxr1hqofZc7cxL7ezOH2W0Y1FOOl3Egpa6Oj2MDVePe1xvqNqX8s8cyce0OttpsVXetrnruZgUYIMuedibL6RiCmyo0+VjqOGJh9Hj8/xzReDBPyF5XY+eZrw1GJqWkM79QBuXC67D7mgJQzomoCh1gf+1LVh2cMbSYma2jDO+odYuxZY+/zSZqSgbOT7btu0uUn4sTtG/OgIyrqD+NEnuIP2bSd85c6HXHa5xYhfOdp+g0HlqGwhkazbdk+uMZDZbjXI527MLUq953nJnxeaHO9WI1qKtq20+gVqF9qLF9tFP6qBfCR4PwlSrgk0uVgD7EybnA9q0I7vxhCOkyNcemUEVRURfG78d5xFs0ytfXYVAEOdF/nMCCsBB2seFy0TPUwGDRgb1vhuTUCV1zYF2KgUoRkgZtzSIlaAKVOQ+tr11DM0rKH0zLD+7RniC01IEZmaoA3ZsSUoEgNjxE8/QgzYESJW5PlW5OVGVJef9ZkW4gOjJJwb5iXtiyy7AuiShiFkQ3P5iR8dQV5qRm1cRVppqDhL3EHEfgtUMeBk2bG+a3L8EuXDvdSK+QsHT69+RejsHbh/dPP7a/dO0vvqLbwee1GyVy5GpDDEbTS2rrDSN0bE7ePkYg/dq4U1jblQrslFmjRE51qgPxUnZXcEWefYiTmaWnzcu9O97V3lSyKRHdEjjfVvztPrjlF36SYedKqR9lqLmfHfOUnd699h/+zYpsLjzq1eOro8iLK6LTlKrudO9zFbg5lJwIblVX7qtnDnu14czigDxxaYliP04tpRXz4Uc1bLVy4X8n3DbRpiPPga5rH7nZs+MLkSfOSKw1wuYX0e60Lme9pPU48XmhxDdUwGMsnpXjv5FQtzlUa1B2n2sdJ+T4K0gMo1AcrdPabLBx8chYcDyI4w1v5p6t++TuhJH7eWDZyM5dckDcse8MPQp1c2r6XjK8cXmlGzhVUVhsSIFIagneVfv0ln2xqay4+0XkvHSitjJFGDdmp/9z69Jg0Bga7mIE/DGotHn9A5fA45m5SLoE4eIbrWBIKOKWxH0ETGL19HitgQNAlprYZE9yRq3TqmkBUpUl7mvYOM8saMjrltmYTXtS3CQTkI2QNonuoQ44OkPX7eR5ulwhxycPzLN7n35keER7o3ScJ34QRXHhzl4tUnfP3748Ri1UvBLBrjqJLG9NAdenyNnPJ30GgwsLiLOguR5GzlxYBqY3S1loaL95AkFVWVdhyll6KBtDsinDw9iyhpfP3FUZQsX4pKxqK07kayxhCMyqZCovaNbwk/6yW+VLpWPRPxhUYATpyZ4db1I2iaSDBg5fGDTlIR4+MMkXtzkcv0IRdBPiu6EMUYqytO2hb6SHasY3AHGfEXntu5TBgK+a1kohhJzv6G2c8ylKyHh3VoG5uQYqEGD6st8X73+4Ulx9WIl5rLjOIgYK+Ia3a4pL1uL3PhzXZUqBZBRhfpXW9m5I4DY6MHbWCUN0+HScx6SKzVEF9sRE/unAaCkPtb51ooBFEDwPpVylRCF1U0SxQxYi05gsTm80Zt9M26YXbrtyOyMWNjtvUtjtpELLeukA+SasC+njpqS39D+ZM3gdT3DXibWGubIaSJ6JYYvEDkuBwIhiTuS/eJzjaBIOC/fQLnqWdEJjpIBvNr8tLfTHUEMLhC+Cpc9A8yDgMxTmPWb0VZbmCtY4Ka0VQCnaFkPa6zHyGIOsdOzXL3Vt+u2pANSVpa13E3zGJqWCOiSlhDTgYXelB1aVfEOBP5TC2ylRe6YiASMWF3xPD7tmRmLi1y+vp26Jwammbda+fZ4zb0DXlV6rF+PtOKqfoFalURXZFB0HBdeICx1o/x6l1WP7qGGipf1gzLa0i/OcO5ixOcuzjO3e96d9iS59Nm50KuUGWCqKHrwmZdxz02Gho9RPM4PZZq11uN9bRwG8XJ90uUhxeWHFcjVfRBzWi3F/3aL+K/V8b7ue7PbndkBVipZ90awdyxiLVnFsfxMcITHSSW60kG7AzLHuobAly4kjqq1JISK796m/zCR8d9ZRhx3YWYMNJjUrn3h7/E7HeQHO1DCtuRfK6SzC2KnRpkO/JAcXtFyC2Mx+MCzDfRiIB/6Dsi3TM4/vMfl+RMeFhQaig3XTGw8uErGGt9WLsWEGQV0ZxAdgZ3kONcYzLZtEJ8qT7nJuugoBIN8GHRGsPWGFfWauiu8ROypTLL6apM+FkPassiDY1+RElDqyBbHYDJlODaG0/x+6y453qRv7uIGDfRZ9KRcszN/YIccOFyR7aRY8htY5s+lZANSY4MLtLR6SEaMzI10bRJjDPvzT7FKNW5y9y6giBrNP3hp8SX6zC3rhKda8LSvozr/EO8n+ff1Kfbz+4zgKpK3LnVy+lzU7zzg/t8/cVRwqHcpz2l9DtzfIumVKpv9CuAQF19gPZWH75b+SNWlPI+qsFFCkJSMTWtbsbUz0Sla+hB5T77iYMrzStEtQZgsXiuzxuVmD/ku+d5EuPsBThzJ15O0oZSwm6l21IjVsLP+gg/68VYv461fwpb3wy6KvGGpKCpIrGFRkxNq4iyivzqDe7d7tmM25nuH6S0jqZGLxoQ+vGH3AN0TSDmCqKdfohoTKZuSBg489EPt6UTL4ZcZLhQuXwkuZCgW5lvROysQXX70GUFIWncdl8mDpOwLDd18FC4nljYRmy2FUFWWUmorMshFrMIRq5Nm+YIokbzx8auBAfBtOJ5E+NK5FJitQbnmadYOheJr9Ty2bfdDD3tR5hqx/WjL4DKkmMIgk7fwBLWoJMrN1/f+mGPZGexDW96Tg+oNpSJPo6/ep36+e6SzIIu1ULtK/cBNhzvrFDgSD4fAcxHvEakMOpGymtB0jC3rrL64SuoYRuu0CQxp3/HPYW+QbZ2dhgPd2/1cvzULKeGprl5faDohqdYvN8B1YaWlAlJGsmAnSHVStPFYUJP+0isVCfCVDH5WUlcdgBUicRKbd76ykX2vNtvW9+DgheKHB9EErvfKMeJMDNE136/u2LOCqUGLy+l7oKJHZbtsNzBiBRiumkKTRXx+6wMJVNH5PcMKxw/NcvlV55x8/oAifh2ezxdMbD8y7cRLXHUiBlBVhHQ0RJGUguOjv3EKPaBKe79wa8483c/RSzR1CJzgSxGlEtdTLMxIoXpfTZA5O0vUqHIcuCwkOLdkLlh2cMlt4BcE8B2bIyFudqNMFaF2zG3LeHsmCPy6dWK295rVFsDvB8LZaXyKBl04PNacddG0BUJcWNMGzccXQ1yEuJb8mVAtW2TE5kkzWhUqK0L0dzmpbE2giFkp+9J7tT06flXaJ6WUiazXKmQl1L+E7b+6ZSfRRHY+mbw3z1GfLGRgJrqSzHiWC7an55kZKwLwaCAJm2eqix57Tjb5jfLVb4BFHjysIMLl8fo7VtibKQ1b8lS1xExYULxOdBVCWPjKjogOcKks/2lN6zVCI1WCuEsmSwLGnXvXGft02voiqEq8zMt8zPNHr9vBPmFIcfVJneH/VihlP4Xe2d79fzZi1Aa+Y7BCh2PFQpLUx4hEOha7tnW3rDsAV3k0f1OXjvq5dLVUe7c6mXAvz3yiK7KqKHUVNITUpZ/nkDo0QBJnxP3pftl9Ge7Rnh6yVFwUS3X2z2NAdUG3pTQC/30A5wZMZZ38/33WpgW2gCVi/SYkuyzCOgkFpqwjHczlMzthJNuc9y5ivvSfeIrtRU78h1UPC+TikrkePZCfvP6AJcaVSxd87z57kOC188TX2gh4ZmlvjEAk63bIhcAmyT5R7KVsTgsDd3B1D1HMGChZboP6V4jA8qWnWwp5kzZKOUkqNQ6M+emgID3q/O4L94n9Ky3aEQeLSnRpJswRmrRpN2FFi20wffSJAABAABJREFUzgwkHZDc+ntECpMM2JEdESRrhNuJSElt5COjui4wMdbE8VOzO8hxuQQ2PQ5sC00Y69apvXYXXRWwdCzwZNmMuOrcti5UM3ZwNkqRm5lyTzTHsI32UxtzbytT6elyttPnfsqBg5Qh+IUgx7shxtla04PwUUpFMY1vvmPx7OfN9fx9Jr3q7yVzkqV34ZmCJtMJJPt6MQ1DmoxVOpFzpXTealfgy6e1vBI38uprIyRWPGgJA9HZFpS1Le1iIaGpxY0IcSMTcYH+Eh3m04R4t4tqKd9Onm7flmSlUhQ7ESi3nmqVy0S+8RSd6iir332BBiY+uUr9O9cxNa9W7IWfD8/TtGK/iXGlMjybGA/LHoYSjcTmIDbXgrlzHuvlO4SvnwNRpb9WwT+5s54RKYyhxkf4+AS6OY5DB+sHP6A2I5X8eFZfMwlyqSZQhZCe76UQ5GwSk1itQ/E5MbcuE53qKHivKKto1vzENN8cyBW1qZz1c0C1QchGAEiefAR3ejJ+zR3yrZgJjHfNjs0ex2aPbrM9rpTAxpe35LkgpZ7r/KVUrKI1jw0QUL7c/SlRMTmTnWkvfS373n7NTKJlHsNMZ856Sl23C5HS/dQYl9rfDsPen3QfenLcYdCpNPt5tlnBfhLjSjV9mcgnlLIHei6SnJ35LF2m2hr4bA1iKRrjTOKcq2wpzhWVIptgZxLk8GgPsflmOupCrFqDuC8+AF1AjRlRvG7O2v2Mj0ToXevcUWe0ZQxxrJd+Y3ljLN/4qMZinIaODgJIYWsq0oYtjC6qCGXYR6exX6R4t8iZQrwC9K43EwBqrqbSGPpuniY2v9MxZj9RLGPXQcNemnTFZtoQJI26N24hr9USr/NtzXFRxXF8DNkVRLLEEE0J1p/2ofgcKD4XqCpkmWRl97Wa8xCKb3ILvavobAuW9sWi5NjU7CHR7MH88MTmtULjP7vNfFGNdHRC732CZbwDJgbz1qdGjbS0rXP/bjdmc4Jzl8ZxOGPcvN6PNyMcYinkNu1EaLXF8zrmlQPZGQYc6DpocQOSWdn8ra4+NRY+Mq1wKl48qUy1FAVp5Kon/Ae/wzDRjRjP7fNwEE/Ad6t02y8T0ENPjneDUlLj7hV2Q4qLIS1E08+XS6gWy/4HhXeTpUz8THulbqE0dWl2dp/das+ynTmyyXquZ8yngR6WPZAAdbYBaCA82o3sDFL7ym2MtQFsgMsdxvfBdnI8IoVpbVhFWGqumn139vhJWCKISXlnvGNKE5BS0IFhogdEDbXBi2YzIQW3mxU8b7uzctLBFkMlz1HsPQYeDBBfru5x6/PQHu/nUWq1F7pcYyI62YEgqThPjSADsZOPOG3QWG+dwxx0Uj/dj5wwsrRUhzvDHyDfO+ho8TPRMI9BlXGHnThitqJmT8VQasSZbEVG+vqAakP1tBA8/TTlO1DgFCgwfJSaVs/mfflQ7rcJ1XrQXEGahMK0wjzVRWJgnAtOmSmzH1nWCIdMdHavYjSoLC+5OaPsPIUpNA/cNWFWl90Fy5ciL2IzrahRBUEA0aQQW64haoxRUxMFQPFbOWu2MCwsF4yVv1uUKp+MI0cwzFaeYCyNSvlPOeQ7V9lyzCgyx+OsIlB4C7h7fK/J8UHbUVUTpZDfcutIIzsmcT5s0wiL5rJDAmWaN2zPYrTlTFOoH5ntZWqcM0leXnu5UmxZNZHvQgr8LhXq53JjkprL97F0zhOdadtWh+YKklTlzTZ3SwoSlgiTF24St4bp+/YqK73jiIpMsHGF5pFBFEuMlpHizjkA063TaMsukmrKQLBnuo3xyPo2rVkmqq0RyawzF3ItctmmN7lQTtjAUsoXm0PRmRYEOXmgQrpVHB98j1ENUpzTpCLPeIiMdSNIKo7j46huH1bRQOdCH23eJmaWUpvAfiOMx3cu4k/tCxwNN/N2VyrbZMAc4X7XCAB1QRedv3+r7L7vhXJECtlRw1bMrcvE5lrylNIxtS0jL7RXfWMSc6Q2B+KGqVk+4pNsXEGQNIy16/RFbQgBJ+pyHe6mNdwnZxm6MInn42skg6XHQtbybAZynRDlH986Na98R9L/CgCCDuamdZLBLSdsgytC/ds3eCtsZvXDRnKZguznSY1p5Mi+tZUPmfMl15iqRuSv7PtemlXsIXbrrJZdRzUFTblxEXdL8gtGc8hRJhOVaLXKE1jby5XbTiGCXEmd6XpyIb7QjOdTK/Wv3qYl4oLlVN2ClCKd5ntbnu6VEOSxDfshpXOW8IlHSLYYaAKzYoLohge4xeciWL+K3bv1jguNjxEpjKtzgehUO/ns/rLLZ/47+/2VMpYK1VkKSgm8X8l3rVQzbvv4LcLvfkbTH3wOwPKv3yzqFHVQcViIcTaKbZQkexjH8ZTVsPWbK9QYRFRgJkffUvNMR3YGWW+b4s2ji8BDmudPISLQCrQsDTK/5sQVcSCU6ZxXLWKca14HH/fjvvCAxJo7p5Oo7AoiO8IcnevOWWcl5n5pO+lgwwoAbUYNew7Ti83EJYaUqYKxwYuuyiR9TtpHj8MoJF0+Im9/Tv2737D0y3dAzW3alf7WjyxLACzO1+Qtk4mC41vQMNavY6xfZ+2vLtLiDpM4/ZhY1IjdkeqzmpCQjCqSLYYgqehqYQr1vCM87KUCsJjWt9Iws4V8ovYT30tyvBdRHEqps5ofutxBnysbUCbSi0uxxbEcMpwpnBo1S8G0lrnyp+cjsKUInHyLZfb1UghRKXFR02V6vS003LnI+CvXsdy4yOSyG8mUAGAyLKFnaa1LiSWs2ULETj0i2b4ASYlkzEhg+Di1r9yBpEz82NPNsglFJtq4QmK0j0BG/bqURLOHQNCJnX6I6fExZE+qz/5bp5EcIRp//HvWbwwBOxeafMiXhjW7TObvmfdVSsZKib1ayLYtX7uVbJoknxtxrgmtfRkAgytIYrU6sVGhctOKvViYqxkmqhoodfyIpjhaUgJdINm0wvhiSrOqG1Jzs80VYWngGUuCgrFuHUSNhqQBZc2K+c4Q8cERxIyNo0s1EY5smR1lmlTkM7GohBQXWzOSjSv0xO3M+q0AJFbqMU93YT82TuDOyR3lZVcQyZzYEW89u7+VkGRzwIkmaiyv1G0jx2mk10DJW4dqiWOL2kh2zOO4dXGrf343tt++S/gPPqTuzRt4v7iErqQ0t9lz3lC7zpsXRkh43Az420iHXMtVFopvoOrf+xolaCM604qy5scYriFx+jH1jakxpgQtGBzRzfKWrnkiE1155VgpTnellKsEe21rXEr9mjWMLmpIoe1jqJBSsdjf+4nvJTnO92HzfYhqDLJyYgpXMrDzJcHIZQJRSsi0YijH3rOYXXK+vO+FyFMhwbItFFsO5CLI+fqbqw/F7KFX55qI99agHHsCy1e4nQhz1W/B0rFAZKKrJLOONDRbiMA7n20mE1n56BW0uBF0ESFqRrfEMD0dRF5uRNjQYkTPDuM/exfF72DVGsUqCMizbay71tEUGXujh0jjlwA0A7oqIEg64bEOFK+7YH9yodA3KqRZLjdRRxqFNllplGIvn6vNUsZ1rueSVxrR/U6UE6PUvnqbpV+8CyXGs87Xl5coHcW+m7JWy8qv3sHY6IGL3xEe60KyxrC0LyKIOqNJGXm0n3hMJPhwAF2V6AvV8mMTeNtmmbZFiAWSePOaK+TH9JKDjrARSBSNRpEvolAa2eNa9Lv4/7P3X7GRbGl6NvpEpLf0vmiKrnwVy1dt73pP93SPxvyScGQgSJBGM/olSP+dbo4ESPgvdHkkQQcyR9BgDgQcDTTT3dPTvbt3797eV+3yliyy6L3NZHoT5yIryGAwfGaSrN39AoViZkasWBGxzLu+9X3vN5lzb9H2wUKI4mgv/jffJ67h4iPlPJDdqdVuB0Z+1cuHx2l5MkCLhiFI+VlMBii4C+DJ415sQsh5d8x5rlQI91gX9E7S/N2PWP30Arm1WkDC17ZE3ZVbZFdq8NTFiN85QsfoEVbFEmm10n+1xnQxkMIdSpOLBbf03iejy9TD1oaaJ5KikHXj8ubJrkbJbpTeo5UFmtZ8JdejkrEcdsik8pnbqYPeNdTvW/B4tp6l3nhc9eyBZeDXkhwroecjY/dlWcnQpixf6/pO/ITNyGOlYDboaFl+laiVPDsGJTuuFEZEWKuMcoOYrNRLLT+3K2ta2oeveZXW3/8l/vdO8PBuJ+cuPSG3Vkturcby+2l85RPc3jw//8m5He9gQPQQd5cIs5B3M0IGXCW/C/FxF8FegWLWQ2aumUwwRfjEQ7w5D+5QaRJ5/KCdI8dnS+c/kywqpv2YuVXYRbXboVabMOq7VnZGtHZQtEi+8jv/WH/p/4cnWHnj/S0rvFLqzwy/IcTOYEe2K7vYyNoX5wh0z1DMeIn8/G2EjI9en4QoifgygurdSqx0TQAwuVSDdS9YbViVXFQHVetBzOzWKRYTz9y5Ll1H+nw7TbO7doO6K7dwLdWTCSTwpUKWkpbI0LOGy9933zxHcKNW836UfdIz30ru+DD5jjn8185RDKQYVbmABG4NkUIg3ztBw2tfs/n4MMHeSURPAYBCykf9599lIV9AkETd/qlWHtKC4M3S/N2SsSCwGYWiq7TIWBdIj/aQ65nYGiNFT2nM9dbHiBwfYe3zC7quH1pjTbWCmvcqm6kdd1Mh592aTczqcxBJ8q8tOTZqTGYuEHYnfHVHVXYOM39Q9fFmvr9WA93UE4rVycVqVh+z38shTXbP1SPLRgGCdp6nsk7yecGROi63LOHz5XnpylPidwdJ3jlG/ctf45npYPrGUZBEXOEEnLlLOJxmYyPI8mKUmakGzuSaEP1pCu48mZHuHfUYLIQYDS7R5MmTmmwj2TUOK9uyTMWMj82H/Yr6hBAmTxKJpsikvWSeZfmbnannpdce4nk24ZSyQe0drJIaK5ZiKF8iyCzQz0q7ExBY/egyDa9/ScMr11n4yRuGAXqVUGSpBPYr6Uc5GHYlHOnZ5lZrt3ZIxEKJlD1NK7TdMzvbls+X49iTk4QzQdOyq6lCBPqGGzWJLmY9NLfEmJe/EAs0vPI1ZD0UmlZ58NYvOfLRa8BuYmeFFGsdH11qJh3axKMhK6asWyFYameFpJ9c9ySZ3jEiH+wMahSKLk7eH2JktI/V07dBEraIsXekl+jtMwAM6tRJ6a5n1K5Ff5rmZ2nFg++/ytiGj+Onn1nlU0HcGzXknxFjeYdt80kH4f4ZPPUbjERmGVh3ppugVa9s7xgURbzjPY7KVMNocaVWaLKDclxLrZa930T514ocW50w9eTd7EweRn6Xds6zeq7dCcKJDJbcgSqRHMQJSa7kqtvI5cLKcWbuKMmkn7WfvY7gKpS2Aq/eIn5/gM3HvUSOj9LaNU16rhFX49oWOfUHsgSDGVrb1xh+mOL4qUlWliK4n3Rvlbs12CeDpCbbCHTNsfbFkOl9SJJIbGPns8ukvfzq52dwuQq8+dZ9/B3zJEd6sONzbHRNZVIXO+c4VaEwW2jKWdCckEB7ig9hhj+6TOvvvkfNxTusf3HO8PhqEGO7/aQaAbHVhFzfSmYqU1tr+3wS8YYlYoEETTHrOwBKWJFoc+LOp7VNrSQf61+fofGNLxG9GYoFN41vfF6yfrpyUARPxs/TC1/j++gNXDpBZXqkWO+eCp4cqZoNAptRQyK04EoTTvjZfNhP9NRjsst1aJU4lhFxZWoI+LP4jo4hrkfxjvbiWmjWeyya0OvzrlCSprc/BWD9+knmN3Zb4T1Pe1hrXMR/aI70fCP5WJhQfymMUxThdK1Aat38WmroBS67lhsRbLq96C2WwPr8bHVu1SvPrD0afaf3vV7fGM0IRHL8RsrNKSod5VhJq4odi3A5MPL3dHK+U1Lc55Oo1ckn4WTCtTIAKa1xVsiHXYJihTRDKbV0eroNLt4lcmKkZC2JhSgkguRWalktFGg/tEZyvJ1gzyzRmpLbQ2PTIzbjfu7e6uZMbtsSo7zvjW9OsnHzOKiCa+zeS0/v4pZPc+Nbn+PvfAVXJE/BgpSS0sKuJsNmz0iPBDtxndDzLZbbitV2pk5AYyXwT/P3XIQY4G/dW4vw85YA5KBBSZAlocjssfucmOrHW7BHWKxYkNVGGDuxJlvKD2KBYjTG6PrOBW3vahvpkV7artzBtVpPvuDhr117gaXIKp8dvcnx97/D2KUvmTv6kEP3T+2y1mkFGJrdkzvrw531sdoxSc18O6D9zFzBNO5Qmnw8xI2bXVy4MsqT0Ar9Ce0gViHrpZD2EvnwFYqhBMW6dVxJe/OG1pxRe+kGAKufnCe7vPPaW2OGq0A+HkQQIdCxBB1LrK6EeXC3k2Mnpwi1LD1T+jG+lmbZ7O6frli09J3FMUtrTrZLjJX1kuuvd61quWto8bXfWI4rAD0z/H7oGNshpGYRrurGalev1MlvZnCqlxwpGUcddTCt+lp5znqWSysWTS2UY6Va+MkbBPsm8NRvILgK+NuWyIdjtEcyPLx3iI31AN2eWto61nl47xBHjs+wtBjlTM7ISiJsEeNyrI+jI61MTzaQyXg4K9ZQOOah4ZXPWf/qzK5JQw2jbIZaxxp91iOyRoOkUVtSK6FYhdWgPLOyVz68RMNrXxM59Yj4XW3N6d+4VDiH0xTBSmi1N3miXugfKWWJvD8ILebPp1yXCjsWtS2IRYqhJK5n5Fh5vO/OKTLHHxGojdF28zwT8QhTjaVUyE9Tbop3TrDw8mfce9jJd321wLYs2w5fYhtSddlAgoXeJ/g2aiBZq32QWOrPDa99hfTLF4BRDnUvwAPtsSb00SvIb8i1UYtL4ddsB8r3LPoyuCNpVj8b2jHGDRZC1EolUj/s2qTld35FxFXc+n18rJlH90tkeHK8ib6B+R1lW+1H2e4J3AvNDKa15zYr5egZq4zGRAkJSSwgFrepn9lutZXgcbXm+GAhtNWP9HbilfcxmhF2LEzlz+r73Es4D6c+IJjKWWsU5cLKRFuOpVbe8rVyjFO/Xyvn6mG/JOisQvls1PeoN4E6mVidEhkp7ybxuI/1L86RW4tSSHsJR0oBdJ0eD+trEe7f6SKV8lAoiPzyZ2d4/KDDUn3KJ1cCmYwXEJCyXnJL9WzcOEndCzcohWobo1Jb23oWXnmgVP876OhZaUPKi4T6J4mceoTwLIDyoOF5I8bVcKlQI+/NsNQzxrnFHgRJ1CSHMomU/9mBXQuxHoS8B8+M9jjR7xU48eQYvdcvE4jXAFA/3UkuViJfozEv+Y0w3c0xulvjO0iw2nJsFY2ThwnEooz1P9Y9Jvmkh+X3L5OPh2h6+3MAsscfUwhvllLZa0DZRp3MRVptPL8ZQPDu7pPrQknT2F0bQ3AVkQqld5BJuwkG0wCcuzRCd88ikUjadE7VGqPTF29QjGya1tmobzoZAyVPlvS5Wzu+0zM+DRZC9HtFJLFgu27yb3Idi7406dN3t96vhETq3A2yXZNb+v3K4/fbagzfEsuxFqq1BaCnz+rUPcAp9tL/z+k2zX7Czja6DDOSqZdQxC427w+SeNyL65lyRH4jyhBAHm58neXqy4+JbQTpXenSLaMa1sYd9yNICC6Jxrc/Yf2Lc6bZqqw8x0pY+pQox99db9emUrszAIIkUvOj36UQiZM+eZf6V74mdvP4M1mqykIQJPyBLB73OpGTj0mvNDE91g6p3cFRSjyvVuNq43HvMNHFFho2a2nQsKY61SyuJpSEQo9ceNIBXIE0CEWQBIKZAA2BLKR2HleOFTy5GSJ/5Al8pf27lHeT36hh5Vcv0PyD9ykkggiuAvG3foVvshP/jbMI6EuMOXUXkP8fKARpevsTBHcRX+sSmek2UFyvh1ICH2/9OlJRYO2r02QXmyhe+IZ4PIA/kKG5pdQOXPPNO8pWQ28slKUe1QGFlYyr0drdEgpuAtfP7zpGD6kLN1geb6N7qresuggZH56xnq33CqVkMLnOKbyTO+c5LRlA2HvC/K0ix2b6e+UOTnp6vFow8iuy2viddBIrW9BWfYfNfICeZ7Js5IYBxok+1H/bh4CU95Df2O2T17faBYURmlvXubUR1BWzrwa2/GxFP49zKfoXIywtRTn21uekplvIrUcRvTmQSjJxubUouZU61PJvRq4CZinEnUwO5Uwo6vPM5AKduDi54hGavrhKdmAU10vXSY51sXl/gHJl8wQBItEkR47NUNewSSHvQkj78dRskq3foPHwOLHbR0lPtWueb4cYH6RgvEpD3X4ksUCuc5qmr68ysbGTJOol+KhGPaoBbzqAOxGic2iYQjSOL5jm9FIfUDm3EP/dk/gfHAOg6E8hFFwIOe+utnbLvcLrRQl3ZJPPPz5KNuPh0gvDFN58l/THV7dcxuw8FyvuTnMn7xDIedi800/Nufu4fN+w9sXZ7dgNUaL+la/JLteSj4fIb9SAJBJaaqPlyBg+X26rLN9DbXcpGbrzhA0NdKP7tzsHC4pnauV6wc+ukBFSunxGL7BQXY6AwGhKpK92nWIghWeujeBXl3Xr4EQNo9L4VpFjGUrfFT1U2xpqhzxrQU9AXSv6U32MlTLNsJerNPU97EUnMCPL++kLWkz7CQSyljLzVQvra2GufzUAwLETM3ibVvF3LCAVRJKj3fg7FoieGiYfC5GaaiMxfBi7ZE+PKFuZ4JRQHme1/dglITKRVE8SVssREPCN9OOZaaNw9UtcF+6y8c1JkMRdbU10FfH7snT1LBOPBchk3Gysh8jlSsO1z5flf/7P/0ln9zJXr4ySmm5j+dr5HSmrBwshCrVriG99SP7IU5bfe9HW/arv/aBgL6zc2SMjhOJRAhslVwR1BL4TF4rI7p3pXVDLfdrtB8o6GuHIzYusdE4QHOunZqGV+aJOtLQNKJ+RgAByQqKL1ylG4vjvnsKTFnkoxUls+pGk0vEfvHuGyy8O09AYZ3K8mc8/PsaZ82PUv3yNtS/OIWW9jtqfVt8cLISYO/sNob5Jln75IsWshxrA17xK41ufkRg+XJL4E6JIRQGp4Gbl/Re2zk+Nd1BM++i+fAuApV++SGHTD2XKkqqNbuXqI2spZZm5g+r1K6HoApegf7xYQPRlKaYClnyXC8UYK+3TxBajO753Oq5WE99KcnzQYCWgDKxlnqlU0hI9OM2wU4nryX87sVCXu9hRdlAtn8a9JKb5eIh4zEPtPlxbC0u/fJEb2QRSUQAEhvKNuEIJAl1zPNoQGDg6Q+uJJyy+88qzRCL2oGddUatNGJGFvVzMlTN4i8kQ9R++yvKLn9Hw6tfE7g6SW6lnqFDPTNtT2jtXaGtf1zx3fKyJaE2KaE2SpaUomelWFn/2+q7j5LrJQVruZ/rV7toNpJwHwZ2n/dAy0v1jlupcLiE9CBOdVeTrV8j2P6H3s1cZy5QXkuN0+1/rb6syW1b6gX8zQsfDnaml7QTdydcyw7ArQR0SQipAZmAYnyfDJV+etdUwN6/1UhpLmhCER0RrkgBIksCt6728OjRH5NgTYrePW64T6O3sSLiCaRbP3CDUukziSTeFzdJx8z98m5rzdwl0zRE+OoorkMXX8jpr//miRukCmfkm3v3ZEGeFKMXsbuk3q3UsR07SqC3olWu0k11W/xbAUxsjo0riAtqZaAdjUWK3dr5TpduLuo77OXZ8a8ixmQVVL1uPkUV2L33E1LBDeMshBkYJT/Ss1FrnO712pWBUV6v1Mwq6NCJneoOLnm+62jqk/l/0pwmFi45JsRWXDztlf5NOoYzdveVeZijRyObDfpbdy6yvhnnre7dp/t7HrH56nuxSg6EiiF2XFDuqE9Xos3quFk4HbqHgpvGTl8n1TCBevEsx60FwFWkoCqQmOli6d4JCyr+1zSt4cnhqYjR3LPB0xk/208v8X3/xR/zf/+efm14r9Is3SbzxEc0/eH8ruxdAFsgOPkH46Ws7LM7VwEH3aZbfo+TOkT5/C9+9E8ysahPFShoi7DwXo/YmuXNInhxiKui4flb0mO1AvrfNh/3UvfANG9dPkVloAleB0Guf0dO7yPhYCwDF+8eJnnqwde5QvonEEx9NF+/SatDHCnWrCIkQYtanSazk/wM909ScfUh6tomNb06QmpJTgEtEz90j0DVHMeuh9hdvM+qNk2lsBtZ0r3sm14SsX9Hw+hckxzpJTRzSPV6GVSOZGbTmoXL7mFq6Uqu9abbBgovMXIthuVq7gnowctPYa3xryHG5TtuVGPDyDStkTt0j9OGrlq9lRtqrCSP3EyOfHy1fZLu6x1brZxfVfG5GKiB2ztHyXVd+562P0VkP9+/oB+TJcOr7XK7LhjzoDeUbucUyH713glffuk/9S98w/+M3t4id2vpup77l+NzvhXJNORAkEe/Tw3jGu8m3LCJIAq6F5h0BK1CaLKSch+xyA9nlBoyF9bbP2bIex6OEPnqZ1OVrEPPgHevBPd/KzKFxas4+pOX7H7Ly0aWtjHG/rpDEAskrXyNuRPE87XFUhhExXRdyZRMYvcV5MZCi2yXiK1ZGAksvIMpOHWXk1moIf3kZrnxNcrSTzZHD5O4eZ+DKzdIBI/Xc2MzxVk2KaE2C3pVSwiPRnUPIadOTYVcCXAVa3/wI93Q7wS8va14bwF0TI3BimGtf9rOyFN0x/qRO36a1e5HcRpja6+cR8h7681aSvUgEumdITbbjisbxNK5YIsc76q/4W+1WYKecShHIaii/VFNNptNTfX60L+Q4kUjwF3/xFwwPD+PxeDh37hy//du/jSAI/Nmf/RkPHz7k7bff5oUXXjAvzCaqSZ7EeBjvkz7Na5qt6A+CdIkMp6TUyB9aTgJSTWJczXL2CgOFIKtrEVY1tnSrOdg4gZJQp1I+fv6Tc7zy5j3ckQT5jeiu451kvTOCERGu5s5PJfxCZQiSiGe+VfMaegHAdkmWa72W8C++s/VZcuWpOfuQ3FoYT90m/vaFX1tyPFgIIQlFUpeugwCB6+d2LVDUMDISqI+pNDTbmbvAZMqLqKOHbGT80OtDlaz/2GoA92dnaTk5hhh4xMd3W4h+doTjpyah7Qtc3xwmfm+AoQtPib3buRWslnHlCVNq7wOFIPnmRaTIJoy3QsFFarKNzGIdGe/Glo+zDMGdx1O/TuDsPR4/6GBlafd4dLhvEYDary7iitVavp+BYpCNU8MUkgGuf9lHMuFHLyxP9GUoZrwMFrbVfiqlELPXllUj41C59+MKJihk/FAo3/+9UtgXcvyjH/2IeDzOP/kn/4TNzU3+1//6X9TV1dHS0rL1/f/4H/+Dixcv4vFYy0xUyWA05TlGwXC7y/bCUgcYpHM8SLAyaGr9ZhValmmjRUI5A/LzrKQhoxDcZOXcTXzRTfjZawzl7WXlcopK+jSvr4aINK9okmM17AzuSrK71yoqerJvyv+NJody3TK0jnvF8//h7T+s0zjaGKV6Shz+8iLhlXrmBx4jCAdvbNorSGKB1OVrSN4cwU+vbkXzK2FloWU0birlwcqF1mJM3AxBTn+ssJK8wW4d7CzWmr77MS5/lsBffo/sWx9wqMvF9GQjX312hJeOrvDiuWlWPztL68kRZo7eZ3SklfPpIK5AmvSJ+4gTLYwJSerO32TjxrEtMrbxzSm8zcsEemb4fKJUn3PeIA2vfo3gzRKPBxgfa2ZmqrQoVy/Ol9+/TOMbXzEdXaVbRY6NdrmkYBL/cD/d851kXdt7Ob7WRXLr0R1xF6I/g1QUGUa/vxv5AxtBeZ4RQVWXLd+bHfdAPdgZB40QPv6E1GQ72cUmS8dP5YRvX/robDbLgwcP+MM//EPa20sSQ1euXOHWrVu88cYbBAIBIpEIoihSKBQskeNqEU9lZhe7/sESEk+ufsbh65dw57QHxv0icFr1tRP0plWeGdYV0drVuO9KBAbuZxkSEsnXP8EjwconF5GeQ2IMMD9XR/vFEXIbYcOBzi4x1vpbCwdxUeR0wnASlKV17u7zBJ7O1QEShNdZWowyo5NF8tsLCV/rMokTTxATwRIxLmhPh3ptaq/kprSImpIkCzrzSzWgpwJh1Mbjd49Qe/Eu2b6nZKQiR49P43YXGB9r5tNHDbx2aZPWv/YBm5teertX6arNsXHrGInhwxQOzdL74Cii5GL4ly/ukkDLLtVTSPppaZujbWqAQN8TXP4sC3/1OjelDcN7yW/UkHx6iOjpxzDdu3V/6oWM+v6eZIscXqvb2mG45V7ildPzBLvnSM+0sP71GcU1dhsJzBbSVsYLs7gY5THK7/RIv5o4W72+U2KvxC33Mucf9FPMHywv3z2vzfLyMoVCgdbW7a3EtrY2PvvsMwYHB/nkk0/4t//23/Laa6/h99uPfK8W7A56eV+GzcZlFgaG6Xhwctfver7GWsdUmvzbLa8cUW752CFbV9wfOCW36nSXTpA9MowUSLP+4WXyazWOy7GDaqhgLC1GSaU8RC/fZvWXL1FM+21HIFtRbVFjr3yMKxlcphf0Il/TriST0XVKkPB3zpFdqqeY9iO48tTWbzI5vnsRY9Q2nnfiLLhzRM8+wFu3ge/uKdzTHaauFPsNM1ID1d9m17IY611zh0zjdBvz0224IpsIzdPcu93J8VMz5PMuGseOsv5VHdnTtznUtYJUBHddkcY3P6eQ8uOJJijWbiCu1TOY3x00KLgKNLz2NU2ePOmTU/j9edIzLUg5D0OYy3Hm1qMED0+Tr19hbGObb0SiSYRYHklF2FyRTeou34L7J7a+u1QvEOyeAyAfq57rgRnMpNTswMmultN7HMo3Ejx1F5c/w/oXF/Y9EE/GnpPjTKaUK1BJfAOBAJlMBpfLxT/+x/+YbDaL12ttNdzpkciYH2YLZokxLFkMMgHO/NVfQ5CMB10zP2Q7aUaNSLbRtrSV+zkIotzVhNV70ZoQynkORW+G7NFh0oefUoiFyD3HxBhAKopc+2KAl15+jCuQoT+xve1oZdCzs3BTtue9yuIoTwCVmujMpJcqKrsESFkPxUxpbPW1LuPz51ldMc5+qIaVtnNQCbQYSFN35SaBWA2Bd7+jay2uFCrRTqw+y0oTZS2lHa3P6uPU7UMm9i5/hvqmTaLRNIlbx2jvH+PryWfH3unGe/8k9S98g6d2E0kCT6CUqjn55kdE/vfvaS5gQgMTiJ488z9+E3d0E9fZB2TmrSdrkvIlN5rkGx/j/eQChaQfoT3N1ZcfQ2GE/HqU+USQZD5PNuvm6PEZAGbJkXnWRxcO39wqz9u0Co92X8eqSsNgIeTYEqulF6yG/Dz0jtFyt3BSBy0YWaXzazUIdTHH160G9pwcF4tFze8laXtStEqMKw0rE6vVybfPJ9nKgmNYTpnH6ekHVwPlRtDutb6h2QSgPG6wEGJA8jPsjmtaMZwgf2iWVOcU61+cI7f6fBNjAI8nz4uvPkQQJIoZ+8EVVnZU1Mf9BuZw18Tw1MZJTXRsfeepXwcgFM6Q2KzsLt1BtDyLgTQNL39NYLIL3/3jVbcWH3QJOyNYqbvWMer3Lie4ueVehjU4MdFIZ/cyawuNNF64RyicJrHpZyjfiJSHtS/O0vTdTxAEQCwgrEQRE+Fd70omYQ05P+uTbVB0kV+vYeWDq9ZvUiwSPfMQqSCSWWigkPJR/9pXBILnIecGQcLbtIa3aQ1l8uTEky66pnsYcZW0mcPuIrLXoLdxHZAoN/ulGfTm2XKJbTVh1O+L6QC51RJfOggax7AP5FgQSo2mWCwiitvkUfn3fmCv/GCt+kzudYa6St1/uQ3aiV9lJTuRmYZk/Afv0OrNMfHVKbqn+su6loTEWvM80lzTszTM1Uc1iXEgkOHF1x7icpXabtNvfU7qSQ/uhVY8c20mZ++GVf3tSmrPHiSo9VrLgb9tEU/j2g5y7I5uAtDZtcyjB9alqMrFfhDnfreL5Is3cE/0bKU2rjbs+uYalWEUFGrFKlnpoFAr96H1Lu/f6ST49DCN3/mc5KNerr70iK8+H+RW7JnObrqR9FQbga45cIGYCFHomiU/vIZ7ffcY6Z5rJXDqAfF7g/Qn6209X09NjGLWy/IvXwJgtH6SF3058rEwsdtHQSwyIW7SlamnpS7Bui/JWEzk6pOzCAjP3ucmba6Sfngh6UcqCjS8/iWbjw9Td/kOAB++dxKXq8CxYi35WBg94mzHYmzlvVXLHUwP5VwjM28tEG8vIUhKk+0eYGZmhv/4H/8j/+pf/StCodLLu3fvHn/xF3/Bv/7X/9pyOWtra7z33ntkv/EgbZZPrNcLUOvaGThWKdRaMKCpr2vlnEqiGvetRPNpD4t3chUpa13YXU6tVF4Am1aZWgj2ToFYhKJAdq2GgCDiWo/ukhKyglAoxVp7SS9TylY/AG9RTFk67sTJCPfvxW2X39m1jChKSEgkNv0ERBHRnS8lnxBAyHlwrdUixvUnCC1o9QW9/lLtdrx1fYvtpZroPuVn4m7a0bnu6Cbe5hWWF6NsVthqXEk0F3dn3lLDtO8LRXKHZghk/IRXGlG2Pa12VI02ZHX8k9uV0/FM2S7lMrS+M7uelXNkyOOK8l0tiqmtz+pxp9Un4O9YIDXZTioYJ1KTZG6mnmJR2DrHFU7ia11CyHqQvDnci82IGsFtUU+B5Z6nuBabcG3U2OqXrlASb9MqqfFDLIopQuE0Tc0x6uq6uPlNmtb2NSRJwO/NI7iKeCa62MgJO57JujtN8PD0VpnekT6K0TgURPLt8wCkUl58vjyCJCDl3BTSPnJrUURfFn/bEpnFBgpxfbJbK3l23Zed9mHUpqz8plUfK8cKnjwI0s65TXzmOVDc5mzq+/M9eya1ef0MhHU1Rbznc7z11lvU1VXHsLTnluPGxkZEUWRmZobBwUEA5ubmaGnRz7JihIUfJclMlFenSkiIGVl87dCM/bAca13fDHZ9PIf+KMSt/1LZYCYlrKykrfjkma1+I6emCfVPkppqIbtSS33HCsHPLzuKGM++/Stit2D1owi33CtVs5rZtRb//X94iD/579PmBz6D15vj2Mkp5hfXt76b/+F3cNfGqb1wB3ckiWesh/WiRKBrBtGbx/OkF3EziPfpYVO/z0rIClYD+71t/vYf1vHuf9PP5GUEf+cstRfuce2LflaWzWX39grqPvA9t8hoRrD1rNV9O3X+BsG7WQ5fv8QaO8sx0mavJOyPf9kKXDVr2+Ksrd29XRc9V4rSe0tvnX/LvczfzHQ/Oyet8jeVqHv5Ib7GL1n4yzcQLt9ldTnC+FjLjmOiQ1MED8/gedyP/34zxZopxPVaBIW7Yq+/wJ3LwxQbv8T/+WXGF625ptW9eB1f8yqbv+jl05FUKSjsjU+I1qS4dEnk0eNbTE6nyedcBEMl4ua9E2dytGPHs5GQWLw6iq9pFSSIvleDmApS9KfY/MGXAMQ2AqQ/v0gx48XfOUfk+BMyi/WI3hz+ttLYvPLxRXKrNbijm+Q3IvDMMl16frv7uFWL/4h3A2/LMpnZFuRFoVppwmhnQQvW507pmSFJYd0Qioi+7A65OzVqzj8ks1hP1/ig5u99PolsN3Ser65Bac/Jsc/n4/jx4/z85z8nFAqRTCb58ssvefvtt/e6Krtgxb3AKLCtUnWwg0pvKZtpH6uvt98KAk5944w6vrEs0VHid48S6JkiPdvCzNNuIAeunOH5WgNKLuMhMXaotMJ+jr0CBo7O0taxDkBqqpVA5zzN3/8A0budrjhw4ywBYPjuIN7GNepeuIHgKlJoXGX62knDwV7th3wQiDFoTzLPC9JTbXDhHhevPuHnPzm3b/WwsiDs80mQsd6vZBS8abInHpJvn6Pw7luaPsZ6CZoOUjsrB1qSW/JnrWNlYus0xbv6bxnK8gYLYWbmm/E1bhAcfEr20RF6r9yk7umgYhgUiN06QXaljvrDM8T/4C8B8N0/hu/hdsqNh4ee4lppQMz6KLQuggVy7Klbx9e8iu/uCeafdDxTUpKI1qR4/xenONwdpZB3Uci78fry5BM+3KEMxdAmrd9/H7x51rNuRG+e4AcvU5OoIduxRGq6FTEVpFCzQeI77+MeP0S6cYVoTQqhe4bE4z7Skx2k2mbISRlCw31b5LjhlWvkkz5Eb55i2ktyrAtpdNBWwGjRnybfPguAd6zkJd3n9rB2dIzcau0uQmrUd5xoHu+GsJMYA0iiJjFWttP43UFEXyUWh+VhX4Tlfv/3f58///M/5z//5/+Mx+Ph4sWLXLp0aT+qAmhLcRllfNMihPs1mFqxXFfqGnbLVx6r3hrXqrfe8zPyLTbrrHYJi9XjU+PaEuRm6gMyJCQKTSvURGOIvhzud85AXvNUx6imf7ES9+908+BuF2dyjSBIIEiEPQUKLct4HxzB/+C44miB7HI9iz9/hebvfUT+0CyRjIsFT57MQiOdE32GgVKV7mOVsBRaFcLXssrtH6EW2Lh5jJqzD+noXN5KlFAtWCVbu/qz29l4JnmyZC7cJN8+T/CDlxkwSMJxEP3VK63nbaQ0ofxbL0GE8jwthQO7SIz00LzeAC99iS8ZJZ4I4mtdIj3VvuO47EIDxQv3kCQQBMiceIh7vhkh66UYSuIZPYyAQK57ko3OSUvXFrwlQ8bAbCcTlLb5fa3LJJNecjk3mbSHp6PNXLgyytPRZk7dv0ChZp1c5zR1C61sROIk3ClCXhCyXnz3jzE73Ugh5WfDlaDvmUJVXdFH5uff5WnzNA2vXCNyfHSrDqmUh9idCF98coQLp2Zx18RZWvMjfH0Jb9MqocGnrBwZpbhSTzgeIj3dauivDJDtf0L26AgARXcO//ARxGSQjWuniZ55ROLxYXLrUSQkR8GoZnNtOWOash32p+rBmgdgVbEv5DgUCvH3/t7f249LAxBrWmCh9wndt8/hTZd8nJSTpJoEW7XO7ucgexAHeDX0FhVafysH/0pLy2hZUPaarAgIBD9+keQrnwFwWoiirePiDHtFjGWcyT0LqJAEWr58gUI0RuKND/HpBD9JWS/Z1Vp8jWuE+qZIPOkiNPiUjbP3cSdCCHk3xUAKcTOM7+FR3EvbARuVSNNbTUuhVltSE+iDgNR4JzVnH3JqaJL11TCJROV8j+0kEtDDsCvBcH7ns7Oq/Zw+dZ9iIE3o3TdxxaKaWUvNUE15QC05zf0Yw+0QY7MyzL5Tfj9YCMN8mOLtE2TO3Sb0cAD36Ufk1yPk49tKQMW8m/RsM9mVWiInhhFESF68TsGfQfTmCXx2Bc9cG66VenwXbtL6+++Snm8kM9tMeraFzsFJPOM9iIkgI89EX5ub18kDrryH77ldjGYEFs58SH6sG38gS0fXCheujHL9qz6Gzj1FGk/iWWrhaKx5q17vFDYRzjzE17xIz0YjmWiC9MvXoCgSK4r4ptvxJUJ0+IrkvNtcTyoICC6JQCDHwsm7nIg3IPqyfPrhMQbWSwaX7FID2aUG3NE4nvoNfG2L1PdMk08ESI510Tl1eKseyjYjjRxn9OFRJKGI5N8WuBU8efzti/jbS+myE2s1eIf78MwcQii6qhLQ7vRcbbeenRjNCBzfg63Wg5WSZI8QWW7Ck/ZTdFmLvKh2Br7ngdjuNfZKQaMSEeVOIWR8FJI+1hJeiinzwKODip1bpqXn6YpFif7or+06Vvl8s4v1CEKR1Y8vUbKISLiCabrCOZAExGSQQuMyqUvXcS014r9zCtHAV80K9rKvWdm2dLrz8XHuH/F//59/7rxyzzD/w+8QGhjn/OUnfPX5IJm0Pd/5cvzk9Z6BEQHWWnioCXLRmyF3aIbBr68SykQcEWMl9mJX0Em7tLNINCL6TolxOSRaro802c+tM/cRJJHAg+PUvXCDr6730LtS8lem6GL9qyEAAl2zFFI+IvEoLl+WfNfMs+BeEOMR/DfPkD38lHDRjf/cA6KnH5F1F8kOliy2XY8G8D7pZ7PvKRRExGdb/n0+iZW8B1f7AqcTh3G5ikgSxDcCxDaChFoWcK3tDPoaKAaJH54hm1rmceAuFARElwTPRN3yh2aZPjSLNxliYLWZRw+Okj3+COGZks/De4c4dnKaYmad1c/OM6ARbJiPRcjHIqTGD4FQxH9onuDhaWJnH5CPham5d5InK/X0u0o0TkCg3+0CXIxubvfj3GotP//JOXz+LK9/5x7Fug3S524hxqOaCiDyuzIao6o5R1qZg7+V6aMrjamcQLPNVYQgiQTi1hz37azozY4tuHPk/Gn8m6WV8W9IsXNU06pjRUzdShlG6KiP8+iFT0k86uX6VKBiGQT32mKsRYytIvG4j8TjPsU3AoVkgKfJwA6S7ZnsJH32NonXPubE5y9DZpsg2yUuVuXhqg0rpHhvrMwCiZEeIv4M5y6Ncu2LAfI57WmhmvrEduXIjEhyrmsa90o9obX6itVP6ZtsJ2hZPrbWVdnxXmvnQw9WsndWkxgPFkKl+mm4yAgIDP3kdxnNCAgICHkXly88ZOWjll2+qaI/Q+zmcZqXn8kRfr2zHO9oL97RXiR3jtyhmZIvcjxMoWGVbP8o2aMjZPueggCebIk8ys/l5K2LTJ28TaghDgTIrdTy0sVJMgtNZE4+JN+0hPT1i1vuCP0ekaU7Z5jpH8Y120K04Ga9fY7mkX6iKy3k/CnGz18nHY5Rs9jCsacD3D6+nR3k2Mlp8psBVj68gpTbGVimSQ4lkfRUO+mpdgRvFl/rEuKFbxDEIo+/vsjgepOhq8RQvhE24dMP87zWu0m+a5r0+Zt4Hw/gmW1HUPsGK+pSCVj1Y9+7cc8czz05NsNm3Qq+ZBBPpmSZywQSbDasUD9ziKw/hS+1+0VopUs2C8STfzMiyAV3HqG4U3buN9Zjfdh1a6kkzAJMZBil+9U7frAQ4snhETyTXSSfdkGFCO1eEmP1QFfpAU05SAp5D/5r55k7dZv7L31E94OT1M61O/Kbe1762d5OEALxu0eInstz7uIYX38+wFC++rqjcp8pxwqlJsmFSJzMiQcEvj6/J8HKANnuCYScB8/sTn/Zgz62yxZjgL+Z6a6YhdxuHIyAQL8PQEKa6+JBMEXD618Su3lip/5tUaSoIpKa5eU9eMd7tj67VxrozQVZ2ayhYaqTnC+LN7XtTjmaEfhoxUt4oQ5XOAmSQP2XV9n8wTuIgZJDRKFl99jaNHGYpoltFwdu7fy9bnZbO1wsuuj74gUmh27Sefc0M6EYnHhI0299TG6ljuT4IbLLdUg5j2l/kLJe0pMdpCc7mOsc4dSVL/liuYG6sV6OrbZt3dcWMqU+8me+CYYG58m3r0NBoFi3wWyxQFZMMli0ntSq2juran33/SLL+5t5owLo9Bh3PG8qQKxpCSh11qn1EJGV0sQu+xtrwSjRgJIIy7CyxeVNB/Ald75oZerb30AbZtn/9H6Xf1P/qyQGC9vpPpV/m0FIBShES+kyK2GR22uL8V5DQGDzwQBr9/uZPnWH+2+9y/033mX87HV6/c7dow66IoGTIFRnEIjdOkbUV+BKa4UjQ3VQyUlWfiaLvSMIWQ+pF7+qWNlmEPKlbGrPI4byjZrEuBLjpJPxNh2J0blRz8Dtc0SHHhA+NoIs5ZOVJATRfmRGn09iMhYgcf8InkyAYKwGt0J6s88nMVgI4W1co5jxIhVFnuTyZBbrENzbY0uyzplsogxfMkTvtUssT7bTN3+I0z/9AdF3v0PNXAfhI6O0/OADuo5MgGB8j8r+3zY1wMq7r+BvXWLOkyTesESkd5qCmCfrT5H3ZMkcfczm27/kameKlrZ1AHLxMImRbrILTXjqN1j97i+QVDvwyoVnuQmJKuF+tdd47i3HUzmBeEa/M3vTQRqmuwDFMang1u9aagl6ZSkHDy1LsbIM5bHFYBLJk2UwvTfpgX8dYVdFYy9k5ox+m12opfH4I0R/2lDz0Qr205VCRjW2w7RcW9LTbZxcOMyIPw5IbFy8wUbzAky2G5SkDa0+fBDIstpiovdclWL8FfGVL7pIjR8ifHQUT22M+P0BkHbbT+T2ttdpoM3a2EAhyOahBYREAN/wAFCdXSe1IcQz02F63H5Cb7zTc3fQms/U5xldqxykw5ust81y+MZFznzyGvde/xXho0/J50Xc7iLpUJJx3ySdm3W4No2tnfnmRYRkgFHFcUaKVAFcePsnEVzt9OOjMDJI+uQDpJo4QjxIKrJBIBZFdJDwKVmzxuNXPiK4WkfjZA8Phm7S/uAEA6MDMH0Ypg+TDsV5+MavaD3+mPkfakvbavneS1kvuZU6as/f4+lqHQsFD5vHvkESd76LmqGH23/fOIt7tYE2YDS7iTucZOnq5zR98YJmqm71Z6OxRjkfKceI/Ro3nOK5J8d2oTcRqv+208nNJlQhGUBweX8Nn/bBxX7rmHa5RZJAZ9s6E+PNmiTECvbTlWIvoDUIC5KIa70WAPdMG9MNCwQskGMn6dz3A0ZqKkqsC7mKay1nV2uJnBzBHZngs5E6igXtdnkgJzhPDsmfRgqkcV2/sN+1OTDQI4RW3Nb2etFYN9dB3VxpseHJBDj+8es8PnYH71od6SOPiZwcxh1OsSlBbqWW6EQP3qcl1wbJlSfXPYl7ugMx6wNBKln1daC+p+CnVylGNuFKikLLEp7Zdjxz7RSisVLgX/8Eibo1Isv2XI66W+OkPAVG0z4O3TvFk6EbAMRrl2lhYOs4fyJC160hJoduIbjzSIq6m6l/DH9yEVcwSbB/AqF9idBKI4GNKEv9owiJAKFkmM3aNTxThzj+8FQpGPFZW/hOqonbm0H8HYssXLhG882zCAW3I7c1NbQIsRMd7f3wRf7W0DW7cmtOgiqcQkCAgpvRwsGcjH9dsZ8E2b3QwsrHF2g+MUpH6zwzX562TZC/7cRYD8p35l5oIXXpuuHxei5Pz0NfVPrlqieHkuU4u3Wc8n8ZdsnytY08V9eDzM/VHkhibDRJCjkvnqfd5A7NIG/FPw/vuBzY0aw106s3c1+zqohRSfiSIU7duMytH/wlIpCYaic50o3gzeNtXMV79g65zhkSOQHRl8PbuAbnbpNbi5KPBxG8eQI1m3hG+vBMt5M5fR//3ZOIid1tSCi4ca3X4loJIm5uC+26YiUlicbJHsO65nxpxLwbl8KyLCFxL5shPN/Aqanvlb4MlFK+uzQs0A1TPUwO3aL+5WvUv/em5Xc7WAgxnATBXSAfSJPaiDLw8BQdj06w0TKPJEi8NH0Ef94HzUkAJuYjz96rSOiTF9n83i8J9swQ75pBECHy4+8zUszt6G9WCKqWtVg9Vz0PVuRvDTkG/QAAvWQeTsoqF3rWq0pZq7/tk8FBgpMU1srzBhc7kZbbSbz+MYGuWVITh0zO3MavKzFWYyzmpTWURHLnEPLW04ka+R9bcatSl7NX2SCdymdZJclD+Sbyd9x0XbjN0yfbKWeVcGL52Sv4Hh4l1ztBWyRNJBfe+r5aQb1mc001sdj7hJkT9zj7k9/b9ZuWXr8MO0oXWrDiglhJCJLI4WuXyPsyTJ2+TU3rMrGFelqf9jN9E9yRBMWcG0GAjW9OUsy78DWtED13H9FdRAKKNesU12pKFuD8bmUG5Q6NkHOTb1nEFdvpBql8txIS7U3rbDas0DjeQ6pmg2wgxbxU4Oh8SWRMQGD61G2We8ZpfXSEtpGS5vvA5y+T92SIrO7uQ/GGZUj78NTGKTSu4F7W7md1fROkQ5v4EiFWp9oQsz4GCyHmJYHsag0NX1wGd+nZ1c63090a35Vkqrs1vk2QE2H8n10md2SE9YVaIifGiH/vXfrffYvp3ocknx4yNN7ojTND+UbDuUpJkq2MK0V/GqEggkFSn0rhW0WOZWiRYT0XCqtlVdPKaMelw2kd9ktk/tcJVsmL8pgRIY3v0WGiZx6SWWywpHd8UInxXm99DbsSCO5nI37ebXr9ci1fRu5Yyt8K0RiSO4d7tcH0HmRYeXbq39eFnOXy7ZDk3EothaJAU3OMJZ10vHp+hfsN6Vn2sycvfrqLNO7FGLiXu1H1U114dMaLat2nHuGuNmrnS25TwbV6JkLriI2LxN76gEhBxJ33UAwkySw2UEj6yW+EyS7Xs/7VELUX71DMuSl2zCMUPKwkvLQXREZrF+iJ1TM/+BgpL9K72MGkmGJ4s8jhXITJySakZ33Ff2iWYlFAynhxhxOcdPmZ823y+OgTABY9KSRvDu94N96VBh6cukuNp0jzk36We8YRc25aRo5skdFgTLtP5d1ZRs9/hbgRoejPkO19SurSdSI/+y4AklggOzDK4eUWHp0tuWaIeRfFU3dwrTTgezxA/P4A9S/cYPmFz+m5dQl31mf4XLtb46U/5iOw2gZftHHztV9AzoWQ9VAMbRI984jomUcEPr3K0+VQKfhUR/rNKZQWZqPxRHLlKASzgPWx1Sm+leRYC/KgVfSnQBIRM8aNRo29GPC0gvnAvnVKEopk/SncWS+ugmdHGb/BTji1/BiliQbrVuTBQojheYl0UzM1Qw9Z++Kc4fEHlRjvJZTP3lMTJxcLbW0/apHMamc4U5ff5RZw5YNYS2a7Da02VenFhjWSLFAc6+FQ17wuOZaxX37oes+l0yMxkvUw8NnLO76Xx38rFk+ZxKj/tgq7UmZO4c55t/xz9wIHYQ4JxmrwLtXSOt6DdL1UHwGBEf86gUNzBHqmcYeSCN48glikKAE5N6K7QK53nCiwOTBOkz9LPCcS8ZSUIZI8pmbTjyecJtB5hZbf+RKpKCCIu+95CiDtgc0AhFPkjjzB8+QwhfpVjmzWk5rrpODOs3T4KQDHPnwDEdG0HYX7ZpB8OaTGVZBAaFpGCqTJ9o2V9Jv9aTKn7vOI+wA0jvbS+eA02UCS0bpFNq98Rd97byJ8+CrLL3zO454nnBg+AZRcKJavfok/5+PEdL9hPVqmu1jofooUTpK88jX+D18i/dqnpF76gprJNgJdc0R++DtbGQeNoDdfKS3KZtZlGcOuBL76RQrxILWhBFBdgYNfG3K8haKIUHAhufJI7jxipnIpU6sFK5O7cuCKNS0yN/iQhqmenTqMFcLWalMHeoOA+jy7k0614IQ8lZtuemcAlcDmw36avvsRrlCCgoY/HPyGGO+GRLBvkvRUm6334HSSN9p9Un6eWY1syViZuT/Jv+v5Chu57ih9ju3CLOtV9PRj1p7qBx4d1PYxsxqh4M6T92VgU/84PSuyPEaZjXFWcBDIpBaM7u2gjMlqaPUjpT/uQLoWntTCk+109ZIny4iUwVO/TrB3GorgaVjDHSqROtGjkkwripBxUcyWDEoyMY5v+FmYr6EpWUdDXYJc9xRFBFzhkl9y7M4ROoaPlerjk7YSjIXXGuh4dMLyPaYe93BqrAMkECURseAmHYrz6IVPyDesUJ8JEBoZYKFxHjGUpn24dK/eVJBjqR7i7as8+e67hP/qe8TvDeJ97Uu+bpvl5I1LJOpX8OTdeJ+5nv3Vqc8QJPj+vReB7TYxMR+h/ckxgvFa5o48JO/NIPnSuJ92MRdcpa5uA4D0qXsM3hoyvB8r7hR6x+lZkDOzrYi+DNPp6hsrfy3IsbJjic+2GSShiOTJ7ci2BQcrQ4tThFcbCK+WolVnjt6nbfjoVqrMcmBlwtAaXLtb44RD3l1zlbK8/RyUrWhUG6ESKgFS3k1q/BChgXFit3YOqHtNipuLASC9Z9d0iujQQzy1MTaun6raNcq1OjttW3ptSjk+2XGrsHvN7Eot8djuLfuDSoplCEUXnoku4k1LRFa0yb38DNUEuRKE+CDAibVbea4a6rK0yleSq0rUQwmnfVDIeRnEC4sRWOzcsatXdOUoNK3gikUQkkHmTt4hfHQMABew/P5l8htRQOCWexl/IEPXK4+YXonQKkBuLUps/BCZxcaSwa0Cyg4Cwg79ZSgpWBz9/BXWDk0hCRJ5T5aW5Vaavu7DpYix6G6NU5zpY6J2kdSLX3D445cY/+QSNRfu8Pi1D0rxqQIc/uoyE4sRenLnSYc3dd9P7UIbtQulhCIFd46lmiT5w8/2wgqQ63+K//5xBFV9lWOIPFbYmb/kc7TGGZkwFzM+kH5DjqsGQRI1dRL1iLEd8qNlBaoW4dayTrjyHg49OMVa6wyrhyZpGj+MNx3UONsa7EwaTieYSg2k5aAcC3Ilzmt5MkjszQ+I3x/cSin6G2uxNvydswQPT7P83lVLkkcy9FyUqhV4a2WLXY9A6+1OyJ9rJQ/DrrWqjC2p8Q46+54yPdmgmS3vIEebuxeaWbhyDX88Qv1Mp+YxW8/0WfYwMzcLMF/A7/cYZnXsnZiPlD2maz0TM2JshXhroc8nURQLjAQ3cK848zXVSjMuzrdufdc6OkC26CXdN4rkzdF6ZBL/rTNIvjSD691sXv2AordAW22axNNDxG4dY7AQ3nGNau0U+BNh2h4fY61lhnjzIu061mhREvntey/wUccj1n/wDk2JIOJGLflwaiuu9unlrzj7k98jstKku3hUw5X30DzWzwoFirUbSBk32fZ5Jpun6Zo5XJGFgRXs9Vjza0uOZVgJoLCrIWpm8dkr1M13EFlpIta0SP2sM3K8l9YUq5NQtVBOMI1T67GSAD1JC9TMNxE5PkLs9vFvLTEuz9IuETn9CP+heVY+vEQ+vrOtGPl8Wwl01YrkL8d6XE35K9lyXMmxRX43qak2IkeecKWlQHpm5zEHIRuj0T17pg8hfOJh4uXPiS417wpKku/xlnt5e/s2E2J0Isob3THHddrvxb2d69slyHpQlqEuz2ixYPdZJWvWyTfPWSbHdscXMRXE/+AY3rEe3Ee9+G/Wkj0yguTNIiRCFBvXcc224BseJLzcQKsFQqiU2cv50hQ8Ofw6iUuS0XWCsVrD8ma9KfKts4TXGqh/lthMhvJ59sxfJBtMMu5NUAwnoOAi3zUNQP+nL5nWWwuiJHJitOTGkQ0kuX94mrqrt8k8SuG/t5Ose+rXaHj1GssfXCG/HrWsRHHQ8GtJjtXSaWZBWXYmHqNGoDdxS2IBJAHBhs6t1UnVnfNSP6stEWZGRvd6m7FSA3Y52K9MaXKbGH3YR+Nbn/HNRBQ298Yf/nkatGov38bfvsjCT19DymrL+Tghinbk2+yWaVaG2e9miwkr1nLb9yGJSLNtuGtjMFOysOn5BsLBa0PCMxUHl6KNSJ4s6RMP8d8uueHIk/YOkjwRpc8n7fs4tBdQW3qrVbbyOyfW9dGMAPON+Of151W9XRYj9yT5dyWe5Ir0xgOIaRf+26cByLXP4rt1Gu+TXstW0tkj91kYHAFAXK6j2LgGEhz5+LVdJHihd4TZE/c587MfGGbfE4CiP8dC75Nd5HjncQK+ZIgjyRCjC0IpmO/mGXpdIqK07V45e/Q+seZFxJyLw99cxmOibBFvWKLoKlCz2Mrpd77PetsMK/O7rc/5eJjY3UGknAvEgm1lC613WZEMoDbxrSbHRluaZoLnSqiDZpQvSUmG1XIkekRZbfUoNC3jWrSedacca5N6cNpva4cSRtt3cLDqqobe1rcSRiRm6/hkkMTwYU6cnuDrz49UtI5aOGikxhgS3uZl4vf7dYmxEaqtDa4lGSkH5tk5Twt6k4NZKl+lQoPye7l+aiivIfqy5GNhS5bi/bAMmVnM3RnvDjJTDCXJ9Y/hnmqn113EtdAM7KzzsCsBmRDJZJFD9Ski6ec7/sQqrLhA2IEeCbY6httVDTJTD7JTlhqeWWup6ZV1jj0jxgBiOkCRNRDg0csf4YpFcC+0IG5ESV/8BoDwYiNCQZ9ESkhkztzD+3iA9JERNutWCK8ZW9GV9RFyXp7mdo51RbFIqmaDuukOXAaZBGVsNiwzf+QxfV+8QHS5mYapHhqAUXVdcx6ST3qInr1PZqGBrxbNy9bqx1ppq6H0nrqK5pKn5eJbSY7LFTnXK0+rwyonBC1JEiOCDKUX7l5o0byu3uBvRQvZSiR2ORbjalqbrZZRLR+/aqlX6L1P9TmJsS4aD0/T1rHK3Ey97bpYQbWITLXch3wtS3jq1ynEwySGjRVY7NZBr784aQfqscKsbDtjkxOFFKcJG7xNK7jbFrjx1Lpc0kHaOhUyXvK+7I6t7YFUlPsjh0lduAGRJP6vzzEwfQih6NrxTMdq54m9+gVjeTe/dfsl3BXWdH1eoJSz2yuUK6VZCdhVgdEab0I/fZvEb/0S3BL5Q7MApCbbiN05iqd+HX/7Ip4jc3ie3e5m8zLDtUscWW/WvEYmvAkC9M10M9G8xErXhCk5Ntu5OvTgFIceWA9mbhs+RuPEYdwqw4RezETs5jN3izLdsIrBBJKrgCteylS4V+6pzz057vRIqCWLi2KBdDhu6sNjF1uNIKNtQVZDaVG24mpRCV9lq4OLU3JrlYxWY2Ddq0G6mr7HWu9TfY6U9bLxzUmOnL/L4nwNBQOLwq8DXMEEdS/cBGDpvRfQytpWLtQk1kl6eSNXLfW1jD5XAnoJS4zqJbdBV2STmqs3uHOzh824PQvNgXGzcBUAKHhyWwoAAgInHp3mycMcFEQ2f/BzhLwHz2z7Vh98EJzjQksGigLBpSYedoxxamrA0ri33wF5TqBnKKnEfdh5HvtNiu3q0yuhdU72yAgSAmufniO71MCWXASQXWiiZ7ZnK4lR45uf4xaLuJYaQSfJZ95bkp+bzAp037zAo1c/oPPOEKIFV0ytnXD1WKc1XmiNFR4L0rd6c6CVxbPWNfMuF6lIkkh2u46dnurLJFp3cn2OIAkS/u65Xd9n/Slu/s6PGL34Bamw88ALeat0sBDa+vc3M9268iPy/0bbk3Y7vZMJdWI+YkparURkK/+ZHSeXGUtsrzbleij/7TeU28/lkhW5TZSD7GIDYiJEZ3dlg5+G8o37T1xsInz8CVJRYPHnL1GIh81PKANabggyrOxIGZ2vPE9vMrJCruW2VbJwGUPPlUxdR3n8ueVeRhy6w5PhNhbm6kzL18NeBe3pjZtCUjsAWUBgQPAiFjyE33sd91zrjt9PnJ4kdHyEsbFm8vePMlmziIS1ifggxExYhXr8NhvPwRphrva4rh5XjeZVq4klnBJuSShSqF0j37xIr6+41X8L0Q1y/U+J3Tn6jBiDekEvXzPQNYPgySLEIlsLOi1MhmK4pzpwbYbxJ8K48i6yod31ttL+9IKOlTtM5QYiy+/JFUpwqVbUlXWzMle6sz4iK9tz1l7phz/3lmMZyeg6nowPTyaAq+Cme+kQqBrK+HyY2pkOYs0LPHr9fQ5/dRnJVXSUZUhzqzJjTogqufVYjXSoepYEMyJsVqZa57jSFmWnA/FeWPHUsLYbIIAg0RkQGa/QdZ83UixDcBdAkCimnMsRGsFKH1KSViv9zsoxer+rCbKdNmkmFWdWnseTp6Y2ya3rvZavqYf9dLOQXSlS0Q1dySoxsXOhNVAMEG/bYGykheFH7QTzblpdBZ5suLHq4X4QFvp6sDLmmkm0lXN/RuN0uYHQWm2tnLZnNEZLrjzZ7gky5+5sfXdvpo3Al5dAElh74Qa5kW5SE+a8whVOIhVFis0r5NrnGJ3o1uzDUiCFmC5tkY9mBHyJMAt9I3QVgvQsb1+nku3PLrfQeneyC0l2ud5yJryDgm8NOU5H4ogFN55nGQ21GokAHL5xEUkokvNl2GiZp+ApT0h/11aqhsuFGna3Hp34cRptjxh1ICPpHbOAOauw6tNsR7fzeYOZOw3AneEmTp8dp24uwNpqeRbTvSQplfY7zm9Et3Sfq1mHSgTs7YfaSSWvGalJkkz4yOUqMzXsBUHWetfDrgStwErXhCU9VwmJ/FsfABAeOQrEAQEx5ScbSOGt0sJsL2FnTK2GrKZVTWMwDhY1c11TEzA77W/YlaBH8O66nhKpi9fJHyrtTAc+v4x7oZnEq58S/z9+jGesB2/DOsWcGzP3L3dtjED3LLGbx/G1LhFe0q+nkPFSjGybl4qPBtg4Msyd6CxTQpoXlw9bcrHQg56l2GhMMZK9lIQi9S9dY3LVR2I+yFpkhqPxjufKQPOtIcd6Yu9aECQRbzqgm1rZboIArQb0PXeQ0YzAYCHEn/kmdIP1ZJg1GqvBeVqNvBoW5r1ENclvudtHRrAiP2P0+/JSlIf3DnHx4hg3bvSwvBR1VI/naUDSwlQ+S4SM4WClRcKsEmS779+KdbaS/U2vrFqXdQUevfKU937LvczRlg2WF6OOslvpodJ+yFZUgAYKQTZTPqKL2sHOcr8fdiXo94gkXv8IKZTC+/AIfck6ii4fnvp1hIyP8dZpTq46Sz6x31Cr/diNBSknOLscGI3LZv3arq6u2bHKMToydJ/QoTmkvMDiO68/S0CUIboeIli/RqppETdQTJr75koFEQoivpZl6h8c25If1PIJFvLuUkbfZ/DMtuOZbafozbD2vXdZTzYSH9WWbLUKu7tOhv7JkoC3aY3+JkjPN+A/N8HPf+Lb9W4Ockbib6XPsRHMVkd6vyv/wU7/QLPr/M1MN2A8OVidhPI168TffB9J2M4LPyJlt3yo9QYUK76QBwl2/eGcotrPpJyOP5RvpGVykI0bJzh5ZoJzl54QCGZsnf+8E2OAZNJLKKx939UOAFNasrTGAeUxlUBRKLLRsjteolpQxk+cFWo41LXCwnztnl2/UlBKeg27EswcvY+Q91A7py3D9aSQJ9s1Sf3LX7P5g5+Tk2D9mxP47x9nxJUEoPbSbQrNywh5V9njxHphf8ZgmbhWy8Cwl7t2Wv6pyrgfJaxu4StjgoyPW0Jw5QkdLmXEWfrlyzsyc24+6mPtq9Msv/ci8z96i9jtY6bXLsTDLP3yJdzROKnf+oDkS5+Vch6we17KNy3j1pB7FbM+hPVakr70ju+rNWca6cHvkI5DwDXZgVQE0Z8qfSeWOItyrHY6PxaF6hv7nnvLcV7Mo74NqwOQGSm2cp5VKBvBrm2fYi2I1l528jsfEHrnLQRJLDn+Ny/iG+8h78uww6l3n1HJ4JSDbjk2W/2WK2CemWvh+ir09i/w0msPWFqsYXysmfXVENVQbjho8PtzZDLabhVqS4T8rCthjdgPF4lcIEXRIDCnmhBcBURBYmO9OpacvVKyCB0ZI3L8CYF330TUkGHLezPEf+sd8ptBsjMtPJmL0DTXTSER3NFPs4v1BFqW8T4eBJzJ78nnDak+V3snTytbXSWhtkDvpfXYihymHWJs9ntL2xpDHau0tG0AUEh7KaZ3WoaLGR+Z2WfBnZL18ULKeVj54CrdPQvkuidJvPU+gc+vUozEeHDmHgOfvQIZP8WaGOKY9k63a62G9UiMk4p3cRBcDUNfXyLW9UO8taXFpiAUy3azGvZskj32mNCZON18p1JV1cRzT44z4U2STRmWprejjs0kmcqZ7KxOvHrHyatcOX0pwEzbU072rrH2ySXNspTlRH70A4R8iSgU6tYgmELIe1icbWYR4wl9r1wrtDpmLOFlSpEhqVKScXsBK1mX1Nl81HBKkOXB5FSmhVuPXTwda6arZ4mhc09ZXQnz+EEHmczucKFvg8VYRn3DJoGFNlIWjt2rLErV6ku+ZAhfcu+3Gft8EkXJz2pBrHrbqYofsljE37aAv2sOTzRO4NOruGJRRtn9ruKNS+RjIZZ/9SIA9cA37mWG2PYrvu1Z4s32BfAUEU0yhzlBtXYerIyrZomVnAbuVQNW5RXV46tZ+7Lql3ylNUfthadbn2N3B8loZIUrDwK+0T68o71kjj8i8db7UAQEGE8LFNpnSjq/y9quPe6Zdp68/gkda83EnrlWOFWjsgI7BqXQu2+wdOQhwe45LjVKpGf0XV70+oGExGrnJHXTh0idv0GxaRWoLeMOrOG5J8fejJ/l2nmk6RYE9LVK7URsG0Er8EPre+VvWr/Ln2+5l1laqGV9qeR64euYJXxknPRMC4nHfbvKlIkxgHeiZ9fvI+4E9a3LMLr7Nyewa3HQi2zuVH2nTl26X2TYSTuw4kdcLkFWDt7Kv/M5N2MjbdSMHqFw8i4vvf6Qj98/QS5b6srfJlIsIxjKUBjX1ttVZ6WsFMyUJL5t6G6NEw6v89OU/cyDdlHue1LvFvg75oicGqaQ8hGZ6cTz9SVDQjtx9AHxOzuzT6rLPO/3I0giKx+fw5mn/zb6fNIu/3Azo00l2pnZmGoUfA27x37l9/sxXiuVYpTPyu6C2KoL41C+kejpD1j99Dx1L9xAECWSo11QRuCbEQQE/A+O4XtwlNS5G+S7ZsgMPCHfNYPv/jEEneu6Vxqpn+zifmCFTkrk2CiAfi/fnStWQ+zmCdyRJPmEvma6YXsXJGJNC0yfvIOEROPTHgId9gK0neC59znOe7Ospl34zt2h17+9HakeaLSC7KykdrUK2ddNjyzrpX4t+YU2cbMY45Z7mdxaLYWUD3coZXiuHpKXrjHTOK/522bdCulg3LJuJ1ROxsfqNfYa1SI7eu+sUsEHN4sxPDfOMzdTx4uvPKSufvO5IsZWn4OvZYlIKEMhuXtgNQpwtdpn7OqcPk9++3bhLrip8RUN9VbLQTV84AM900RODRO5doHG99/E93jQ3NIrCRRSu4/Z4acqAHkXuZXdGSrtuuCZRfxX0hfZisaw1W13vePUho291HbWmsv1/I71YL0NSgjuAqI/w+LPXmPxZ69WhRirM18KCARvnCf42VXwZ/E9PIJnUl9woM8n4dsMkwpvZ8Ct9juxw50GpADe+g28ddu5JczyPigxlnax1jqPPxZFchdJ1K0RWa609X43nnvL8dJqBDGdoHWua8fKStmJRsiQ65+Gqd2WWIDucAZ3zluWL7FygnWS513usDdYZuiL87brIcP7pA+K2h246Cqw3jZHLpii8+4Zy2WqCbLWlpoyNbXWgBoNZfdsK84uquVbWk7WJeUAbihyf7eLmtU0p84+4eNfhZ8rgmwFgZ4Zuh+epHajeVf/MQq6sfrMtY4zaw9aFj6n/qgHBRPzESTCiE01dJ4YZerOAJXyZ69Om5RInrpNy+ElVj6+SN2Kdtpd2P1uXLEI3oZ18hu7bcJyXX0tyyysBitmPVpXrTeqqW5SaRzU8RqMx2y93SQjl4pb7mXeppT8Jjr0kHw8RHaxASnnsWFSsg8tbuBeasK9ZE4CRzMCmbSbTNMyebd52utKWo+tzJ0jZOCHb5diAU4/YujO0a13YMVNtc8nEf/qKuuts4RXG6if7MLbXX277nNvOZYCacRUkFTd+o7vd7wsUaJ3VVvSJ+dLM3nmJkVXvoq1tA6rkjJ68E514Z3RlnSJLjfTMjpgixhrwcjycNAGUjuo5E6CEmrrZKWla9Jzzfh8OUCytSJ/HhAWBFw5z5aighnKvXenwvfVajuVgDqrpZ5lSUDg+M0L5FsXONxUiu6thAtExSEUGbowxuHDS8TuHCG/XmNrB8C90IKvZcngCAmxZ5KZqZKPp9MsalqSmsosnM/7ggoOTvCXHuyOB3J77a3JEDw0T90nL1PMVN7nXIlKxEl4x3oRYiEenP+a8aYZ091hJ5blqVO3dJV0rLRhVyBFoHMWkLZ2kdTvR69eJz1+IuEsi/1PECUXflUCn2rgubccd+cDJPIuhLWdIu07XlbRDzrtz5Px03lniKdJbR8WtfVPudKR/7bSuJVBeFYmDK3jyiVVJX+tsorQhdkAGUt4n5uVmHo1bPSO9yLJgZnm7M3iBq9m3USiKeKx4I5j98uSXAnFiL5ggXTDKt4nJf9QOcmOlR0Yrb5pVfPYiV6wugyj3w8y3DkvncPHmD7xAD68hFPrcbXanbdlifoXbpJdqmPhr14HlRqFlXbnnm/Bc/Ke7u+j9VOcdxXpnN+pDqAsW6+djGYECjXrCKkAItqDrZGVbT8DqZ9HWAkOMyLIcjsVvFm8Deu4AmnCLRLJlz8j8M1ZxIwP9shwVs54KSAQfv91ct2T3Dv2mMXoCoOzPURTYUSdmdfuTm7NfDuBjRrN3yQken0SY5nStbTG6MRoN4HuWVzRTQqx7WsOuxL0PQuGNQrWD988zSlO465CgKwWnntyDCBIAmLe3q3s6FAZfcFueYLVc5uw49sI1qJoK02KldjPhCAH2cJgBj2CbDf7kvwuy5V32wmBXNaN273bV9ROopmDAKUUm+vENdpW2jju98CzrUA1Qbaz02LHzaUcknKQyIx6ArQyIdZPdzJ9eBR/5xzpKW2dYD1Uu425fKVt49XPzmv6fxq9W/md5ltWyMf0LU9nWjMU51rQWhgo25BWsoaiL03iOx/gGesmcOOc6f08rz7sBymxlJIgK8dVI7ers0INLn8Gwb1BsG8CX/MK+USAQiJIOnOY8C++g/hMrq2yY3X1IOQ9eEf7cE8fYvnoY2ZPXiOcDPLW/auG52mRUU3XyKXmreNlEisft3R4jJmTd9m4dYzU00OAQOT0QxIj3RRTAUCgEA+z8tElgj3TtN28sFXuYCEEboVBSicgdK9I8db19vRqVUIqEiO4UYugsiKst87ij0c1TfB25EiUA65+J5EQvDmkbHnR3vLkou7YTgmy5M6RPnOXwDfmA7USB2nwOyjQ8i9XwooVuVoEOegS6MvWcx39dOgHmSgr2/dgIUT0yChLLYv87nIf6wbHV3rS+jaqVGipDxiRZAEB73A/wf4R0lNtlq+zF20qtx6lmHXjCuwO0rQyRkpigcype9ReP0+jTv/zd8yz8c1J3TJk16WhfCPDeQgfH6HODf5bpxHybjxPu/CMdxvWw6rf7EHFQesPyt0+PYIsiiV3nPiGn6aeO0jPYnOSY10khg+Tf2bNzJ+N8iS3xqCi/OeFIAOIGR/+26fxReMka9coCkXbqaXN3C7Uv9fNtjNz8i41Qw/xty+w9uUQob4pQn1TJJ50Eb97FID8epRNhZ74YCHkKMvnXuBbQY7Da9r6f08vfo2Yd3H6nR8gsNvHy0kCCL1JWQyk8dRtbAuBVwAVmWzybvw3d/oYGxFfy+khf41RrhXZKUE2cq9YS7rxhJIMre8mM1rH77fbhRF6/QXuHH3AH6z1ECx6tshxt9J6zPZECNpWYT2FmHJhN728nTIr3c/Mdmv0SLJn6hDCwDDB3imYDGqduoW9bEP5WITsUj2+tkWSozsJqBWXitSVr0lvhJlbDoNG+3CFE4jeHD1LHVuZ8tSQM57K13TXxMm1LuMb7kdMhAl8Yx5QbWXeUbbzb+v4qycX5xRbz0nlfiUIEpFoihpRpK4pyeonF7fI8EGAXrstyz3tsytIb3zEZMMcPcsdZdTOHJ5MgKMfvMGjFz/B17xK03c+Y+WjC4QGJ/A0rFH/6pesfnQFEBhI127d1xvdsV1lpd0ZZud3u2/stQzdt4Ic6+HsT36PTCCBwM6JpxIr9V1SbSnIpPR1/MqFupOoLQ96RF9AoM8jApIu8bWCb/MArQej9lJJy2UlrBL+QJb7uQzrGqTXKGLbqhh+NaA16Pf6iqx2TFP05ElNdTBb3DlEKQdI9XaqnevowciXVK+PVaJvHLS+JUgiNTfP4Xrpc4IrAyQTu13P9mthlVlspObsg5LmrMr1QR0TAtvvv+hPkW1eYv3nr+iWHeicJT3bQi2CpXYzWAghffoSki+DaOCe5wTPs++6VVST7Mjj6stHVwgNTJDJuMmOdZMY7tnlq271Xe+l9ViZEl2+vh0IRRfe0cPcOv6Q2WyRoXQjwWxlOYo8Ho9mBMjUEP7lm6TP3IFDszS8ep31a6d4yiahSHpLL3zYleB77uCWK4Ua3ryH1vZ1yO7sT3vtlvmtJscAvtTeZJuq1javEmarSLPJu9zFQTUsZs8DqkGSKyHztk1sJULBDIn49mBiRnr1iM0t9zJDYmUneRla7VdCQvKnKTQt052MMjEwTCq6wYXRE3S3rgEwO7tTzkhNkMH43ShRTrpwJ+ft5aJSaQUu1yLX55MYXa3HP9bDkeMz3Ly2WwZzP1RRhvKNjGSyXABEf2ZXGl8ZWv1L8uSR8i6knN60JxHoniX6ubGPphoCAoIGMVaOCT1C9ZOrVBJGPvfPg6FE7ueDhRCxgQkAEtfOkF3S3mVWolbSTzCxn+4VTuYM92w7nL/F4pFh3mWY37v2ZsXr1d0aZ3SiRH3FVIDgl5fJtc6TunQdX9sC3ROHyD5s3HWOHkREggpivF+xSt96cqxGtf27rCT/MDrPLFWxmmAopYGMBizl7+UQBBkHfXCsBsyIWDmDpvJ8M9/lXfI3h5fIZN3kdCd96xbioXwjzcUAkLZYc2eQkEifu0Wud3zru0fP/n/lwQXqEzXM2iivEm3aDtRtQc/Ktxf9RD3RaKVot+JDqDcJNUbTlnS39wrLSxGKRRBE7WerRyLEeBgp72K+a4TWycFd57mCKVyuIuJabdl11BoLnG6Rq9uWlbFeCTttUK8dqxOVPE+GkvxmkNjNY2Q10i9XWlZzL2CHJIsZH5Eff59iKEnirQ8YXfbR15ipeJ3U469nvpWnP30d0Z/GpUhotl3v3e4UBw2/VuR4PwIf9Hwg1WRYHjj1vldDbQ2WUa2EFlrXt4NI4fmwOFiBHhGzujBSkl/lQCcTZHUwppbVV/m715snlbQXybtf7hRFf5ps3xjZ3jEQJTyjPbgXmhlYb8ZVKA1H9RY0OJXbecpFH+i3TfXiUC9zph2oy9gP0qBFapVk16qmqRZBdm3UkFP5ZhrtOlQTW6mdfUFEEQppfWus1pgpIJCaaqexaR0md5/T2Rwjv9BMvw/Ubmh6cLJj5JSQabUzLRIrww6BNoOynT8vY3ifTyITTLDuy5CP7w7KN3sPeu+q0tZjrWtYKV/LkKZ5TBEG1msQ0l4evv4ruu5fwVMsj/q1t5d0wtW7eup6FdP+Hbs7t9zLJZeK5wC/FuT4oEQD63U02Baa12vgRgRZC1pEoZznILlzSK5C2X51VsmD0UD8vAQK6u0GKMmtWpdXTZCV/+shnxdJbJb3XnaQcdFfEf1mZbuVkAj2TrJ5/Ame6XbcK/V4RvrwLLXseIdmRK69fWlrQO5ujYOKIIMxYdA7Rut3q/3F6NhKtFUnLhJaWS2NNER1UXAhWkwnrW4v1SLLoq+kyCIIu1MdaOnR73g/RWGrbur6xnvHGBg5tvXZSFHiICkXWGljyj5i1K7Nxlt1WQcdG61z+GbbdyXyMCPG++FfXK5rptF5AgLBj18ideEb3j3+FW8/uOyYIMvEWP23PBaDfsD3UL7RNuvcr8y6vxbkeK+3XM1gJYpe2TnNtDuNBqqK3bskUKhfQ5yzLu1kBWZ1s/K70hK4V7ArBahHkNVWZDVBlo9VQk063J4CuVzlU6xUwro87ErQ73KTeuFLan0ZAh+8iiu2nbbX7jubnW3aQZCtlGHWP0CbANgpz6rV2gnUFmAn2a2sTixqf+58rIHbNXFcoQSFhD2LZyXJsiAU8bUt4O9YwNe6xMaNE0gF69OX3F9bp3sIvjXK6Egrt5Klvie4c3ibV/H6s9TOa49vlZxDKpEgR4ZVa67VHUWr46iWkUOPpO+FxVmv3r7NMIWucUBCDt6sxLOvFnmuFiEfdiUYjNUQfv8N3Jev837/TWqKXgJZP2cmj1TlmqC9CHUyfu0Hfi3IMRw8gmwFVjvgXhBDoeDGU2FiXCmY+X1agVGH1SMWdlxY1FYBJVHQcrMwgtqtIp9zUVOnLTtVSeiRGyPSHB16wObhaQDO/PR3EIsuRrH+joy272C3K4BTK4MTv0w7qEQfrYTlxG6wnjvnJbcexV2zaZscq2GW6VEPjU0xzp6Zopj2EZ7swnNniNpUYEdf2dIdVrmnqZ+5Kx4h+biXV157SGa+iWJugcChOQopP/7r5+hpSex6HtWcNyqRVt6KtdcOnOx2mC0u98Jwoexj3a1xnqx5yPnTFCObBLpm6Xy628/cDEYLmUoQZGXSo71C7qvzCK9/zHzDMp3LlZnTrY5Nsr+x2sBxEPG8ZPS1DDlwwI4lyC60fH2dQu1WofW7VYJsZLXai8FJKyPZfmw/2n3XVoKVjI4xsiSqIb9vLauaU8taU3OMpQXttJ57AbnuWvfga10EIPLDEjEGexOlPIAqt+9k67GMalgiqtFfDsrifGI+suOfHuR2H0oHcQUqF6A5lG+0vAshikUuXHlCaqKDhl+9iW9kAFFDMlNZnnLMURO9Pp9E4nEfS794mXwsRDHrYendl6n/xW9xdLVyGvV6/UFZR70MjlYh349yzjOaB5zieXGhUGJiPkKydo2pM7cAqDl/n91OOMbYS8K6l3OlgEDHgxMgwcBcl6Vz2tuXtv5pQT0GG/VvuQy9sg4KvnXkWB4wqtWh1b6NVq5j1PCtEspKdJxKPhe5nsp/6u+Vx64LuX0jy5WCGUFWbyWaEeRKIp837sp7qUmrJAbpgkB2pQbBxha4VRgNrloKDnah9f7Kbb8HhSDbQUusDn/HQsXLlUmy8p8aklR6XoJHP/MjWF9cyvJexYyPT5/U8unjeooZ39a7roZf4y33MotiypQwg732VQ0iDNrzhPI7J3NItdu9Vp2iCzsXO4JGOvByUe5Yvl/z4cJCI668G5fFzHnlWHnl9i4/KytlWV3AVxOOZqyNjQ1+/OMfMzY2ht/v58qVK7z22msAzMzM8MMf/pC5uTmi0ShvvfUW58+XMgZtbm7yJ3/yJ2xsbPB3/+7fpbu7lGloeXmZn/3sZ4yPj1MsFjl69Cjf//73iUTsPxQt2Ru7KCdi3ch9w2lHkK3HeikX1VAH31SjcZUTPKB3jpWBJtc6jxRM4h3rNTzO6ja2XdJkJ0uPniICbN+reqK0m7luSKqjrjZJfrGLtrxPd+JVum7spRTX4kKUQSns6F2oya8WGVZvzynbvbL9O+0D6vdXqQx7z5M17kQhylxNHMQiFKvv266EJAmkZ5vJb1h7f2r3Ctg5J2w990xoexx1wzv5kluSnja2shwrULs+yd/JdVRDuUNYCVcLOzAKPDQLdrVTfrXbvXoeFhA48d7b3H/rXQKfX3ZU5kFMDuIUaveNQkFkrGmaU9P23U20oNQ7hlJ7386CV0Ap36ZHkPXG6f0gyLZHumKxyJ/+6Z8iiiL/9J/+U/7gD/6Ajz/+mFu3bpHP5/nTP/1TOjo6+Bf/4l/wyiuv8Od//ufMzpYUSz///HMGBgb463/9r/POO+8AkMlk+O///b8TjUb5J//kn/CP//E/3vquWCzavqFyraNOzzW6rtIHzirMZFq0BjH5n1lDKscCUE1oWaLVEPJuRAu+j1bvTU2cyl2paiklGLULPYuZVQLrrVunmPFuRWNbIdV7aUVuC0Cu/6mlY8sdAJV9Qu12UY4luVouFnYtf+pz1gs7v7P6zwqUzyeY9VObCtPds+BoLNODFWuvIBbxNi+TWbTeZuUyjcYR5XgtW5PNUo8b/WblXtQBuGroKRVVc8dNixir24lZG7JSR3VcSLWg7qveVJCum2fJ9o9W9brPJXJuphsquyPU55O2+tL33EHN9NCwf4k97MA2OV5ZWWFmZobf/d3fpampicHBQU6fPs3Dhw95+vQpqVSKH/zgBzQ3N3P16lW6u7u5ffs2ALlcjnA4TE1NDfl8HoDx8XGSyeRWee3t7fytv/W3WFpaYmxszPGNKTuxGWHWcsWopg+XVahdFdS/vZNP7qiffJz8vQw9n1k9iSurZMBqIIG8rWgXWmTZvdyIe6HF9Fyjd6b1LIxIcSWCu8yeqR5BNptw44cmGZ/d6W9shXDb8fssB1LejXu63fQ4M79uLWhZH6wsDp1A7/1VgrQYtdVqjEHqMrXKVm9rNn9zjsyxR+Q6ZoDtBb/6n1VYXfzV1iZLWqkpe1KFyvLld2QnFkWpxWrn+Sv7rJOFr9EzrAZBLrd9WXELrGb8jxbUfTW01kChdoNioHpBy89jMhH/g2OINnaCZmebdo256u+6W+O80R3jje6Y4Xiu95s6cdF+KlvYdqtoaGjg3/ybf4PPt60bKIoi2WyWhYUFGhoa8Hi20y+2tbWxtFSy4ly5coX/+l//K++88w5/+2//bQAEQSCfzxOLxaipKU3yXq+Xf/bP/tnWZ7swUy1w4jZh57pgPJBZzYAmbw+qv1dbOPQsDn3PtpjVW89GgXt2Bi6r20ly1rVb7mlTeSej52JFAs8KnJAnuwkVlM9Sth5rbc8q3VO0tmJBv72cFWqoab3LnZs9jGi4Y2ht46rLqqarxdXOFP5D8/h+9XrFy5bbs1rSTX62WpJv5Q60etvrViQXzWDkgqOGfL11IbfrO7t10CLIWuNdIF5D8LOrpF74EgDPTMeO61YDtXWbnDg9wZOJRmo1fleOP2ZtXcvNAvRdCuy6xqn7rvLaVlO3G5Wv/r2SZMzMyOFETcHsHL052olShhn8iTBN012snbpP8OuLFSnzeYWy/bgXW9j0fUORIqINO6ldlwgl7CQk2m/YJseiKO4gxjMzM9y+fZs/+IM/YH5+Hr9/5wo/EAgwPz8PlIj1v/yX/5JisYjbXbp0X18fzc3N/Kf/9J949dVXGRoaIhQK0dZmXWJEKyLZ6Bg75ZVzjpNECuoBFrYHXj25L7V80bArUcpEQ9cuP00jYlxt6JE/Jez63JYDPQuy1nHy906tkkr3CiMfRivPSIboy+L15dhY37ZyabU5KwS40iS5o3OZ8OAi4fdeZzAT3fGblquJkwQVeu9iNCPQTXlBJEZQb8fD7iQ+TgiFVntQTmZmgbrlXNusHgDuTAOBry4Sv3idpcUIPFMfsQsrbay+Ic7ZC2MMP2pnaqKRIZ3jzGIf9AwNWkTZCFaNAHoopWQvr3w7x2tl6zRyGzHzc9Zz8zN6/moib/T8jebsSsxNzaP9LL/yPoXoBq5YdZR9nhffYxn59lnCKw1Mzdc4yqiphHyumV+51XH+ILhdOA4hv3v3Lv/rf/0v8vk8Fy5c4MSJE1u+xWpI0vbDEkURUdxepbhcLv74j/+YX/ziF7z77rv87Gc/49SpU7YC8qqpYWzWMfWsSKC/Va78Xw09C4gSWp1QORDJA887JHnn69atLUIrg40T67Hy+mawSv6skuRKa0SabQUps42ZwexZKn9XW8G0AvXU33+TTvJG3oXPnyOZcO06Vg2txCNax5hN5Hpwu/PUN24SjwU4cXyWyCcvlSYiG/7fyuevlmzTglabHnYlYCKq6+8mw05wpXy8XE/5msr3p0Slo9jtkKFqbvG6F5sppHz4WpfJzJq7NzmBy1XgxJlJ7t/tZH62fiuwxyhwTIbeIk8rI6Ueys1Upgdl3aq9+C9H8ciKddrOs7FCnEH/nVYikM+bDiBu1FIMJ6pGjp83FGo3kOabgd0ZNcEeSe5ujfP+RLT0XjPb71TrvZVDwvcSgqRkrjaQyWSIxWLMz8/z4x//mLfffptYLMaTJ0/44z/+463jfvnLXzIxMcE/+kf/yLTMbDbL7du3ef/993G5XPyLf/EvdrhoKLG2tsZ7771H9hsP0qbIegFqdQwZ68+yn+r97vT49UJpa1PpT6tFLKz428rnLYopQ3JSK+18HsqtVflc9TFd0SwAsYQXgGho52ctyM+gHCjr1n3Kz8Td3Vqpes+muRgwfRYy1Pe79b3B+5OfgVUon5WV5ydD6zkq66X+Xf0+lVC2EYD2aB6hZoPZqQZNBU+nJFf5ruz4iofDaRqbS4S0kAwQeLb1rn4PRn1V672EQtt1SCQChEIpEonSvWm9A2X/tfqerbxLuX5G1ywHyncP289e6z3+zf/rFF/9aoSJu2nd9l9JKOvmronjCiVtk2OrbamufhOPN8/ifC3A1phmtd9YuZ7dvqE1Flkdn2B3n3LaN5XXht1jQrnlVgJ262K1/da6do4dyjZgZW6PNS/gzvjIrtYaHtd82sPiHWPpQCNotcWDhlrJQ+7wOO7ZNuryPs1jrIyd8tg8t1ijOwbanYetjMVCuIj3fI633nqLuro60+OdwLHl2Ofz0dTURFNTEysrK3z55ZecPXuWVGrngJRKpXa5WiixuLiI1+ultrYWr9fLxYsXOXr0KP/u3/07RkdHOXr0qGE9Fn6U5MGwcTYep9szZuua0Yygsd20syVYX2Gnn62iXYxj1Ch3/7bTL3l6y9IiI76VNWgFgM1n30+ZWM0qZY0fdiV4+w/rePe/rekeow4WA5lImycfMLJw6L1zu6tWEa2tHmvES/0crbj9aPmay5AtwINHS8FRw4+MSYddK5X6XVmx9AtikUtXR6irL9V76Zcv0rexfQ/qezZ7+sr3s8m2+sTmbNOz9lsKrtFrw8o+f/XcmKmLhbyXZS3bnvZ7L6e/6L3vnX1hG//P//cgf/b/urvjPVXLYrxrDBMLNL31JbG7R8nMNdsuz6g9NTZtcPrcOJ9/fIx0qvSmrViOzWI8jGClf1h1N9LdkfkjP7f+W3pXWU4tyGpLuBN3KKNzy62X2XWV1nO9dmtnV9DK3L7ets7s0Yd4f/EmgoG+79Afhbj1X5zvGjwPrhWCO0/L73xK5H//HgL5re/Vz9Fsnow+G5fF2Sbi8xHdIH89aGfrNJ9Xfd3Qeb66RgHb5Hh0dJS/+qu/4p//83+OIDwTaBcE3G43LS0trKyskE6ntwjx3Nwcvb36mrQffPAB+Xyev/N3/s7Wd+FwGI/HgxWj9lSuOsTYCvp8En0Ed3w3Wtj+TemTaKUs3M5S2GoFpmgFoMgahFafhd5xdknAYCFEreQx9HkrZ5vRaBA12pLT8y/Wg91teBlq9wp1nbSep5brjHoySyR8NLdsmF6/3G1c5bm67kDnnm4R441bR+nb2Ema7G6NarlYaEGPwCozh3Gj11YgiNq/vFJuNFowWgTJ3+2l9J4V3BLXaLh7iDNn75Fbf8G2koSea1X34UX6j8xx65vDpFPerWPLJf1mihF65NAO4TQLsB7ikGF5dt+zk7gCrWtqnV/uWKGOldG7rlKTWobapcWqH78yoFVvnKmZa2eh7wk1A0+JDfc5vEN9FH1pcj0T1NSsIfqyxIUstYEiudUa4g/7kLIepCokQ3ICT90G+XhoV2IU9fPTMhYooTUuq9+T2Rx8UN0rbL+ptrY2YrEYP/nJT3jhhRfY2Njgiy++4IUXXqC3t5dgMMhf/uVf8tprrzE2Nsbk5CS///u/r1veCy+8wH/5L/+FX/3qV5w6dQoo6SF7vV56enos16vaDv1KqJNswE4/RKM6VQvqQUXLT1lZp3L8uNRi63J5Tuorw0iJQisYpNzVuZlsmxYhkqVlyiXIw67EjkWVGalSTzLy52TCTzC0aLsuliEWAGFH4gft5AYNtLTdAGD9+knSU+bSbVZgxzdNiyQrCfLoRJQ+n2S5LK1375Qg6/lwmhFjO6imn7FWP1xZirK8FCHWOcr4WIttQrWTzEn0DczT0bXCl58cIZHYSba1JluzOirHCK366xE3uyTTCaG1aql1EpxspgZkdnwlFmJaajhW6gLmvtJG/tBq5RX1/CYg0Djew0bbHJRJjovBBNmeSYSiiOQqUGhcplC3jnu2jYH1ZlYSPpJiho2MB3/7Ek2/9QlIApnFBrILjSTHOqEKGfusInLqMYknXdRr/KbHDeyMx+oFj10j1UGAbXIcDAb5B//gH/DTn/6U//Af/gPBYJALFy7w0ksvIYoif+/v/T1+9KMf8e///b+npqaGv/E3/gbNzfpbb52dnfzhH/4hv/zlL/noo49wu910dXXxD//hPyQQsO4/ZcUaZwdmDcCKTp/WBKv1vRPoWciVg4Y6UtuskZZz/XJhNLlrWQ6sCvZXAsr35nSVq6X1aecZak1io/kkPr+5f5vTCa/+pW9AKLL60RXD8ryNqxTzLpZ/8RLFrG/Xuyy3rdh57moSWwlVDLtQLxitRP8fNOuwFnb0ueg09Y1xNuOlMdqpxfGsUIN07iaNkSxrn1wmkd0d0KzcAbPTlrSCJM0CptXQWpgaQU8tpuR/m7ak1lOO3KUaB6ld6QUaq4+xAqeSdtlgimTGgxPPbAmJQssi2e5J8u1zeCY7oSCCJNI13kf4m0bcudKORxPgkY0hK/XEH/QjenL4mleoOX8fVzBF/N6RHeWLgTTN3/2Yhb98oyoW5lvuZVyuAq9eGUcMJ0hNbhsxKmXt7fNJkNE2XlWKe0DJY6CzIiXpw9EbOHToEH/0R3+k+9s/+2f/zFZ53d3dlgL2zGAU7aqEHR8Yq9A6by+3C9QLA1m1wqyRavv8lHd95XWsHGN1UaM3EFZLrUJNtJRpiZ0kBlGTJeW9H/YXGBWyiCn9YVs9cba1r7G2GrZVD+uQ8DasIxWNF5miL0Pk1GM2H/TTn9q2Q1gdBK0+S7tWZLsuM+Uc6wR2Fnd67VvtqqQFK31O+b2lMjIh3EIUvz9P/VwX0XxU8zxDCEX8HQv4Tz1ieTHCyodXkPJuhtgm20riZFV6zex5GPm3KqFn/dQrT+3Wpge19djI7UAPemS90lJilVTtkOum93ysPAetc+2Q5Kw/Sc6fQsg681XNHhkm2zuOZ6KT8M9+CzHrM1V+AkpkkQTFgovUZAeiP0PkxBNy61HS09uStTVDDwBwhVLkY5Wzpiqf4/FTUwiCxOcfH2Og6NpllbdDkMF4nFTnZSiXGCv7/l7Ymg+GA8wBh1JOqlraqZWEnkSY8nc9VIosq69T69IOwlLXxambhhHKWbGa7RDYeT5aW+5y3dY6pon40yTuH9E5exsySY7WJllftZahUIZVy4wYKAUPrXxwRf8YX4aG178kPd1Kx/Axx7J6dhYbTiy/e7FILSd5wV7ro5abHKnPJ8FqG6nxLhpfvk7kJ7/NiLAzaNAVTOJtXqWYdeMKZCgkAwhiAW/LCv7WJRAlCokA8W9O4V5s3KW2orQyapHkSqPccp1IWoKxpKcdyUur3+vVQ4bWc7C6oJCP1dNMtvKMjFxJjMiyFQ3rxAvXKDSsEXz/Vc16CO4cwd5pBpp7WHzxFoJYpJhzE836kTx5si0LRD58xbYUnBybJAfwJ4YP0xyrp3DuJrnVWgRXnujQQ7yN6wAUbPrwm0F+Tp6GNRo6V3n/F6c4ntTOI2GHIMNOkqw3jit3rcEZSbaTJKlS+A05VsDKJKrWXbVDlstJIKGEmkCapivOOE8OUEmybAdmwYB2SbRVglztbXeteo9mBPqmu5CQsJIwfSjfiOjLUF+/yc1r+sGuWrC6/S3lSkND45tfkJpsIz3TSmahAQQJiiJiIEPtxTtk5ppou31ux0RjZfGlDHpTpglVty+7A6qVIDot7WQr/dioTHVGRDvQS7BQCVRyK1OJPp/EdNHNkidP/A/+ksOfXqUY2STfOk/KlcNds0l2qR7BVaCY8eJvX0D0Z8iu1BH65hyu9RqEZJAGHb9LpZVRz4q815OlFdh19zLqi3b8h52qVugdb6UNavl1Wz1XDSsWdTX0iLRefxJjUQrROLPhNdIbCvkyoUiwb5JA1yz+VBB/3kPdZDcURQo1MYScG9wFwiP9u4ixkTqDVt+Tn5lnpgPX1a+pvXwL0ZclOdqFlHfja12m9sId1r44b3r/dlFz7h659YguMdaDlTHYjBfoZagE6PUVefzKh7SMDFI316F7/b3u678hxyYwS0Ig/15OSkW70LI+ggWLUMaZ754SZsSx2uRZy9JshyCblWcXlVKwAHZFDisnH3WEt691mfX1EAWVdKAZrFqOpbyb1EQ7ge5ZfK3LeBrWqfHmED15CikfgidHeqqd2N0jxDSIcTk++1rPUy+rnt45alF79fWaO0r9dnGm1G/N+rEVOI15sEom1JP+upAz3VYup32bvcOulR42E82MNU/z9KUvaIjXcHjxEO6Cm8hsiFBm20VIs4/4ALSfmVkCCqUl6qBCeQ+V0KI2IsCVymyppSAhw45V2Qh6wZLy33oWYjtWck2r8p1+vHP11J6/R7Bvknw8hJRz421eQcq5SYz0sDLTSvxkBM9UiTzLadLtQDlW6bXRYVeCzqfdcHiC3EaYYsaLr7V0H77WFVyhJIVEgEoF7YmBFO5wioWfvgYYW+j1xhSrhgrZ79hqmxzLiKRqNhi/cI2p+Qn6Ro4RWq/b9/79G3JsAuVkqZxA95IU6wXfqWGVJMsR/NWwKOlNqOGQtSQLchl23RXUcNqxnFiNlfW181zV1sY+n7SLNGttuQueHMGTj7h9s8dWPe0F5whs3DjJ5kgPja9/QezaKToTtYiJEMVoHCERpDbro1V1Vvch46x0VqB8nlYWP3ptRa8dGRFgs35dTVk3sKZgoTeh6UXwVyPGAkqLuSP1SY7k62Gm5G8+u6pcbGxuHav3XPWCJ7WIspF/5PMAp4sgNaxYlK24JejBzCJbieeul1HSqGw7yhearmRFF9mFJpZ+8TLelhVcgTSiN8vmo95nCW2225zaTcOu5rJZ7MywK8HUrSP0jndBzk3h5F0kCfLxEJ5ogqa3PyUx2kn8zjFL1zRD9Mwj3HMt9KdqGXElt753oohidY5T+8MbIfTe6yTe+oBC6yLDrYuEf/pdREdhk5XDb8ixAmZb6sqB3WiCrQQxNlK9MLNQWSHJMkHuLrum1mH0bLXSV5YDp+SkEm4V5S48tOqu9EFcPTyMd9PP8qJ13zenUevFpB/BJSGIEmIsioCAa213RqJqyxbqaW8awcx67BROdwvUsEuCKgmnz8LqTprR78qxU89tS2uMU7ssVJKsKaFn9avkItwIdnx8wXo7sUMujcqs1nOXYcUH3ylR3sKiCwg9+we4V3aVK0OpuSzXzwxmbWWrjJUQ+YYV/O2lOnqiCdKzTfjblwj1TbH5YAApXx5Nc4WSeBvWCLzzWwgIuhZ6NazI5snQul87Cjau9Vq8w31kB0cByAw8Qch48T82j8GpFp57cjwppqizucIoNwrTDE4nTyfycEbbN0Yk+Y3uGBPzka186N9zb+vu7ocot5Vrqt/NQdBH1KuDURtTW0a1oBdUKQhFurqXGXlkXUu4HJIlFdw8uNvJ8as3GXsvQCEe1pRqM3t/dv18rTwjKzCyHhsROSsWZCivDVqReHNSBlQmM6QZ0XUCM7cVPb1qJWQ1nmrBjp97NQKI9WBFG94qyhkTrGYkLeeZ2LGyl2Ml1zpvSKxsQJwMeUxXGz/cKw0c/fANJsnRn4oynvAiIbH04qcED0+RGDm8oxyzxZN6p2W2YRpxI8BiMccg2ju4aiKr/GxlUaCe7/p80o5MiHpBmkr475zeIse5wScAuFbr8CzZz8JZCTz35LhaqARJtgOnOq4yzEiynr/mG90x+uYjvJMvbbUoSbIVlBOgaBd2CYkV63Gl3UvMJks7cnC75d8kXC9+hdtdYH6u1nKdys16tbEepJh1c7cY5zjhHQOdWXtxSrDKDcxTQk+n2owgw25Lp1HZ1YLe1mfJj3VnqtVKuFA4fWeyH7cM2Z+70mPCXgXi2VEgMfq9WnXdL7cSs0x1sHcJsPSszHaTj9i5nhGstpNdx8WjHFEdk84GWPPksQq9YNBobRL/ej05xW9O9KXNXEvUc6naQq11DfV8Gfnz3yX+f/x46/f1o49oekaOJVeeJ+5NpLyH81S/7T/35LirGNCUCDNDtdUL7FqPK7Xtq0fO9AYt+bp//Ozv0YzAO9OuLdJjtU5WiEalUK6Chl25GidQWgn0YJcgI0g0Nce49mU/dgM17BDkRTG1NaD5A1levjRG7PYxjid2RjkPFkLg1rcal9MetJJ5VMJXfmI+wtVzJU2Q2dmmihBkub5y+co6a8EqWTKb1NeFnQlgtK7nxKJvB2pCLEMmxlrYa8NDuSh3bOjzSdS69s4to9qwQsqd3JeV56xVrplCiF3VDzlhi9F1tFDpOeTw5GHyl76kkPKRmWmhmC0FCcrzl4REvmMWIevFvbTd356ElwkfnsYdToJYoLFplc1bx3eVb6aUokVq7RBkdWCe1hykjLUpQeDudDv5tnn8d07C2TskL13DtVpP9thDWnw5Vj68BL8hx9WFHYIM1R/MqxXEt0s2TMeKLJ8npzaWibKMwUJIV41AnmStEIlKw6rElhJGBNluRjb5HK1rq6Eu24rlUb6HwXyE5dUo/YRZsVQ7+7jlXmaIQ4TCaRqbN+juWSI91bZDrB62JwonfcJq+zBSrpBh1n+Vz1su74sbvVw9N7bLdaIcNwuzOith1CaNYLbA0evb5Y5bSgKsJL1WibGRn7HTsdXqM7RrIVOOCXth/bR6DSs+nnbK0AvyrpZVvlLP0rIv7zNYIctmcOpj7ATK+SC0Xk//9cvMHn1A4sQTxESQxGaA9Ewr0kw3yRe+pNC6CIA430Toy8tMtU/SOPQA31Qnrrl2hIKLqQcDW0lFlMlqjBYVyn6jHnfsEuS/meneslbfci+bPs/GRITlhSLe0T7cM+3MHR7F0zRP7e3TpC9+Q896855kAfm1Jsdgf6KVoTWYaxGfg+AXqzegWNkCUhLlHf9PRPds62yv4JQgQ/X1oOVJK5iIkA4lGZrr2frNThS6emKof/VLECC3WoPozXE5FKerx8flF4ZZXoqQuXOCzLz2ZGL0/p1qCath1of0dkT02nd3a5z3J6J0z+4mcFbg1IpsBjNSp/y+koF6ZgsDNQFu7ljaIr+LM026BNkqyuk3RkF7ShJglLK40kGP1XKzMVMssjsW65Hh59GSbeTKZld32uh8o2tbgZW2rtZ9By/hz18mWbNOJpggF0gxd/Qp8Uu3cS010jjay3LPU4qtS8R/76+oBYIfvox7ebtd21PCL0Hdb5wQZCUGC9sWZL1zJUrJsBY6pvDfOwGAmA6QGO7B176I//I3uOdaEPLlyyJawa89OVbCCVHWSmqg/P4gBY8pYWQhsDoQKy3L6i0YZWcaLIQYinm5VUFC7cQaph4cqykHpbd4kv9X/261nbjnW/B1j5N80rP1nZ0EAOpBzltfkl7LzLaQj4V5MhPm1MkG3n/3FCCURR7sJsixq32shFFAKqo+2eeT+M/PXIcqtRtk552WQ0Dsvo9y7s8K8TUiyEoibRV6wcdWdhGUUJJkvWx7ys/K4COt9O52667+LhzyVnQuMCO1VgPj9ooMK59luUYjs7gb5TWVsBPQaDXo0Cr02oYRQd65YHWBFIVkFGm4mcebEG9apOlpH50PTpMJbjJx9gZcP4sYi9DesEFgc2dadyvyckqYZWm0O3/KFmQ9ZIMJJs59g2u5Affs9k5lsH+C0MA4wmaI/sfHCfgkFiuk/2yE35BjHVhdmet1cr3tc7Pz9hJGrhdWgi6U0CJoygkp6DrEn/mmdxxrB+rtTvXzM1L0UJexX3qp5U6Oklggc+o+6YfatgCrQSjbBFmikPCTHD/EZ2NynQLk8yKyT7OWpVLpXqMHu1Zio8WEDKdbvvLErLTKlHzs4T8/W9x9b75XY0Iyx25Lj/49GCUEsAKjgDyj8cmpT7hMbLXcKtSylsqy5N/02oCW9d3IIm9maLBCBK2mYwZrBFlrvDHbWbTymxaMXMaswKlBohzibGTNNXpORprlRp8rQZZlaAW6apVnBLtuebBbelL+rIyRgJK+eDq6wczJe9TOt+NNBfElwwx+9goAG53zPLr8Jc1P+ul4eFKz/lat7OqFpVE2QjtZFeU+pqyTNxmi55sLzA8Ms/m9dwl+9BKuzQi+5hV8i82cun1h69hOT/V3rZ97ctzpkcj4rHXkcoiQk20sLQJ3EEixFtQdx4rWpBb0iG9zMbDDkmwVWu/MTqBbOe3CjmtFJd0ItOoB0Ny+TCLjJTXeabtcgLr6TaK1CepGj5a+EIu4QmlSU22A/ruu9FZ+NWDVD07tg/zHz36fmIf3J6IwUbK2yPJ0WqTPKBufXK7W93r11oLR89aS0tOCUdu1sgiwoz4hE2SjJB9ynaz4eMvHmsFoHLAzfhm1ca2xX7lDqBxv1GSnEjsT+zVvOJWpU0t6GcHMiGQVVowj5cjuVYMY652nrv8XN3Yu3GdnmzgmBqh7eoyRUzcJbEZpetqLL1UK0vMmS66Qi4dH6Xh4UvPZmC0ctGA2BzjhV6MZgWzvGOlztzn81WWKYoHBT17h/oWvybcuUkzG8dfGGPz0nK1yK4HnnhzLsGJVMktJanaMEk6i5g8qMVZjKwDMYBvKrt9ereRxlCHKaoBMJZM8WEG5Uf5W66tsM7GmBdyLTSAWSl8Uty28MrTcW2RcfnG4dNrgPLm1GlzBNKmUh/uuNcjpZzBUL2r0gjL3Akb9XI8Mqa2Aei4Q3ewMvJOtN2ZWK6h8Ehs1tPqOngKCVv3stFczdwqzwEX1IkLp36p8nmqLc6UWmDLUC3w78l5aE/1WWRlFIO+Eauta8VlJok/Yq/ouOCXIeufZWcDZJZV258ZKB7xbIcp2sBfEWF2G1gJcHfTevdzOzYsPSUdjrHRN4N+MkKxbK52QF2mY7LLs6qUeV50aEa0QZKX1uN/jIn3mLgBPL38FwOTZmwB4n3aTPn+DwJeX8SX3XrbwW0OOwf62q9ZkamZtUCscyNf9dYCdDlOOq4LTc+WJV0sCzCrUA7sWObJCNGRyJcNKAKcZJLFItu8prQOlbba1r848S3u6DfVCRbktllmsJzV+iNx6FHdNnMf5FOurIZQEW5YyUgq4a2E/lVuc9HMtgixDeS9KwmZ38aKGlTo6aevq8aaS78KIGMu/afkRa5FYdV9UP3vZimxFJUTvGmpo7YDB7nHdSqCe2mBiZlxR/y5rxwMU4j7GVcQZ7L07q8cakU0rCz1lGUqYkeRy50E7JNmOeoyybLBnNTXblamWUUaL4MsWZNgmyIeWW0klfLQ/Ok6ibpX5lAfXUiPpoTsUfTnNsuXyK+W2poYZQZb7lejLkLp4D/dsG/47J5k7PErk+JOt49KXrtN16xz1sZ1zUHdrnPk9kKv4VpFjsL7KdZpdSG+bX3ntasFKw62WZI6ev5bWc9Ta9q11bf+tFfhYCakiGVYJsp44eblaujIx1rOY2YHyXtbaZgGQigKCKJHfCNsqq5DyU3vpDgCL81HWr/VbPldLxs/qBGUF1d5VMfIjVU/KZvfjJDjQCay0wXJdJ9TQ8jNWw4gka8HMH9quRrqT+A2rAUdG6YONshH2EWS0oH1d2O3HKhPnwWdBymYk1S6JVu56OB13lNdXotrznNk9O2nXRpZ3vfnBiruS2bOt5BgJO8l4e/sSp6YGeOfsJ3iiCa48OcPyUhQJiVz/GGvAxK3zluI5lFDPyU5dLPUglxc59ZiUWKDh+jlGyOCaaSFydJShiSNc9rqZnWtkeq5W8921NiaBmorVSQvPPTlubUxCplC1VZAW9FZGdtQu7MLOlpYWyq2PHom1E9Wr1D6sdP2sQN0m5MlRniArGZgnBzUCkAlpBhGCdcuDJBQp+DMAFJJ+3OEUxYy+K4QaQ/lGpPz2pBIKZzSPkaFWG1FDb9B3ujVutP2rpVqgNYCDseuPkuRoLWjLJeiVGG/s9INoKMum6jvZolSO249VlQor0HqvWu1dXWetIEC9oD0z9QJ1GwF944hRNi+9c4afJTEb1EhMoDdX7GivmRCoFArUO092F9eVdlGw0zcqpcphdWGgfGZGZNQq4ddzV7IKrb5XDklW11tZ/8PPys14SguvfM0aye98CEDnzbOmZZtZzZ0QZLN5dMS3QevvvA/AyscXWCWD6E9Te/kW8Yf99CQ7WABc7AzG3Wu31OeeHMPOxiIhkQ7HmVkPIezj7ZXr9K/WoTTrrGbXqxRpruaio5pQ3qdeR3casKm0pCknNSXBVL5D9SLKzP1CQmKhd3u7ye3LEbtzBMmi3qOsTOGpLZWdHO/g+mitxjE7P1vxMTYjYXYmAz1fOyuTmhZJNpLw2kJm5/sut5/ZQaUkoQBT31019ILtKkmMZVglakpyo5SIU0rCGRFkNdTBR0YqOzK02otRTIVWu7LihqE+djQjlLTjFTtOMuT6V8I3V4ukWXFdKcf6LKNS5EbZTpyQTitEy4pKiRaM+l651mPlOKgcF/8fC4P8/1qG+dHFX+04vmG6a+t4M6UXI35QqTFPQmKqa4yWZ7uXiSdd5Fbq6OlYIX3pGzwTnVwcOw6t6iX/TkzMR+juSBoeUwl8K8ixEutts4xfuAZA9H//vu5xld4qqBTUDdHqFr+eG4RW41Z2CHX5VlxEKk2QD6KKhx3XCj2tVy13DaPr6A28kquAJ+sln/BRTPvwNsRIT7daqpsMVySBuybO/I/f5Ja4ZlhPM1QzTbgRQZZ/0/OXg91bgXoSg0pdW/l45flOYDWo16q7hBWLq9N3oddm1SRZTYSbO5Yc6Rebwew+lPWx6odsNK5ovSNle7GKclRctMZRI71uI/cLuzs26meoJptm5zuB2fZ+OYF06vtXf1bfi9m1qrGTaTVWxQxKtxkZvyvmeRBaZT7mI7xaT+vokR3nVGJ+tcKZ9JJ7ZPvHyAzdoRZITbax8c1JRF+W2kt3SHcsyAciGOgX7zVPeO7J8fxycMfn2vk2aqc7QICiyblGL7uaWZT0YMfvyQxW5HSM3BvMfIArNXjsteKBGbHXsh5b3dJUko3RiWjFlE8K7jzzviTuUAZCJXeI2su3WP34sqXyAXwty0g5NxRdIG5/r6VhLCuL6JF2O9v2TiZbM4KsrJMRSVZbkWUoXWnUJFkNJzsJRjsQex35rodyLcRq8lxp30qrMMtYqGxLZn1f752pFz12lY30oN5BshIYpjcWafVJO2TM6Hyr71SPkJq5OijdBdT926zedschO245WjCbC7RcO+wsYO2mqZfrI4wf5gSH+U77ErOJJqiQ1V7LjdLOWFn0pdn8nXcopL2Ev7qAa6GZ+ULJD6n2yi289RsEPruCKxZByPgYzQu7kjap77sEl6P7sYPnnhyrfY4FSeTwzYsAjFo4X+9lm0XrK1HJiVUJOxNpuUFk5VzbKcwCG6woASjdTtTl2RkQ1NZEo3dnRA5l0iBvj1rJwqSsu9bgmPOnCETjpXCeIiBCbrXW/KaEIqH+CQLdM7gjSTZuHjetixU4sVY6SQpi5f3pbd3uaL8Z5/3QDhHSOsZKUI8MIx9TrUVGKJTSLFuP+KqtxUrya9UabJYyWi/Bh7Lu5fpDq+tptADTWzwZwSyuwug9643DVvxYzQLDqrFYskLGnL4vq+dpWXGduOLYqZPTRZyVeqnLdkqSrSpxKKFWtJCvb+biYrZTLENL4lUPTxtnaHjtawCiH76Ca7N0zUF8FL0ZNus3CH78Iu7F5q1zZB/8coLYK4XnnhzLMJMmMfKtceJiYeV4PSWHamRlO8hyct2t8R3pU2G335TeefKxajixwCnLUW6762UAUk50djpqc8cSv/tsEtdKHKFsg290x3QDOORz3Rkfm03Pjnlm9U1NtpvUQqLm/D1cgTTxe4Pk42EKicDWr8r73IsMgU59A8F+MJAVlwsZVvu93UxQMvTIklZ7Un92QkrMVCbM0j1rfVaTUTMSbfSute5Jz/9ZD9Vw61BDy9XM6Bij7ypVDyOU6+pUTVcpK9eQ28p+udjtxXUrae3Wg5HrlbIcq2Or1o6xFdezIsUtYhz+6XcRU9tzj4RE5tQ9XEuNuBa1763Sxj4n+NaQY9BZ1Vl0QNeTJjOyHtsl1XYCNbTqaDRg73dDkmGVQFo5Tm1FMOrI5cg7GaWW1eukaouFFulQ+mcqj71qUCf1QFgUijx87SOKnvzWdwt/9TpSzjgYz1O/gbd5heV3X0bKl7q5ViS+XpIVo/ZUjvWvHMuN1QWKVfUCsL6V7hRW+qVdy6r6dysuErBNOqtNLGUY3YcVqTgZ9e2ldrs627jrmGrcixYhKLhypCNxgut1mn6RZu1SbRxQwol1G3Y+XyMfca3vnWAvFiVg30Je7m6E8rp25pi9gpn/tB7UZNyIJMuwMm9aIsie3Nafqctf457uwDPZiVAUSZ+9Ta5tnsD1c7v6klOXxmrgW0WOtWDkm2j2gq2kS9SCnnamFU1gIxjV9yCQ5Uo0Yj25Mz0YPRMty5wsA2ZFB9uso2oRZBnKCd4o8ElZlhoT8xEevvr+DmJczLlMiTFAPh5CyruJnHrM7akwiYQPcm5deTa1lNEtldUc9m9y0PJLNCPZRoO+lfdvR6LQKvSen9XJTt2OguEkNRaJsfp8M/eIapEgq0ReDSUxliHXsYjE19EFTm827DqmUhbBoqvAcvc4XRu1IG23HTt9QrkbpLdtbtWFQm7/Vq395RBbp+/MDqy4ETwP2CsLvJ1n5dTybEcLWQ0x5yP8znfIN6ySOXO39G/oLkjgnmkn8s7bjBRzDFosbz/wrSfHMrRIstYkaZTcwiqUlsi9CuZTo5qay1rYT98gLejVR02Q7VqPzQiyDK2JyM4kI7fTQ/dOsdEyz0rdCsX6dVY+uGLpfCnnYeWDK0ROPebk0AR+X46Vd17TPNZse9iOO4ka8nPQk46SYUbA9UiOlYlCz4psdSCupOSaEfZiYpVh5mJRDvT6hN61ZMswaJNg+Ri934pI5ISCaQB2OfBk/XTfPrfjOy03GD1/60IoqHuc3bbiRKqvEtBa/FdyEWW2g6LXz+32m2qQ8HLrYPf8vVhMWFlYGpFaMRHG5ckj+UoazOE//2sIAghFlyV+pcyouR/84rknx02Na3ikjONIeLtuFlaxX6RYjXKIsdUguUo1XKcWnnIUPZQEGfQD85wQZDWsTiTK5+BLhFnqnABvyXrc9PZnLP70VYpZn2k5Us5D7MZJwm99hMdbgGLJYVkrw6MVTU+zezQiW8rBXNlXjXSNteqjPr6cbHZmQVR2YKUPVDsxRyXLLJf8qLd0lddSkmE1jEiw3m9uRF7e6AAgZrF+TjV4zZQKZOi9a+UWdyWC4OwscJxYj812wOz6i1uF1UQae7mg1IOTOqjfv5N7OigEGfR5lLheQ/inv4WYerY4lLQ5lTwGq40WWgS5vX2JXKOP9d9kyLOGchuKniXJSfCOVVQzEKrSpNjKb/sBp1Y9tbyT/C5kmS8rBFkuC6ozYI9mBNLdU1vEWIanYYPMXLPOWdsQfRnCx0YJRjLE7/cD+pHGaoKoJxcF2r6OWnA6UZoNxkqNT6P2aJXwVKuvlIO92Mq2Aick2WgsNiLGdlAJElYN/1InyglmiLuybLpytGV391077WQv/IWrkURGjUoEHxq5YxUJl1V+ufWotlSmXdgJjFbzKAEBIRXUPNaKkUYv3qepyeoy2Dm+NeQY7GkEyrBiRZahfpnlkOVqEeODEpgH9nxV9ysJiJXgLPXxymesZem0GqUvD4JaAvXy84i3zKO0EQe+usD8XL1pPcVAmoZXvyIz38Tiz1+hmPJb9qPVSkusdx96mdUqMRFb0f02amPVjECvxlb48wg1edaSsRq6+ACwRoz1rMars42m55vJuFn9zQr28n3+f1sfASBKcDhVw9VYG9FCKXV8tVxjZOjFUZQDKzEYVmE219tNjjIxH6HTQR0qLW9ZTY1kO+XZLcuKq5rRPGQlCRnA0nId5pE35eFbRY5lVMKKrIQRWXZCkKtBjA8SKYb9DeIy81GS62Yk66f0GdfKoqYmyDLsBl0ZtdM+n0R6pYlsy+rWd9nuSQanOnXb3W3vIo1NMQaDLnKrNcRu6Wsba6FU/92BTWA8UFuZ3OwM3np9Tm/w1FI2USYNqSRJrnabrgQJsZMO2uh8J1C+C602E+1eIjaxXb5esJ1cd5kY6xFnqBwhqLZ7DNh/JwIgAUUBRoMbtE72cSaSNzvNFiql+OAUlVxc27mP/czMaoWnmL0Xo9/LIct6Rhswd1WrRFDdb6Tcqgh1ZLAMpbXO7rarkduFFZJslRRbidysVsOp1MRvNdpa+R6srhorUTcj1Qrle9JKNQzsSiyhdreQr1MuutaaeMLjrc/ipvGW3ytv3CcQKMnorH11esdvWgocVgLvKjlpWh2oK+UDXI3Jz26ASDUDqIxI414QY6XFeOJZ4hvlu5Pfd3PH0hbJHf/imG3yY0SMtVAJH2ItOO0LwXCSDM7eyd+glkw0xtOsxHGfyAtv3NU9Vu85WdWm1oPdemu1Sy1JPivXs7PwNkO1XA/sWnqdqE3ola0myDK/sXINs4yLZhkF9XTln2QlkATDdNBm2G+C/K0lx6AfPVyJDqJOCqC0Itu1DCuzvKm/3wvstfC6siNrWXHtytLpJVOw44+lfnfqID3YGWSpZU1W17USz1Qs7EyTmesfw3/7lN7BBAI5piYamXjaRP9aa9nXrwT2UqKp3GdeyQXaXgTMKIlGJVxZ9CQGzSZ9mRiblatMjGOE9vYlzS19rXs0CvA0QzUj4ZUEbwPn7g/HCUAswJsO/LXV7i5Ko4Qdoq/nvmFm0VfC7gJHhlEgYbkqD1rvXtakthMsbAdOxwSjuBY9gqw8Vo/ollNPrf5WcOeI/85Pcc+0EfzCmrqSFpTZ8uRr7eVO9LeaHIOxgLYdUmiWUlTvOyWspmisNvT8M/cLZtswRitIs85tJBsGu7eB1KRXnUHPCIOFUMXlwQKxGvwbUdI12wEI2cERxJkGioqsQwC+5hUARh63cTzRZrsOY01TPG2e5mrkKjcHb7BUs8ahlRZ+J91clgVAjyTu55amElrvzIq/sxnU6hx25M3soBzZQD1fYTX0+pLSYixj628DzfJyFyGVak9Wd7isohqJMqwGMarJ561ru12qtBQS9O7RrA3J9dKrnxWJPqsweq5OM88pz1WjGmOTFSk6uwk+1N9pEWE7ZNxOoiX5eDWepkU8T7vxjvVsfWcl46QWZIIsl7GXBPlbT47NYGX71crL3G//mOcRyk5rZnW3usWilnxRX0cPav9xLTcZLS1kLd9kdblasD5AuEgHkju+yZx6QPMpmP/xm1DctiwLz5KFDB6dgW+MybH6WcYufMNo21Tp72CCpZo1AKYbFsjONuKTXLvKsIqDRozLXcAYpWiVoZ6o9pIYy9/rla88p5ztXS1irISZTumwK8HwM9dZuZ8YySUqUYn2Uw1irPy/UsFyMrE0IsnqHYQvbvRqHme0ha7EfqilqO/PLqF24jdtd3fHSSyNngunWRl65xiVVc5OlZNdFy0IBTeBb3ZqgjtxkZDnZNkdcMd40pE0L6BMPPfkeGm5DmYLZWsDGmWGUa96DiIRtpLZplorrmpuH2v5ycows4AbyfXA7uekF2CpdKfQSxZiJyui8npK6B3rvXaa+hdv7Pq+/uXrbD7oJx8P4WtfINgzA8ChrlVWxlfJrexUtTDa2fDnStHvYmGb6ITSAa48PsPKMwmp/VZcKKfvVYMQg7merd4xlfAxXlpoYHNW/xpGFkyrrg1a5ynPtbrrs8sXfz5CHwqJJ/fO526UarncccwpKTbzg9VL3WwFVvSdH107BuiTLTDe8bDz3MyIvR1JPqP7Uh/jxMK8H9n19EiylvXWKjE2Ol59XaPv7bxnvYy05fQxvViejeYFBAn6llo0f9ea6/Xk3uaXg7ZVReziuSfHTmBmUTQamK1MnpWyijmVQFPXpZrbEFZF7ZXY5fM1H9kV4CbDDjHWqpeVOik7s9b11KRVSZb1XC2MXDCsEOcd5azCW/EA7khqx2/e+g3qX/pmx3fZ5Rq8jRv42xd2kWMl1ERzYKGHgYUeAAIDdbRce2OXK4UTeSH5eCsDvxHk91Ot9KJWkqDIcLpIKMcap/bZlTfMKx0AZDUrGRi/S6PgVbW7hdZitVwZtkr6jjtNiKI83sduBRE1wVSTxVvXjjMxH+Gd/E4rmToIPHxiGHdkk/VrZ8Dl2mWFl6FH6KzqlithRVrPCHrE2E7QnhmsuLhote9Ywgtkd7Uho7gSLaOMup0qj1HOT1pB6UYwa9MHxV1NDbEoUnDnTNW/qpn/wQ6+NeS42lsq1bLGOs3UZPX61fbPUT73clbwlZKAUdfLap2Mrm+kSKK2JFvJjKhUwDCDfNzn1w4zdH6MaE161zHFvAvRXQAgu1qHJImE+qdIPu2isKk90BgNsC5JNPQxLqev2Q3+rAYZNrp3o/5i5Z71FhCVIMZ6foZq2OmHVnRgtRbYRu4U6n5iFrxaTnvQe5dOSbFdC7xVJDeDyI5J8gJHed/q+9p+htvEWM+qlkmHyfTMMFAMIBQ8hvWwQ4zNrNlWoUV4jfSstWD1HSj7n1N5yWgou+MY9bvR2sHSIslW5151X1L3Hy3rqYxKLP60+pRVOVQ7WJwxT1xlVYt/L/Dck+P55SCtbPvb2Jm0yyXGdo+1QoSt+iEdpEx15Wpk2snhLh9fjXrpBeeZwW6qcCepxQc3DpH6qJXAK9fw1O68f5kYAxSSATbvDxAcGKfh1a9ITXbQ8vgYoip4r1w4FalX9zk7yiTp03fIDo4CIK5HCX5+FTGpnX1JDSsDazl9ymgsKZcY211wOnGZMPILt7OAN+ozu37T2S2yioPo3mYEmRDLluDBwjbJ2nUvmZ0LciPS4Bse4PhEP7gAlzX3FCuLN7vWW7V+dTlwYvFV/16uG5iRwUTPxUtL4szIhUINrf6jVsZS10OuayVwUPvUftTruSfHsNOy4XTr1wh6bgrKNLZWzrXzWyXP2QtUSkTeaNXs5N7V8jdmg2qlLdhm0LNKa06GBZDee5N82xySq0Dy4jeIqsmwZughDeEU60WJ1GQ73sYVNvt/DpRcLopZL4FogsTNi4TW67bOU7bjzoQX0WL9jVQM9GC0GDJ69t5HRyiGE+Tb5ynWxtj87V8Q/svfZkDwOtYEtxtYYwYnW9RacEKKwbrvoZWyzZ6NVl8x2mXRCma1slA00ua2g3J3BaxCy/IeOR3mz6YzKC3BYL4zojUO2JW2NLo3q4oUVqBHjPWSvNiFWZvVGlOqGSdhZMFVuwiZ8YBu+ZyMfv+xUhcnfcPsnJs9D2n2Feh4fHLrO6djeKXqtBf4VpBj2DuLqnrbpJrXNSp7P4IQzGBWHyt+wOXoRZtBfV0zP3GnGRDN4EQGUIaAgGeuHQAxFiH52ifg3ZktK98/jpwqpJjfprmFrBdvbRwpkGYm5cJlEOBmx1fdSTvcKttga1kNMesj+PlV+nwSscZFnp68bek80zrYhFa/1NuOt0uQzaTVlO00pljElONnaHUs0bqGUSIdPV99JUk2c0mq1Bhg9q6dLu61dPTlcr640bv1bIY0zrXrX2nHL96sjjKMMimaEWMzS7FMip1age3Ejei1f6vvVa8PaO0mar0zs2BhK+OpmiTrWZGdLJjKQedyK6GM9Z1Hp7JtWufvJ7415FiG3AgrZck0Q7mk2Mhp347lx85Asp/EWs9HEoBnvldqCRcZ5WQms3O/6gC9ShHkSpN9d6yW6F/+Dp01SfKeLPNHH7LROr/1ezHvYvNBL9mVBq5vlvzohvKNlutRzWBOPaIlw8ifdTgPgzMtnFn+DqNZ88HXLOW0ExhFqSuhlchCC0YEwklwWqUt4kZ1MMs0aRTQKuOWe9mUKFfDYqyEEYnUg3qMUQZjKZ/JupCzVAct2CXFVuc9J4RYRrR7acf/6nTgejsfTgO3ncDJfGF0TbkdGxFk0G+ndjSEu4G+Z31OHZBZLeORHho36zS/t+oOKT8XI/cQJdQLjb3KnKvGt44cw06CrMR+S1FpwQn50No6daJXutcwJMYKqPWGlRqHeudZsVrb2eo3U7DQqmulYSWNOMDURhAIwqdXiT77brx9nOW6ecanQxQLWd1zzXx+D5KPu/JZ/5lvovSHb/t3oy16OwlaQJvwWY1Sl6Fuo1a2krUi3fXqvV6wn01SrpeW+1m1EiJYlSzUwo6xIK/fF8u12mnByja+cuIfLIR0+2ytpB0oZ0Z0zGIt7MxpeoszI0Isk1896JFiPfWFPsUzc5LYSQt2Fo9Wn5cZ8XPqB2ynLcrH/TG725q6Dmr//b3MrqtEOWOFctFhlJhpr+7NMTlOJpO899573L9/n7a2Nv7+3//7AMzMzPDDH/6Qubk5otEob731FufPnwdgc3OTP/mTP2FjY4O/+3f/Lt3d3QAsLy/zs5/9jPHxcYrFIkePHuX73/8+kYj5g57KCcQz25+NUg0eNDcEJ1A3PvUkfZDIjBWoZd1GJ6Kaxw27Ejt1UTXOV8LJQkj9DLUI0l5KzdjOJIRim3pFgJWdiUCs+HeOZgRN/UinOpp6MJt81AOgckfBSD7PyCqpp1GtxlC+sZSc4pm8gNUU4XbGG62dHt2odZ38K+tCbkd7NJs0zLSX9ST3Kj0paVmUzVwqyt1OdhL8rDxXTz94K6COILiN4xWM/LG1yjaqayVIMegTY7ukGHYmH9ELbFb2K2XmMyUqPdZUG3YtyU4Wa1ZcLqxcW/6tUn3Z6cJTb1Go9Sy11G4iOQ6mznEul+O//bf/9v9v777j46rOxP9/7vTRqDdbslXcZNyNwQ0wOMEUm5IQBxLKkmRJqAlfErLJd39LQiDf7CZZkmyWkAApBAKEFFgWSCimmWBTbVxwwUWybMnqXZo+c35/jGY8M5oqjZp53q+XX6B779w5tz9z7jnPwWg0cvnll1NWFngIe71eHnnkEU455RSuuOIKDh8+zJNPPklZWRnl5eVs3bqVOXPmUF1dzfPPP8+NN96Iy+Xit7/9LXPnzuWmm27C4/GwadMmfvvb33Lrrbei06XaNSgg/MBPtkAxkWS/yBLdUNK92STabyPpgJiKWWbFAW9koBMMiNZTGfetwFgaTm3xaAbUycqTbnaMbl/i+ZlqbpFOWrfwIGQ9lSn9cAjul+hBXMLFG9AlfLnoG3a8jjjpXGfRQWh4rVAqObIhUBsZKzBONZCKl6EiUQ/9sZJsMIBUHu7JUl7FO17BfRA8f9Ybkn9fqvsmlY63mQyMIXHb93j5invrS2IGyLFqioGYQXFQvKZp4W8Fg9INJNOpqRzpm+PhNrFLNT9yKsKbXISfo8NtC52KeE02g9uQ6BiFH9tYPw7DA994zSljLTsWhhUcb926lYGBAb75zW9iMplC0+vq6nA4HFx88cUYjUZKS0vZtWsXO3fupLy8HI/HQ35+Pnl5eXi9gU5ER44cwW6386lPfQpNC+zIK6+8krvuuova2lpmz56dsCxHdQ7a9P4hD694D61YJnrwnO6rimSvmYabGzmVcoz0B0nV1D7WN+fAYC1fMBhY6i3meezU+GxUJfh8OoFAojLEqz2Ors1KxUg63SR76MZqz5ms7Waqv85H41V1tOG+lku2n6L3S7yAU6fz4/cP/QGerIY5UW0ypD5QRajG35z4+8KPY7xAMVkHwXDRbWODzVSWevVxa6qHK5VmFek+8NIN1OMFYKFj0KCPaBbxyareUJvPdMqQKNtNKg/3ZIFxLKk0V0vU9j1eyrbgiHyxvi/6/B5O1o1wye41kNlX+JDaICHhUklTmKwtLWQuSA6OMhk8h2OVIdZ+HW5ay+ggOdYyiZonBsX6kRF8JqWS7WaHoZ3zdKWcwegOdjKs4Hjnzp2ceeaZEYExQEtLC0VFRRiNJ9pXlZWV0dYW2KmrVq3iwQcf5Pnnn+eqq64CQNM0vF4vvb295OXlAWAymfjqV78a+jsV8RLNR0tWgwATP1gerkwMWJLqDWkkwVPV1D5q6nOHdNI5sfJcZplV3PyRyV4Zp1qGZNs6nEA50+K9og6/kSSrOY7+JV+tBa7r0Q6QR/pwCxpujaam87N6zX52bp9Bf1/s3tjx9t1wmtfEK2eyQDzR8Ys3NHCivMXhZTmgH2CHOfVBaeJJZV/Eu0aS1Z5musY6OlCeRdbg/uuNqA1LJt4r63jSDRBTvbZi3duiU1iGiw4Io5v2REslW0O0TPwASiTVe0dwHwb3wUjSK2aqc3aqg4gkE57xJzpIztS+Ho1+WvH2YXi5k+3rj/TdMNGCY5/PR3NzMzU1Ndx777309/dTU1PDJZdcgsvlwmKxRCxvtVppbg70oC8qKuLb3/42fr8fgyHw1bNmzaK0tJT77ruPc845h6VLl2Kz2UJNNdKV7BdcKh1YRtI+bSIZ7zKnkzs3+uYwy6y4wlWV8DVzvOAsE00ukv36zbRUa4zj3fiibybpBjvBfRz9ueG+lhut2p7wcqUj3v6wby5ktlcPCUYETLU88fZTslrtWCnOYok+7vl6CO7ddGvy4q0zVal+Llkwkai9dPAHarpvU4Kie8qH1wyHrx8yc16O5B4xklffqQyIES5ZABwt/B681FucVvCV6nLDfS4nEh0YZ0ImA2RIrb11ssA5/DqZRRbPe+1xO7KnItOVaImen9FvOaMDZIjdPK7El15z2+FIOzi22+34/X727t3LJZdcgk6n46mnnuK5556L24FOqRMHR6fTRbQj1uv13Hjjjbz44ou89NJL/P3vf2fRokUpd8iLJ520IZDeeOXDPXkykbcxKN5rwkwExIlqBNOpPY613vD1JFsmeLET1VTggH4AXLbQMJfJ1pkJyWqy0hrMI4lko/SlGiCnIzxAW6qzcEDvTKmJRTpt4VORqR8hqWYYUd7MJewZadmTNYmJ/uEfnq1iyHlH6ufdcN6AJKsFjvdaNLhN0UFxovtNuGAQkMq+Dr+3f3KqD+iNWE8mZTqfa7I0nsN9lgynnOGvu9P5TPir/njLxJKJzmJVU/uS1hYPJ31ipt8QJstskUqZoq03BDquH/adeFbG+46xEi89aqy3nNH3ueh7yg5DO5V6E6Ndc6yp8Mg1Bd3d3fzwhz/klltuoaIi0F/www8/5IknnmDt2rUcOnSIG2+8MbT8pk2bqK+v58tf/nLSdbvdbnbu3Mmrr76KXq/n//yf/xPRRCNcV1cXL7/8Mtu3uRnoV5T6EyepjpdKJzQ/xXZ20eOuZ1rvwImmKsk6RsGJco92uYLCyxdPt2/o/sxZaKTvw6G5PsPLnWjd3T44oA883Er91tDxDP+e0doHybY5leM0HPFyo4afy8HtD5ahW/PQqnMAJLwm4l0P3ZqHqkUW3tvTFfp8rH09El6jG5/Rg8lhRVO6Udt/iYwk72xQsnvKSL4/eAwh9nG84rZFvPPKQep3O0dUvkzsh3jfGVx39PkYfT7l2tyha6zbd+JziZaL2IYY509lbvJ7QSr3slQlO4dLFxtp3XViXye6ljJ9HxvOdsbbnnjljrV8qudWrPM0ne+J9ZlcmxubLXDeZWXbhyxr7x+a+WhgIHB+GmssdHzgH/KdybZnJPcDyNz9NVyi6ymW8HMvuP9iCe6rWJI9x2Hovgy/30HkPS96v3ZrHszZiurlinXr1lFQEDsP80ilXW1is9nQNC0iaC0tLcXr9WIwGHA4IjfS4XAMaWoRrrW1FZPJRH5+PiaTieXLl3PKKafwwx/+kMOHD3PKKackLM+BJ120HfEDzqTtK9PpCJHIaI6Gd9iV2g0lWN6+wfL0Z7xEselI7RfsjqgaiqU32Oj7fbJSJn4o2F36wV+PJwKCVHrrj9SxYXTMGano1GzRHbKia4+HpHIjed7fuG6EHb92ssPQMKSmTxk85M06Sm7LFHR+PUZX/Gs73n4ZOGsrvqktGGursW4/NX45Rsl4D+qS/vcPDYDv+GUNf/6v3bz0665hlWGsxDofU++gN/R+MMvsjtsRDk5c+6ncD1O9lyWS6rW/9AYbOx6I3M5EeX4z+UYsE6MoQmpvWZO99YoW7zina5ZZhZ6FqdQYR8eHgRr4QBCdfUURx34bKFOq25PJ7Ugm3be3R0JvlwL3inhNmaLjiNwEzVH6E7yxSOd5Gb5PYzUri9cROWcGVC8f3WE60l670WikuLiYo0ePMnXqVAA6OzsxGAyUl5fz8ssv43Q6QwFxU1MTM2fOjLu+1157Da/Xy9VXXx2alp2djdFoJJVK7Uq/lYLBXyPRr5WjA4vhJu6ONtKMDMNtHwix2++M5EY6nFHkEl2cibYlWWqlVNYX/YoldEzDk6BP8hR+8doOh988UgnM4rUjTib4Sz06nRlA1ewGGhbuRT+lGJ09i4W7l8VcR6LzwPbmGSjND9rYv+bL5GvR4XZ8yeSoi6nkbU5XqudLonRL0eWKdS4F1xHdFhgAQ+wOt0DCbDWpGG6QOBodAzM1EEY84c+qEa9zMLNJcMS2mMctTCp5ndPpuJesGUr4syydphTRHRIrBkyAO2EQN9IOrPGkGoek05cjtC5X1PFI0NwiWTvtRM16ottKxypPsgG2wptaRE9b6i2mRKebmNkq1qxZw6ZNmygoKMBqtfLCCy9w2mmnMXPmTLKysnjmmWdYu3YttbW1HD16lMsuuyzuus444wweeOABXnnlFRYtWgQEUsWZTCaqq6uTlmWTsYlCfeyBIxJ1dIkbOLiSP+yi8yinKtPt0oYj0UmfKKNDqpkJ0m0PGOvzydYXHpQEL/Q/m+tPHMvgMcxAFpJEuVATlTFdiR4gwZvxFa6hYUG8YCTW51OnqFhyAM/MIwC8//ZspjcFfuDWH5wOB6cxx2cDg3fYab80pQM1trkrxyujSKYFj3X0q8hw8X5UpbI8pNYOOdZ1GOv7ossSrwY5XKxrbSQpJtM13LzFsSTr8zKao1IGKzLSzcgU7zkRPqzx4TFqDpXoB0QqWSgStS2O97xLp3ZzOJK12c/kQB1B0c+p6E57mUrPGZQoSI41kmT4vSTR/Wus2hwPKzhesWIFvb29PPHEEwAsWLCADRs2YDAYuPbaa3n66af5+c9/Tl5eHpdffjmlpaVx11VRUcFXvvIVNm3axObNmzEYDFRWVnLddddhtSZuRxw0khM2Ue1yXHEC6NF40Cd6hZiOkfbYDd5kgyd8vNrjkQaJsX45x7qQEvX4DzXsjx5udhg17KNV+5xOoBavY1ZQKud/vABZ6Xwc1JxUVzfjPHUXAO45q/B0HAn8f3s+VT1lbIv4vIaGBl4j6FMb4jpWuZPNG+l1NBbB8HCv+XRGS4sWPA7h7fIS/fhJNC9WDU28XuPhhry5iVpfvO9e6i1OaV/FurckCoIzFUykm1c8FeHpEdOR6VziqQTdsZ4TwcAz+N/ywSGiQyO2xZFsX8W7Jw35XFQNZzAojhUQxwuEk6U2DDrs0sgJC/oTvYlOVyrnTvh1l87b7FSbWswyq4gR9uIFyPFSog5HsprkaKncHzrDOteOlrQ75E0UwQ55T/x/fYNtjkdPqj3f0/nccKX7uiUo3RM90c0k0cke7+ESq83dSKRSYxrx/QkGUYD0eo4Pp0lJtERtioPTY9WwxTvfhpMr11zWSsGqHbQ8+wks05rJW7YPgLOmf4adP/eBzs/RsmMYcvrRZ/eTVRVIyWjaV8OCQ/Mj1jUaQUUyEyHPdLR413ymy/eG58v8v5ufHNU2x9GvzWMd42Q1xsHzeKxGtYLYb3KC01JNtxduOD9Gwy31FnP+VwoijlU6I/5lMpd4ujXGyfICB4PRWMOOQ+pNR8LfWqZbhuiyhEuWxi7WcV96g40//6Y1pe9MN+dzOk2qUjlH0g08IXLwoejvGsmAQqkYSYVaeJlLqnV8/t9zJlaHvIloLG+8sb47lVqv6Bt2eG1ostfy6dSIJMv9m8kTfTxF799kD6zo4aij15VKns1EhltjHq8WIlZAkckAa4ehHUOXYukrK9EMPspbpzPgqMXy3mmoL7np/OQbGAt6iXXb8ZW2cWhPoP44FaMVuE6UgDgVye5RE2Vbko2uFU+8GuNYTYFGW6xrMd3rc6QBcfRnl+oiO65Gt1dPli5xJCk0Myl6qOng36XHA8+VpSNY93AG6EinyUSylHbBY55KLf9w3xTF+2wqb6ASjbCXaPl45Qnmrw5PnxaeIjUYRySLHVIdYCtRG+RYZY71xngsTfrguNJvJZXf1aMxkEM6r4ITHfR0RvNLtN7gZxK9Pkv2uiSV/Jmj0ZwiWqr7NpUgOTp/bLJ1RktntKyxyNMbS6JtjHasrJalle2Aori8B4CO5iIsVhfOs7fiK1mFsSDytZW9fip9H54CPh1z3OmPXHkyGsmP8ng1N2O1v9IdrS2WeE2bUln/RJFogJZE11O8H7XJ2v6Hfz46IBpJPvFUjbSpRufx4iEBMgwNmjMh3rDWkDgohvij/iV7+xGUKOvPSALjRDnqx1rSWCUqjkgUJKcSN8Q676LP61Q6eALkjG5jAeAkCI4TidW5azjiBTzDPaFTCaCSlTVW+9vwGtBEA2QkCpDTrTkezTRmqU6PTlIfqzY/k8MAx5OJfZGoLIleyaUaGNcV17N61aEh0y1TO1A+DU/YyENtrTmUlAbOG03TmOPIzOur4byWnkgycb7EW0cmM1mk+93homupEg18k+o6ow1nO8N/RMRKaRhPslH2UgmKUzkPU+0IGXfeGAzYkE6Hv9bGkiE1uvEC5OhlgtINnJVSOGNsfngTjujnVzq1xMnuocFa/tF4ZmTi3pEs61M6TYTCRWdDGkmQHL5cIqk0WRyLEWpjmfTBcYVR4TJHTstEjWzw86PRYzR83fHKmPQEj5ElILoxf/Trl3g3w+G+EhmtEzbd5gQj7QgZK8XfaBz7cMlqCuMFJbGCp2TBpsHgo71yP8cbCtH1n3hl6PPqsNtN5OQG8uju+bCChqNFVE4v5YVnAynawh8QPWns55EG8fE+M56B8ljV7oxXW+p02+Nm8sGcqvB1xOoUmEp/guHWFocbznkcq19B+H9jvd5OVzoduGMFybECn/Ba2mCgHCv4dfkVRg10mhYxPXr56Hkfufw83+dnqUVjbbae/S7Frzp9nE4PFxP7LVWqHezCpXucE3XAS/dczsS9I1FzwFQCyGRljjVaXSpBMgy/NjlavDbU0eew2ZjyKodt0gfHsSRLUp5qvt3gvOG+Mk9n3bHKGS3dCzKdADnejSXY1i16XakkR89UMJHqK+d4PehTXX+0dHoLB5dLpe14dPqa6PKOpK1ntKLiXubObyA3z0lFVTt5+YEcpbs+qKK5qYDzN+wILTt77nHaWmKnRQw33JqTTKRBGmmP8eEar2YCmapJHkkziuHmJB8Nw/mhlKgiIrWASZGb56C3dDdWqxuD0cdKiwdNU1g9FvxuI56OfBxHp4WGI0+lD0S8aaEgOc1a5GQVLSMNkuFEoBxem9x5vBgviv9HMyvIYsNgQBseEB/EyX5cfIJsstGH5ikUL9JFLS763DrqPV7sfijRQ4fPy0v0UoaRQvRo+DEMDmlyYtCsE9usUBTMqmegsBO31YHH4sBn9KK6czHtO4WazsJhXUvDymus+dFn29F0fqrc2XjN7WDw4c+yo+vLobbLgj7Lic7qxNuTk9Yw9uHthKMzW8ST6nYnfPMRI0Vq5dReNLSM91VK+lajWA9xfjhlyqQPjqcW28GVOOFidE7a4RqtmsR4J3a8G3eiCzVZR49UguCIstVHBkvB+ammpQmVmdF7FT3S4CFesDec4x2v42Wi746W6sM8Hr3ex9TyLtxuPfMWHMPpDLxaycu3c6y+iKn5LhafWs/iU+sjPmexePGFNauAxOmWRrPDYDITpTZ5IksnoB+vTi+piBdopvNDKVHTuOB5G6g5a2NqWTfdXTacThNVM1uYO+84breB1uY8BvoteDx6XE4jfqVh0Psxmb3UlLeSu+QjADreWM7SjqF9AIZdK52hphbJ7kvhz4au8gZQGnsPFeIzu8hpK2VKRTt+TaE0Rb/Og0fno9vgos3owKHzMlcroLsnl79lO8nxGWk3OvFqfvK8ZvZn2ekyuuiyG7D6DdRb+jD5dUx35dCY5SULA5rPiK27gAK/nmqlo8fgxqnz8p7RSbfBTn95Bzk+E1nFh8mbb8LSNoVptdMxD9jwGT00LNxFU1EHJbUzyWkrpbXPit5jxFvahv2srbi6c8juzmXgoxkp7zNN82Oxeqi31nFKlh4NOKYboLK6DZ1e0ddjZd+e6bicgTdyS73FWKY1k7t0L8qnR/n09Op9DLh1+P0aDoeJ/Hl2ik1eAFxOI1arG+U2oel9ePuz8Hbnovx6fA4z3t5slE+P32lmr64Lh8MEaEM70jG852OiUenipRA9ECg62fMPkjOtlZm7lpDTURLqoD1ZB9+KNumD4+b2LFzN412KE5L9Sh9OPthEN9lUApdYN8VYKX6qpvYNGTgj/POxAuNU2+d1a56Mtu8Nl4kgLZM5qlMdXjXR9w03WLFmuVi++iBZWSeGMs2yeWk4WoTZ7KGiqgMAR8MU/C4zym3AXj8Nv8MCaHjj1Gil0illLNrLxjIWgfJonbujYbKUM1WxjmmqOXJT2Rez9QaOFjfh67NxGjaKl32ApoOeHit5eQ6cTiOvb1oICbKzZGW5mDlYeVa45j2cx8o4ozuXD1oNOJ1G9Ho/muZHKV3cdcTaxnS2I1oq+yK65tVX3IF3agtMb8SdPYDOY8Bv8GLwGditKXRKQ6d0+HQ+DD4DWS4LBQO5WDy5aDofXr2XTruJY2YHub02jD4jTWY7Bc4Sytxm/Do/fqVRruXSld3DgMPK/LZSchw28uw5ERlwgqMjVA7+16vz0WcZwGV005PVx/GCVrrOPoRX70NTUNJbyKx3VmHtC9QoFgxun+Y24s/vhuqjmEs7MZd2cHZ3LprOT6vPi04X6N0VvDdWzzRRM68RW7aT4pJefF49TqeR/j4LmgbFej+HPirD4TAzraKDs9bupbsrG7fLgF+rw1bay7vvzqK7Kzu0LcFzVQN69V6MRd14OvNRXgP9Rg96mwO/y0hVjgd/bi/oFP6cAfzl7Si9D2V1UIrCqzT8TjOGvD6cDhOdHdno3z895vFORbqDBYUbOFSFty+L/fN3k//ucg4PGJitN4Qq1EazWaLZDBWjtvaASR8cH/No9LkSLzOaBwkykzoI4p/c6WQiCF9XspshxA6Wh//6cfjlyqSR5JKM+NwIamzGKyXN6jX7MZki36R0tmejFJRM6cXhMNL3+mr8LhPEeFCnGlwm6nU9nhkqRrPZxXgFyPG+M18ZT7pAOFWpDGwSvkz4Naw0P26Lg9qiFvwWJ+55BygcnOc4NhVt8LIwtBcz0AG+AStLvYlfG+caWlAKenfMQ3mMNGV1klfYypnze9DpFNMrraxbvxP7gBmfV4/LbcBs9mAyeXG7DTjsJtwuI263Aa9Xj1Iabrcet8uI0aPHndWO3ZFDVk/BsNO6nbguFX6Dhj+7H19OH8pmx59lx5/bi1GDvNYpGI5V4rINYO3NxeS0Yu3Jx2TPSpq+MTgsTfj7xniJ0YJnrh/oGfwXLroG0uDXU2APrHlqTzE0BQJ6j96L3q9Dr/SBldpOfK4KeNfWzPHqo6FpWV2F6Pqy0Xx6vCY7yq8jd9GB0Hy/X4dS0NaSx55dlbhd8Ru49vZkUV9bQm6eA5PZg6ZB3eEp9PVmRSwX/QPe3Xri3FQeI97uwHfUOazQGtjG6GtbofDn9aKMHuo9fkrO28K0rE4c2k76jA66O7PJr5uDP7oTVox1RTugH0j7nqncJpzHpuE8Vk4HGubyZvpX7qL5f9eBXxeqYR4NJT4dn2d0Gx5P+uA4Fem2HQ1+Zqx6SY5FHth0aihHkuMzlQtstIOMVPZnomwB4esZbg7kZIMnDCdNX7B8ibZv1/Zqior7KOwrxLrgICaTjx3bZ/DJ83cD0Hy8gCyfHlNxF8qvw9MxNANF+Mhr8STK1TkRDKudYApGu339yWik1/twfnAFOxPNsHpxZvfRmdtLe2U9dls/yuzC7zXgbi3EZ7fi2zUXZ+MUrBVN6CwulIKuLafhbiuKWGfC++CHUwL/QgL1ngaDD1CUTy3h9ZcXYrO50Ov9mC0e3K5ArbLZ7MWa5cZk8mIyBf6/CBOayYHe7AZdEy6Dn4N+jUVvfDKlztHRlRlGo5dVs7sxFXdhzBlgwKtH15+Nri8H3YANQ1c+2pEqDE1TcaERrG8a6QvykVRMDecHQKxX+isGpuJ/rxQdJyoDguueN/j34Y8WhOZZimzYPszGBhST/Plnt1uw2y0JlwmXThPJoBqfDQ0NfU+gVnyG2cWA04SjMx9PewEmTykVZa1YN2xm5/Zq3C4DHo8Bvd7PHH8udaqT6QYd+vZidO5A8DzLrBjI68JrdjGzdUrED59UmnqeEFjW1VxC+yurwZ/625HhqPHZJJXbWEpU+zucV3WpSPWGH30xDafmNlmzgUwENqkGI2NRc5zp9STKBJKqTL3BCAQL/ZhKOzgjtx+d2U2zclJ3eArtbXm0twVuoEtKcyib1o3Ho+eD92dw6ul1zJjVCrNOjP7U/D/nARrmslZyl+yj7aU1wPBqXpMFMeFDfI+Fsey8l2i7xyNwHs23ZcMZ0Wr49xfF8UU7KCzpxFTQQ/9HM+nfN4t4TRx2GNrRNEXVjCa0qW3sLmoP1HD1WXEemYq7owC/yxSzA9SWuhwgB/YVAwoycJ56vSfSCnncRrrdQ2u7+lOIQEtKezht5WFezWnG01FAjS9+NovowPg0UxZ5y3fhd5vo2z2Xyq5SDvniVOvp7Rk9XzORtjQdqQTUoSaE4cI+l69PfM1m8v413EBZ5zKT89xF5ITNdzZOAb+OU6b14TA5MBp9+Hw6lNLIUnoGspygKYzd+WheA3sGbHgqGlAWF9buPEprZ5PdWYTRYWXW0MpnDru0xG9a/Xq8vSf243DyhCczlvfSj1VwnKkenenc6MNzcY40AA0fjnW4RtouaThqfLZJ/wo4uuypvo0YjZRw3uJ2KqqO4plRj6u1EE9nPtUV3VTPbOXNzfOorGpHb/BRNq0bgCXuUjhWSmv7dIz5vegsbnKX7MPXn8XUyzbhbi+g+72F9O46BfxDr5HxyhBxshitH9f5+uTDIo+GZG9FgjJxz9OZ3WTPraNvz2xMhT1kn1KLqbSdnvcW4/cEAk3lMUZ816ppTjwzG2g9WIl79yx8/YH9HTiP7YF/k+jJV1jUx2krD9PXZ+G9Hh8Y2odckzWEZ/RRHJtax7wCP+dObUOfPYD9UDX9H80Av55DJH7fPZI3b8MxEQPoiO9zJd7WTAXLqd5nY70RDmV02rYoNM8ZVbZTdblYpzfhs1sp1xnw5fZh3r0A47HpeKY30lBdj2/hLnSaQu81YHRa8Bk9ZHUXYO3No7Qnn+z2YmpduiHfH+uciZe6MPr/U9nm8TCJbhEjl8pFH+ugjXTo0FjrTUf4iTOSkyiVpPmZCuSD35fMRKjhGul3pJoGK1Ev3njJz/dNr8V56i4ArFtWYWwqwzO9Ec+MQKYJT3cujmNluDvzsE8/yuJTj1BYNEBnRzaHPppKXv2s0Pr8DisuR6DJhONoObZZ9WTPP4SpuIusmcew11YSXiM3klEFRXyxrq10Uq39q+MqAFbcegob/7OC7du388pF+zG6TjSHUQSWD39VGn3+eXSBtprhr5sB+s12WvM6qGorR6/01DfnMJDfxYE1mwGofm85Bc3TAJhh8YGm0PmHJl6PVdMEcCirE2NhD66mEmp82UPmAyidD2Vy48/x4OzOZbZRh/2vn+agfoCcRR9RcsGbKAUMdlDq1vvIay3iHMA8pZ32d5cMaRYxng/gUr81ze9XmKe2Y6k4jqUskDbN+fZpLPVG7k99lgNbTR0dBT0ov0a+04Jm8FKS24+rqZS+D2vw9OSg3MmHRE7HSAZwGY5MplFN15DBxMhKmo4zEzFDUDo1ytFlCX9b94G/F47aWOotpg7geGHomJiOVmI6WolCoaxOlNGD3zaA3mXGVtyFI6+HppmH0LcVY9m9gEOuE98Z/t9gmZINxJVukoHo7xgLkz44Pqpz0KY/0QAl3faxwZrYZOmCxkOmbubhJ2qyEyzWyT4axrKmK5FMtH9OFiAPN7XNYZeGzzZ4g/FreKoCnUqsO5Zg2b2A9jVvkF1zhOyaIwAUAv19Fnq6rdiynezdPZ1DLgcYHLHP8dpcLhxsamfP72Cgxs6xI8Us1VmGfd6Pd4e8iSx+2iTFAUMfpqJOClfuIuutlRjaAm1WpxX0se/cl/kA+OUvf0lxceQ9Yd++ffz85z/n+r9ez+zZs3nwwQc588wz2b59O7t37+b2228HwG6309OTRWNjI+3t7eTl5bFt2zY8Az4KGqeR016CtScfj8XBoeVvAfBheS0Gjwn/4kBNo+bXUEBzzUf0lbaBgo7qIwAsee5SdEqH1+im7rR36S/qYNq+BRQeq6S55iO8JhfT9yzC4Dbj+MS7eKwOSmpnUrZ/Hi7bAI7cXvpKWunO70JZnGDwgVePrjcHQ1sJ9n1z0NACwfSO01A7lqGhoXQ+/Hm9aG4j7plHcM8+jO2VteT3jm4O1GRGeg1YprWQv2JX6G+lwNd/opNX7tIP0WfbMdgcuNsL6dtdA3o/OpMHNIW7pThmx6zxNJY10qkG08N9Dg353CjWLqdboxyrNjc8SA5/+xz9GQ0NzWEFhxV9b6BjYG9noLuqzWrHtXAvvetexfTeYmyz6unLcWA8WoG+Kx9/bh9teLB6jNS7jMz02Ki36/HZA5mQwqVTqxw+Pfi5Ep2OM0i/TXo6NKXUxIhS0tTV1cXLL7/ME/9fH21HRr919lgFyekExJOpBm/pDTZ2PDD8wS6SGc0OlLFeXcf7vnRqiuONBgTg1/nYf86ruLKHPlD69szGXNqBuyeH7NlHI+Zt+vsSVp31EXa7GbPJy9tb5g75fHigPLvmOLPnBnIhKgU7t1Vz4YbF/P63DRHLB2VydLx4Kboybbxf2WkGL3strbicRqZN72TRqfX075uJo6EMn93K1ItfBb0/0KRFFzh/fvOb3wCwc+dO7r333tC6fvnLX+L1epkyZQoHDhxA0zS2b99OT08PBw8epKGhAYPBgMfjISsri/LycrKysrBareTm5lJUVMS0adPo6upi8eLFAGzdupW9e/dy7Ngx9Ho9GzduZM2aNfT19WG327FYLPh8PjRNCwXVXV1d9Pb2MmfOHO655x4qKyvxer20tbVx5plnkp+fz9NPP42maeQ0laJ0Cnt+F1ndBWh+Da/Jjd/gxZXdj9FhxdKfQ057CdkdxRgdVgweI1oaac8mcp5miH3/Czf0mlEYcvoxFndhm3kMzeCl7cWzCQQZisJz3sGY18/A4Ur6986OmXnm42A0noGJjlW6nfrDZbJ2GUZ2X4sXcKeyPz1lTdhXvotm8NP/0QwKjH78Njt9DhMoDc3oQWfyoBl8GLIHQGn07ZvFhz3Q12sFtKQp5JJtW0m1js//ew7r1q2joGBop/JMmPQ1x6NpLGuNJ0pntlQkG441Vem2lUz2PcnmB1/VovOjs9sCaYyy7Og7C8BrwJ/dj+Y2oXOd6HkcLzCO/r7wzBazzCppbXGiwBhA8+vIayqny+TEbxvAn+VAZTkAyFlwCABTSRfOxlLc7QW0WnpwuYz4fHoajhaTl99PbukAtmwHA/2R2SfCf4F/WOsLBce9PVY6O+L/Gh9uYJzoOhqtrBLDlfkOq4opl7zKlKipBWYf2ee8y8WfuYDnnvNz4403snz5cm6//Xa6u7u56aabAPB6B2ttNQ1N07j99ttxOBysWrUKi8XCvn37mDNnDlVVVaxYsYKioiJsNht5eXkYDKnd3jdu3MjGjRuHTDebY9c8nnXWWRF//+IXv6CrqwuDwUBOTg4lJSVomsZVV12Fz+dDr9ejlKKuro7m5mZ6e3sxGAxMmTKF2bNnY7Umz44S9B/Wxyd8IDwcsc47T1YvjoUHMbSUYtm5iHy/FXR+GmceQG9x0/K3T4BvaHOWj5OJ3D463eYY6cYbI8nvHt05Ol5NcizGpjLaXz0DY0EP7tZC+t2J3lAoTCWdZM2u57S5gbSGfX0W8sz76Xr7VLY5HTHLNRFIcDxoPJpPTLSAOGOvmKIkuqEkqnnN1ENw4Jx/4CuJcXzDaurC6dsLyXr97JjlnGVWMWuMg8co3X0YL0iudelg50IsOh99n3km7uct01qxTGvF2DCFnn1zmZIzgL68i66ObA7sn8aaT+zj5eeXRPSahxPn+ydOP1HznJfvwO2OvCVkonYi2fzRuvYyeaNNOoS5zgdh7W53GNrR6318cu1+Bg5XkJs3gK+4E+PhGTy27UcA7N27l/fee49rr72Ws88+G4PBwIMPPhixWp/Ph1IKg8FAf38/TqeT/v5+XnnlFebPn89NN92Epo1vsFhaWkppaWnMeXp9YJ9omsbMmTOZOXPmiL4r2N56pL6s+2NG1jNafAVdOE7fjvW9Zbir6/EWduA8dTea0sh3mjFvP5UCd/yh3qVp0wkTOYAOb44xkmB5uJ2mEwXJifaJbyAL30BW3PknaLjbivD05HA4pwmlNM48Zz8AWdUNsP9Ev4CJFBjDSR4cj2d74Wij0Vwi0Sh86TQxGO32v6muPxPNIpTRDT49ml+PZ1oj3rJmUGCsrQKloQwe/Dn9+At6YgbGAL7iTvo++zQAr21bgPNY2eBrS0XXeZvRW9wY98/B0F6MP6cPX1EnmtOCZddCIPbrzfLytoi/jx8vQaHoswxg8UT+8g7fB7W57Vj2zAq9dvd251FwxgdD1m+d3oKxoAe9xcXA4UryWovJOaUWZ1MJC52lRLf5Cl4bWz+YRm5eIaetPAxA2bROhjv2UKYGismEsbzRWqY1k79iF22bzsTXn4WxoIeLZ7Xw6a+v5q232ukobEMpxXmfuJAvfvGLoc/Nnz+f+fPnJ1x3MLgEyM7OJjs7m+LiYq677rpQswqRvt/4rxz17xhuAK40P85FezAdnoFr/n78uX3483swtBdh+mgO+s7CpINxDDfY+7gG1eHbXa2Zku6HTPRTSbbeeD/Ik72JC5fqfTA8SI6uRY5VtkQC6yjiYH4DDrsZv19HTU0jZ8xpCS3T3prD+/sLE6xl/E364PgjfTdHDe7kC46h0Ww3nO4AFJlaNhhw2/O66aisR2l+jE4LBY3TsQwkT0q/NEPlSKbvU38DIOfJT+GpOoq3PNBkwFca54bi0wXaSTks6AZsaC4TyuDFmd2P8ujJX7oX3cxG0Pzg1+M3Bc41z6w6PLNryW0vwWO145h+HGNRJ/X9ORidVg7ldVOEkVM1CzleE36/AV3YA02rPsLfCurxGAKvzS2zcplyeA5Z3flMKXCjdH4avYqSs7cOKXLXuwsx5A6gt7jQDL5QJxyd0YtPadhmH8U8tR1nQxkDB6uJlRM2eI4eyjkaCowBliyrx2qdF7FMUKLahLEOjDOR1hBSv/4UarBD2NAOh+72fAyNUyk9axvK6gTAS6AT3MUXX8zChQspLS2VQPZjJjwAP3DgAF/9VU1Kn7vzzjvZt6+N8tMseL3Z9N/3CbQY2UBGw0j6FHycjGVt9EhyLqfb9CK8P0qs+394mWK1k7dWHierR3FYf4w1gzXEAB7PifO3r9fC9veH5iqfaOndJn1wPN4yHQiHDzU80oAx1rqGs97wmmhn2THc1XWhec1zP0KzW8Gvw7x/Ds7TdwDg7c9CefXozG68Pdm4ZpxC96ffpue9RVQ1zsBvceItP467up6sd1ZwyG7FM7MOlIa+swB994lG9sHAJBXZfz8fv82OpnRY31lO32XPJlw+55mLAmlrshz4c3tRZheay4zJpw+0Tz6qY5o7C51Pj1/nw57XjTvLTlZ3PoXHpzOjxIFH56U1rxNNafRZ+/FkO5neV0JPVh+b8ptwmlwoIFvpyfIZyPWZOWzpwerXU2XP45R9p9KS187+efto0XvR0HCYArlyDE4zXrMrcB8ZzEFcNOcYmsuMzmFF67ehs2cFAnynhWNuha/fFnOQg1hmd08HTtzEdm6voqw0ftqn8RpCOVzwmhvtRPIKhXd6I76iTtxzDtP93iKcDWUR6/iN/0peffVV7r//DQBWr16NXq/ny1/+MllZqbx2FCLSddddR11dHZqmcdppp2H7eWaut0w1JUn1+vm4B9GjFUAPN1hOtelFrFrkaLG2TZ/lIO+0PZwdNd3r1bH1jVPQ6/zMXdBASWkfyq/FXHcqWSsAKvUmGOVsFRIcp2G0aoRjBauZHFwifLlZZoVC4TN6UDo/BpcZDQ2vwc2RU9/Ha3HhyO/BWF+BZdupoRqLwy6N5mXvYp3RiL12Op7OPLLn1mLIOdFRzL54T6hRgfJrGPMD7Wn9Hl2ghtPgI2vmMVqmBZoAGHLsALTM3Yve7EafbUczeDHYnHS+GRi+teCMbZindOAbsOBsKqVv9ykJt7XGbkNnH0xL4zNgefc0fFNa8U5tQdP5AyMAOS249sxFc1qYbdSB0sOABQaG9nqtmtoHegL/AAasELwvlAS22+g3MK1rsM1ld0nosxWdU1nYMAeFwml04TJ6cBid2M1OlnmmU9JTiNFvCLQ3Pp7P7H2zA/tO89NedQS9x4jRacFst+HX+VCawuS0ovfGH1M+uHcO+5IN/6nQWVwYC3oj5k+pryHZWZVsWPKR5gaPZ6xqiYNqc9opWfVe6O+r7qyIaBZx9OhR7rnnHnp6ekLT3nrrLebPn59WJzMhwlVWVlJZWZnx9Y51UxJp2pHcSPNFR+/jVHIup1qbnEqQjM6Hbc4RNL0vYgCpXR9UcbyhkOja4W3vzB78v8h8+uNdSxyLBMcJpHLARhoEpyq69jd6WvSy1VkenLm9HNPcYPCijF40lwl/lgPXvP2g94HSQGno7FaU3hcKcgE80xvx5faieQ24LE40ower2Y2mQVZ1I9rMQKov5dfQBtvuanofyq8FhqjMHRicD3qLB+U14GotxNVahPLq8fbZ0EyeQG9rnR+fX0/v7rkUrX0HIDDcqdOMMa8fAL3NiW32UXY1BYKO2c5i/A4zymcAFOj86K1OjmS3BcqjKcq0wOltaJrKtPYpDBR00jazFoB8g5fc1im424sxOePX8KUymlK6DIAHOB5nvqZ0lBwZWceleBlFanw2nAv34D7lQMR8v8tI74c1gJbWgAVj+SAb7k003Yd0sHb8tZ5bgVtxuVz85S9/CaU+c7vd/O1vf+OZZ57hU5/6FFVVVbjdbnp7e1m0aBFTp05Nu4xCnAwyGYAHA+2PU7CcipHWSMernY13bx06XaGzOtEZfBSc9T4+uxVTYQ9+jx5vfxbePhvHG4piritWUBxvkLPx7rciwfGg0WoekQnBwGbPnL24Zx/GunUlOkcWvuJ29uT2oYwelN6HvicP5/x9eHuz6c4ewGe3olwm/F4DymNAZw6kMet/61Q8nYEk+TqLC73VifLrUB4DyqdHM3rRdH40YyBXoXKb8LuN+J2mwWCUQBtcpRHMu4lOoRm8oDR26ztRCpTS8PsDy5RPL+S1d6rDtiqyZi24//t2z0UzePE7LIHhYTVF0dmB2jvNbuUTS46DBl5jPV6zE82vQ2mBYNjgMmO229D8OjSloRtsWqA0P022AXxGT+j7uqc10j2tkeK6GVR8uCTuPg83WnmUx4JCoWx23NVHQoGxNpCFec889D156HvyyB9cNl/Fr5lOVSZyGKdUcxFHqtdo8Hgqzc+PWi6gu7sbp9OJ3W5n8+bNGI1GPB4PSileeeUV3nzzTbZu3Yrb7ebaa6/l4osvTqtcQojUZCrQPtv4m4ysZzJJFEDHG4AjtRplxdTLNp34y6/R8/5CvP3Z+F3GUMaepYy8DXF0Jo2xdtIGx6P9CjbTGR7Cgy5fbg8NOZ2YirtA82Mq7kIpHabBV+COs7fic5rwdObh6c5D2a0ovw5TURdWnaKt38D+dxbgdscLcrxg6Aj9L6GUvIMdG33hy2qAh6XeyBGnarxRNao+wBPYLxdGLQtQQTZlJHhVGDwTm2cNnffstJgf8et8+PWBwh6xG4Z0Wgk/QiYU/tw+zCg0rwFtcDhVl9fA4RTbM0/0wFhpfvx5vfiz+wdzI9tRFhfK4sSX24dfaXh7bXhrp+PpycHZUHaibXJUb+1MGUkuzuF8LtF1q/TeQKaSLDtX/mYBTU1NuOrrOX78OD09PfzLv7xMXl4eVqsVi8WCzWbD4/FgNBqZOnUqS5Yswel0Mn/+fHw+35B8v0KIiecNz5fTWv7AgQPc8cvUOk8GTeYAPL37skbbi2ehGXxYKxuxzTmKsagbd9tgU7oEY9EMZ7CP8TTpg+O5vnwKvfFHyIseOjm8Q1GyzkXDCYCrpvYNeRWfKKgKlk2zeMhfvhOFwjKli0Rjvrz/zizaW2MMj9pghZ1lEZNSHQEn6bZGnymG0ftxkDoDwYIl+7SGFhoOcyI7oB/AkNdL7pL9qGBuYk2hvAb8LhPKpwMNlE+H8hgHm7X40WfbsZS34HeZBnNQWvHZrfi6svG7THj7bPgdJ4bxTJq3NwMStXWDzDdbgsgk9gfN3RgLezCXdrB0g4m6ujqKioooLS3l4MGDTJ06lQ0bNjB9+nQKCwuxWCxJ1i6EEEOlG4DHMxmCbJ890Axx4FA1tjlHUZ703jSGZxpKpZNgrHklvtEfEXLSB8eVfisFvvjzZ5kVuGyB/xoUs8g6EdgNTk80EEW0YPCbaAS0ghlNvFWxD3N/DnqvgQFbH8rgQ/PpwKen32nE3ZnPvn4fOp3CbHGzYFE9hhjnmKc3KzAMY5YrNG1tWw06lyW14H2Ug9po8YLcHN/Er3nNlBEHnJrCVNQNgKulCHd7AZreH2q24ncb0XQ+dFYnGgqldPj6s+h4bRW+gYnTk3w4TSuG24nncG4bppIOcvP60GfbKV6Vj/3oUSorK1mwYDELFixg7ty52GzjP8KkEELEMpmCbJ0p0EwxZ+EBvL3ZLG07EeyaTB7OOHs/3d02jh8rZIYvF+XT47NbmeWyhSoqr3BVcUA/kFLTufBnQ078+tCMmfTBcTKJsjrEG+YxXKwgOF5gHKwxrjttH16Th8KuQtAUVc1l6L3GQMYBvQ+31UFPeTNTzE50Sodf58M5GBib+7JxZQ2APlAmq9lLXlM5PqOb7mmBLlwz9BqGDDXr+LgErImMRaCotwXagKN0oPnJX74bY0EPfnfgh5LfYUF59Ph9epTHSOc/TsM0pQNjfi/Wqka0wTbdOmPgl2DX20twNUUPSjx8Ec0fdJYMtfNS5OY5yLI5sdlcmMxeFugUXq8Op8NEw0APfT1ZuFzGtF+vBY6Zwjy1jY3/Voj37e0sXryY6uolTJ8+HZPJxCmnnCK1wUKIj51MBdmJnG38Dc3PnIt1ejMFqz7AcaycU/fMAU1hqz6CxephqrWbqWXdEZ878D/nn/j/wWdvKvf/8Of0aYx+JYemlBrdqsRR0tXVxcsvv8yKFSvIzZ1Yr8s/+ugjPvzwQ7xeLyaTCbvdHjH/9NNPZ+7cuRHT/H4/PT09dHR0oJSipKQEm82G0TjyzlETQVtbGyUlJckXPEk99thjAJxyyimUlJSwZcsW/P74P3+zsrJQSuF0OtHr9eh0OjweD9nZ2ZSVlbF06dKY54bT6aS7u5uenh7a2tro6+tj/fr1aZV1pMfK5/NRV1fHgQMHcLlc5Ofnk5OTg8ViQa/X43a7cTgctLe309PTQ15eHhdeeCEGQ+q/1e12O1u3bsVutzNjxgyqq6vJyRndvJcTzcf9mppM5FhNHnKskuvu7sZisYQqH1588UXa209UqLS0tISeW/n5+VxwwQUYDAby8/PR6UbeJKK3t5d3332XdevWUVCQqBHq8E3a4Nhut/PCCy/g8yVoUyGEEEIIIU4qer2eCy+8cNQGW5q0wTEEAmSXy5V8QSGEEEIIcVIwm82jOgrppA6OhRBCCCGEyKTRz4chhBBCCCHEJCHBsRBCCCGEEIMkOBZCCCGEEGKQBMdCCCGEEEIMmlCDgARz1x04cICbb745NL21tZWnn36ahoYG8vPzWb9+PfPmzQvN/+CDD9i0aRM9PT1MnTqVyy67jOnTpwNQX1/PY489Rm5uLl/84hfJzs4e8+2azOIdk9raWp5//nlaWlrIzc3lnHPOYfny5aH56RyTrKwsvve973HFFVewcOFCIJD3+fvf/z7nn38+q1evDq33Bz/4Aeeeey6rVq0aoz0wedntdl5++WX27NlDWVkZX/ziFwFobGzkf/7nf2hqaiI3N5d169Zx2mmnAdDf38/vf/97enp6uOaaa6iqquJ3v/sdxcXFXHrppaF1P/zwwxgMBq6++urQtD/+8Y8opbjqqqvGdDs/LgYGBnjqqac4cOAARqORZcuWsWHDBjRN489//jP79u3j/PPP54wzzhjvon4sZOr66uzs5Mc//nHM7/j+979/0uS6Hytj8czKzs7m/fff569//euQ76+oqOCWW24Z/Q09yUy0+G/CBMdPPfUU27Ztw2KxRNwMfD4ff/jDH5g2bRpf+9rX2LNnD4899hhf//rXKSoqorm5mb/+9a9ceumlzJgxgzfffJNHHnmEf/mXf8FoNPL888/zmc98hqNHj7J161bOP//8BKUQ4eIdk/b2dn73u9+xbt06Pve5z1FXV8dTTz1FQUEBs2fPHtYxmTZtGg0NDaHguKmpCYfDQV1dXSg47unpoa+vj8rKynHZH5OJx+Ph17/+NUajkcsvv5yysjIAvF4vjzzyCKeccgpXXHEFhw8f5sknn6SsrIzy8nK2bt3KnDlzqK6u5vnnn+fGG2+koqKCgwcPhtbt9/upq6sbMmhHQ0NDxA8ZkVlPP/00fX193HTTTfT39/OnP/2JgoICpkyZEpr+0EMPsXz5cgmoRlkmr6+gW265hfz8/IjvkeOYnrF8ZgHk5eXx1a9+NaIMer1+TLf5ZDAR478J06wiOzubm2++echoXh0dHbS1tXHRRRdRUlLC2rVryc/PDz2sd+7cyYwZM1i5ciWlpaVcfPHFOBwOamtrgcBNLDc3l+zsbLxe75hv12QW75js2LGD8vJy1q5dS3FxMcuXL2f27Nns378fGN4xqaiooKGhIfQdhw8fpqioiLq6utC0hoYGTCYTU6dOHe1Nn/S2bt3KwMAAX/7yl5k9ezY2W2C4zbq6OhwOBxdffDGlpaWsXr2aqqoqdu7cCRAahS8vLy/i2DQ1NYUG3Dl+/DiapuHz+WhtbQXA4XDQ2dkpP1xGidvtZu/evWzYsIHy8nJqampYtWoVO3bswOPxYLVaycnJQafTycBIYyCT11eQzWYjJycn4p9Iz1g+swA0TRtyzEYz9+7JaiLGfxMmOA7WHkbzeDxA5C9oo9EYmt7S0sKUKVNC80wmE0VFRbS1tQFw7rnn8qtf/YrNmzezcuXK0dyEk068YzJv3jwuueSSiGkGgyH0i3k4x6SiooLGxsbQZ2pra1m+fDlutzv0ucbGRqZNm5aR4SdPdjt37uTMM8/EZDJFTG9paaGoqCjieiorKwvt41WrVvHGG2/wi1/8gk9+8pNA4Nh4PB5aWlqAwLGpqqqioqIi9OOlsbERnU5HeXn5WGzex057ezs+ny/ih2HwuNXU1DAwMMDdd9/N4sWLQ0O6itGTyetLZM5YPrNE5kzE+G/CNKuIp6ysjKKiIl5//XXOO+88PvroI9ra2pg/fz4ALpdryMPAarXidDoBmD9/PnfeeSc6nU6CqgyJPol7eno4dOhQ6GY/nGNSWVmJw+Ggvb2dwsJC6urqOO+886irq6O2tpaSkhIaGhqkZjIFPp+P5uZmampquPfee+nv76empoZLLrkk7rFpbm4GoKioiG9/+9v4/f5QswmbzUZRURENDQ2Ul5dz+PBhZs+ejd/vp7a2lpUrV4bmRTe1EJkRHAk0/NhZrVZcLhd6vZ7rr78et9s9JFgTmZfp60uMvtF4ZonRN57x34S/OnU6HVdccQUPPPAAmzdvRinFJZdcQlFRERBo/xhL+MB/chMaPX6/n7/85S/MmzePioqK0LRYEh2TvLw8cnNzaWxsxOFwoNfrKS8vZ+bMmdTV1bFy5UoaGxtZsWLF6G3MScJut+P3+9m7dy+XXHIJOp2Op556iueeey7uq9rwYxPrRhKs2ff5fBw5coQLL7wQr9fLli1bgEDNcfD4i8xL5ZqSwHhsjMb1BfCzn/0MTdNCf19yySURHcZEZmTqmQWBIPu73/1uxLSvfe1rlJSUZLDEH1/jGf9N+KjR6XTypz/9iTVr1rBkyRKOHTvG888/T1VVFdOnT0fTNGKNgC2/7sbG3/72N7q6uiKyFgz3mFRUVHDs2DG6urqYNWsWmqYxa9YstmzZQldXFwMDA1JznIJgm9PLL788dPNfv349TzzxBGvXro15bJJ1IqmoqGD79u00NjZiNBqZOnUqSilcLhft7e00NDRwwQUXZH5jBEAoaPL7/RHXkdznxt5oXF8AX/rSl8jNzQ39LZmVRkcmn1k5OTlcf/31EdMKCgoyU1AxrvHfhL+z7ty5E03TWL9+PeXl5axcuZIFCxbw5ptvAmA2m3E4HBGfcTgcWK3W8Sjux8q7777Ltm3b+MIXvhCxv4d7TCorK2lsbKS2tpbZs2cDUF5ejtvtZvfu3aHaZZGYzWZD07SIdlqlpaV4vV4MBkPMY5OsnWplZSXNzc0cPHgw9MNFp9MxY8YM9uzZQ1dXl9QcjyKz2QwQcexSOW4i80bj+gLIz8+nuLg49E+ObeZl+pml0+kijllxcbFkq8ig8Yz/JnxwHLzhhDOZTKGeh1OmTInoyOV2u+no6IhopC0yr7a2lmeffZarrrqK0tLSiHnDPSYVFRW0t7dz7NgxZs2aBRAKwLZt2ya1xikyGo0UFxdz9OjR0LTOzk4MBgPl5eV0dHSE2mRBIG1esmNTVlaGpmns3LkzdGwAZs2axfbt20PtksXoKC4uRqfTRVxXqRw3kXmjcX2J0TcazywxusYz/pvwwXFNTQ3t7e1s3ryZjo4O9uzZw/bt20MNspcsWUJDQwNvvvkmbW1tPPfcc9hsNqqrq8e34Cexjo4OHn30UdasWUNZWRl9fX2hfzD8YzJ9+nQGBgYwm80UFxeHps+cOZOWlhapmUzDmjVr2LRpEwcPHqShoYEXXniB0047jZkzZ5KVlcUzzzxDa2srb7/9NkePHmXJkiUJ1xd88Le2toZq9UGOzVgxm83Mnz+fF154gcbGRg4ePMjbb7/N0qVLx7toH0uZvr4gMMhL+L20r68vbptKkZ7RemYppYYcs4GBgTHYoo+H8Yz/Jnyb45KSEq699lpeeuklXn75ZXJycli3bh3Lli0DAjVal19+OS+99BLPP/88ZWVlXHvttfJqYxR98MEH2O12Xn31VV599dWIeT/84Q+HfUxMJhNTpkwZ0rM4WFMpNcepW7FiBb29vTzxxBMALFiwgA0bNmAwGLj22mt5+umn+fnPf05eXh6XX375kJqUWCoqKujv76ewsDA0raysDKvVKsdmDFx22WU8+eST3H///RiNRpYvXy4dVMfJaFxf991335Bp//Zv/yb5jjNgtJ5ZPT09/OAHP4iYVlJSwu23357xbfg4Gs/4T1OxWjMLIYQQQgjxMTThm1UIIYQQQggxViQ4FkIIIYQQYpAEx0IIIYQQQgyS4FgIIYQQQohBEhwLIYQQQggxSIJjIYQQQgghBklwLIQQQgghxCAJjoUQQgghhBgkwbEQQgghhBCDJDgWQgghhBBikATHQgghhBBCDJLgWAghhBBCiEESHAshhBBCCDFIgmMhhBBCCCEGSXAshBBCCCHEIAmOhRBCCCGEGCTBsRBCCCGEEIMkOBZCCCGEEGKQBMdCCCGEEEIMkuBYCCGEEEKIQRIcCyGEEEIIMUiCYyGEEEIIIQZJcCyEEEIIIcQgCY6FEEIIIYQYJMGxEEIIIYQQgyQ4FkIIIYQQYpAEx0IIIYQQQgyS4FgIIYQQQohBEhwLIYQQQggxyDDeBRDDZ7fbcblc410MIYQQQowhs9lMVlbWeBfjpCXB8SRlt9t56qlnMZul8l8IIYT4ONHr9Vx44YUSII8SCY4nKZfLhdms48FftdB03D3exUlqri9/vIuQskq/dbyLkJIKoxrvIqRsarF9vIuQspLirvEuQkoKSydHOQHySrrHuwgps5VNjv1qLO8Z7yKkrmLyXP+OKu94FyGpgb5Sdm+/EpfLJcHxKJHgeJJrOu7maP3ED44Lvf7xLkLKCnzjXYLUuMzjXYI0uCbJTgWManI0VbKaBsa7CCnLzpo8gVyup2O8i5ASszY5ygmgLH3jXYSUGfMnfnAsRp+8kxdCCCGEEGKQBMdCCCGEEEIMkuBYCCGEEEKIQRIcCyGEEEIIMUiCYyGEEEIIIQZJcCyEEEIIIcQgCY6FEEIIIYQYJMGxEEIIIYQQgyQ4FkIIIYQQYpAEx0IIIYQQQgyS4FgIIYQQQohBhvEugBiZsnLTeBchJSW+yfM7LMc/3iVIjdk43iVIQ7F+vEuQMk+xebyLkBJHvm28i5CyfptnvIuQMmWcHDcAo5pEj29nzniXIGWObu94FyGpgb7S8S7CSW8SXV0inNlsRq/Xc/1NU8a7KEKkIG+8C5Cy7klS1m6qx7sIYjz5xrsAaTgy3gVIw5HxLkBq9Ho9ZvPk+CE/GWlKKTXehRDDY7fbcblc410MIYQQQowhs9lMVlbWeBfjpCXBsRBCCCGEEIMmT0NQIYQQQgghRpkEx0IIIYQQQgyS4HgS6ujoYOPGjdhsNoqKirj99tvx+ydHD+vxdujQITRNi/hnsVhiLnvWWWcNWfaJJ54Y4xJPLk1NTdx1112cccYZQ+bt3LmT66+/nltuuWUcSjY5JNp/+/btY+PGjRQXF/OjH/0o7jo8Hg86nW7Iudvc3DyaRZ906uvr+fSnP01hYSHTpk3jG9/4RqgPx+bNm1m1ahXZ2dnU1NTwu9/9bpxLO7HE23dr164dct5pmsbs2bN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-      "text/plain": [
-       "<Figure size 700x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "id": "658ac441-3d9d-46ab-9e5a-d4c5a159679b",
+      "cell_type": "markdown",
+      "source": "The wave model data is stored on a **reduced_ll** grid and can be loaded as in the following example.\n",
+      "metadata": {}
+    },
+    {
+      "id": "d0fa5bad-4af2-4cb9-8f76-4814295c41e4",
+      "cell_type": "code",
+      "source": "ds_wave = load_hplp_data(leveltype=\"wave\", gridtype=\"reduced_ll\")\nds_wave",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/hplp_wave.grib\n",
+          "output_type": "stream"
+        },
+        {
+          "execution_count": 13,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "<xarray.Dataset> Size: 958MB\nDimensions:  (time: 21, value: 315258)\nCoordinates:\n    lat      (value) float64 3MB ...\n    lon      (value) float64 3MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables: (12/18)\n    cdww     (time, value) float64 53MB ...\n    dwi      (time, value) float64 53MB ...\n    hmax     (time, value) float64 53MB ...\n    mp1      (time, value) float64 53MB ...\n    mp2      (time, value) float64 53MB ...\n    mwd      (time, value) float64 53MB ...\n    ...       ...\n    ust      (time, value) float64 53MB ...\n    vst      (time, value) float64 53MB ...\n    wdw      (time, value) float64 53MB ...\n    wind     (time, value) float64 53MB ...\n    wmb      (time, value) float64 53MB ...\n    wstar    (time, value) float64 53MB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...",
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2;\n}\n\n.xr-array-data,\n.xr-array-in:checked ~ .xr-array-preview {\n  display: none;\n}\n\n.xr-array-in:checked ~ .xr-array-data,\n.xr-array-preview {\n  display: inline-block;\n}\n\n.xr-dim-list {\n  display: inline-block !important;\n  list-style: none;\n  padding: 0 !important;\n  margin: 0;\n}\n\n.xr-dim-list li {\n  display: inline-block;\n  padding: 0;\n  margin: 0;\n}\n\n.xr-dim-list:before {\n  content: '(';\n}\n\n.xr-dim-list:after {\n  content: ')';\n}\n\n.xr-dim-list li:not(:last-child):after {\n  content: ',';\n  padding-right: 5px;\n}\n\n.xr-has-index {\n  font-weight: bold;\n}\n\n.xr-var-list,\n.xr-var-item {\n  display: contents;\n}\n\n.xr-var-item > div,\n.xr-var-item label,\n.xr-var-item > .xr-var-name span {\n  background-color: var(--xr-background-color-row-even);\n  margin-bottom: 0;\n}\n\n.xr-var-item > .xr-var-name:hover span {\n  padding-right: 5px;\n}\n\n.xr-var-list > li:nth-child(odd) > div,\n.xr-var-list > li:nth-child(odd) > label,\n.xr-var-list > li:nth-child(odd) > .xr-var-name span {\n  background-color: var(--xr-background-color-row-odd);\n}\n\n.xr-var-name {\n  grid-column: 1;\n}\n\n.xr-var-dims {\n  grid-column: 2;\n}\n\n.xr-var-dtype {\n  grid-column: 3;\n  text-align: right;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-preview {\n  grid-column: 4;\n}\n\n.xr-index-preview {\n  grid-column: 2 / 5;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-name,\n.xr-var-dims,\n.xr-var-dtype,\n.xr-preview,\n.xr-attrs dt {\n  white-space: nowrap;\n  overflow: hidden;\n  text-overflow: ellipsis;\n  padding-right: 10px;\n}\n\n.xr-var-name:hover,\n.xr-var-dims:hover,\n.xr-var-dtype:hover,\n.xr-attrs dt:hover {\n  overflow: visible;\n  width: auto;\n  z-index: 1;\n}\n\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  display: none;\n  background-color: var(--xr-background-color) !important;\n  padding-bottom: 5px !important;\n}\n\n.xr-var-attrs-in:checked ~ .xr-var-attrs,\n.xr-var-data-in:checked ~ .xr-var-data,\n.xr-index-data-in:checked ~ .xr-index-data {\n  display: block;\n}\n\n.xr-var-data > table {\n  float: right;\n}\n\n.xr-var-name span,\n.xr-var-data,\n.xr-index-name div,\n.xr-index-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2,\n.xr-no-icon {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 958MB\nDimensions:  (time: 21, value: 315258)\nCoordinates:\n    lat      (value) float64 3MB ...\n    lon      (value) float64 3MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables: (12/18)\n    cdww     (time, value) float64 53MB ...\n    dwi      (time, value) float64 53MB ...\n    hmax     (time, value) float64 53MB ...\n    mp1      (time, value) float64 53MB ...\n    mp2      (time, value) float64 53MB ...\n    mwd      (time, value) float64 53MB ...\n    ...       ...\n    ust      (time, value) float64 53MB ...\n    vst      (time, value) float64 53MB ...\n    wdw      (time, value) float64 53MB ...\n    wind     (time, value) float64 53MB ...\n    wmb      (time, value) float64 53MB ...\n    wstar    (time, value) float64 53MB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8105cfed-3f2d-4e7d-a850-8be5c6b5b2d6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8105cfed-3f2d-4e7d-a850-8be5c6b5b2d6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span>value</span>: 315258</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f257bf13-6edd-4102-b6d5-c3b2b9693858' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f257bf13-6edd-4102-b6d5-c3b2b9693858' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ef55012-4ee5-4c93-82e1-a61330efe517' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ef55012-4ee5-4c93-82e1-a61330efe517' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37c24a6c-66a5-4b7f-9d30-2efce3db7d44' class='xr-var-data-in' type='checkbox'><label for='data-37c24a6c-66a5-4b7f-9d30-2efce3db7d44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[315258 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0e7c571a-105d-49a3-9eee-399e47243e8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e7c571a-105d-49a3-9eee-399e47243e8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc7d8872-ab86-4404-b05a-e261e7bfd9e9' class='xr-var-data-in' type='checkbox'><label for='data-fc7d8872-ab86-4404-b05a-e261e7bfd9e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[315258 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-c4a9c41b-ea28-45da-920b-d7d4f8405df6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4a9c41b-ea28-45da-920b-d7d4f8405df6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2edcbc56-7d74-4025-b20d-96d7ec1478c7' class='xr-var-data-in' type='checkbox'><label for='data-2edcbc56-7d74-4025-b20d-96d7ec1478c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4f309929-5349-44c4-a14e-52badffde462' class='xr-section-summary-in' type='checkbox'  ><label for='section-4f309929-5349-44c4-a14e-52badffde462' class='xr-section-summary' >Data variables: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cdww</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4029d5cb-c983-4a4f-93bb-a9858463acb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4029d5cb-c983-4a4f-93bb-a9858463acb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93bb45ef-2795-48c8-868e-55089693207c' class='xr-var-data-in' type='checkbox'><label for='data-93bb45ef-2795-48c8-868e-55089693207c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140233</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cdww</dd><dt><span>GRIB_units :</span></dt><dd>dimensionless</dd><dt><span>GRIB_name :</span></dt><dd>Coefficient of drag with waves</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cdww</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>dimensionless</dd><dt><span>long_name :</span></dt><dd>Coefficient of drag with waves</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dwi</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe22f2d5-b77b-424e-8f20-696765dca6a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe22f2d5-b77b-424e-8f20-696765dca6a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8850d462-00a8-4440-8192-49111ae82f4c' class='xr-var-data-in' type='checkbox'><label for='data-8850d462-00a8-4440-8192-49111ae82f4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140249</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>dwi</dd><dt><span>GRIB_units :</span></dt><dd>degrees</dd><dt><span>GRIB_name :</span></dt><dd>10 metre wind direction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dwi</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>10 metre wind direction</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hmax</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-87ca1bb3-a2fb-46c1-bbd2-dc3d82e8a70a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87ca1bb3-a2fb-46c1-bbd2-dc3d82e8a70a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15b7c019-bdca-435d-8457-f9617b665425' class='xr-var-data-in' type='checkbox'><label for='data-15b7c019-bdca-435d-8457-f9617b665425' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140218</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>hmax</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Envelop-maximum individual wave height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hmax</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Envelop-maximum individual wave height</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mp1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d0d4caa8-c91c-4215-8d66-45c5111bce63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0d4caa8-c91c-4215-8d66-45c5111bce63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44a27e2e-3fd8-4b3a-8385-ddad347ca406' class='xr-var-data-in' type='checkbox'><label for='data-44a27e2e-3fd8-4b3a-8385-ddad347ca406' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140220</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mp1</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave period based on first moment</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mp1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean wave period based on first moment</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mp2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b3551d0b-2e45-46ab-bf26-06df25ad9063' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3551d0b-2e45-46ab-bf26-06df25ad9063' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83f9ae82-e126-4691-ac3e-7c420da750d0' class='xr-var-data-in' type='checkbox'><label for='data-83f9ae82-e126-4691-ac3e-7c420da750d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140221</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mp2</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean zero-crossing wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mp2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean zero-crossing wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mwd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1c8095f3-236c-4f33-9ae5-8f860d64f9e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c8095f3-236c-4f33-9ae5-8f860d64f9e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06203a28-54d6-4da7-8478-a0a766c45f3e' class='xr-var-data-in' type='checkbox'><label for='data-06203a28-54d6-4da7-8478-a0a766c45f3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140230</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mwd</dd><dt><span>GRIB_units :</span></dt><dd>Degree true</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave direction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mwd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Degree true</dd><dt><span>long_name :</span></dt><dd>Mean wave direction</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mwp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-97d2221c-6f0a-4609-ab3c-d54dc758e16d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97d2221c-6f0a-4609-ab3c-d54dc758e16d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0a5af34-4115-490d-9847-14e8d5f035d8' class='xr-var-data-in' type='checkbox'><label for='data-b0a5af34-4115-490d-9847-14e8d5f035d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140232</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mwp</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mwp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phioc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1310c403-b780-4dda-b2c8-abb424cdfcd5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1310c403-b780-4dda-b2c8-abb424cdfcd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee7253d5-1f1d-483f-a779-47d6c10cc488' class='xr-var-data-in' type='checkbox'><label for='data-ee7253d5-1f1d-483f-a779-47d6c10cc488' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140212</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>phioc</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Normalized energy flux into ocean</dd><dt><span>GRIB_cfVarName :</span></dt><dd>phioc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Normalized energy flux into ocean</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pp1d</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9d2a128-5503-4312-8d95-590acc4c6af8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d2a128-5503-4312-8d95-590acc4c6af8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4e84d0c-3adb-42d5-ba74-9b81ebf1c409' class='xr-var-data-in' type='checkbox'><label for='data-e4e84d0c-3adb-42d5-ba74-9b81ebf1c409' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140231</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>pp1d</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Peak wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>pp1d</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Peak wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rhoao</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-61e96263-b167-46d3-a64f-da78076a484d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61e96263-b167-46d3-a64f-da78076a484d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-994e354e-46ec-4195-b9f2-6cbc0b15bd15' class='xr-var-data-in' type='checkbox'><label for='data-994e354e-46ec-4195-b9f2-6cbc0b15bd15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140209</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>rhoao</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Air density over the oceans</dd><dt><span>GRIB_cfVarName :</span></dt><dd>rhoao</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>kg m**-3</dd><dt><span>long_name :</span></dt><dd>Air density over the oceans</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swh</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e9680dc2-c9bb-4371-aa64-457a32b5767a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9680dc2-c9bb-4371-aa64-457a32b5767a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc50ad2a-4967-4a3f-8f4d-0b036310cc5a' class='xr-var-data-in' type='checkbox'><label for='data-cc50ad2a-4967-4a3f-8f4d-0b036310cc5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140229</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swh</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Significant height of combined wind waves and swell</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swh</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Significant height of combined wind waves and swell</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tauoc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d2fd8cba-5d81-4a60-9c6d-634c47ebb317' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2fd8cba-5d81-4a60-9c6d-634c47ebb317' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6a28518-f08e-41f3-a34f-071776d99005' class='xr-var-data-in' type='checkbox'><label for='data-d6a28518-f08e-41f3-a34f-071776d99005' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140214</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tauoc</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Normalized stress into ocean</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tauoc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Normalized stress into ocean</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ust</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c4337b14-88f5-4432-a55a-892729cbdd3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4337b14-88f5-4432-a55a-892729cbdd3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04762794-8824-4d71-9a94-875b1772234f' class='xr-var-data-in' type='checkbox'><label for='data-04762794-8824-4d71-9a94-875b1772234f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140215</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ust</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>U-component surface stokes drift</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ust</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>U-component surface stokes drift</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vst</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b96a8188-2481-4c84-97af-4f9dc395a103' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b96a8188-2481-4c84-97af-4f9dc395a103' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46fa62aa-a8cf-411f-8e56-945fcc05a28d' class='xr-var-data-in' type='checkbox'><label for='data-46fa62aa-a8cf-411f-8e56-945fcc05a28d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140216</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vst</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>V-component surface stokes drift</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vst</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>V-component surface stokes drift</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wdw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ff5cd1a6-cf86-4373-b56b-0731b174a775' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff5cd1a6-cf86-4373-b56b-0731b174a775' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe4b550c-2336-466e-8ded-0b9a164418ca' class='xr-var-data-in' type='checkbox'><label for='data-fe4b550c-2336-466e-8ded-0b9a164418ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140222</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wdw</dd><dt><span>GRIB_units :</span></dt><dd>radians</dd><dt><span>GRIB_name :</span></dt><dd>Wave spectral directional width</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wdw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>radians</dd><dt><span>long_name :</span></dt><dd>Wave spectral directional width</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-281124df-5411-44f5-b63a-06c5563f56ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-281124df-5411-44f5-b63a-06c5563f56ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1410bd4-43b7-433c-987f-cfce33e9c483' class='xr-var-data-in' type='checkbox'><label for='data-f1410bd4-43b7-433c-987f-cfce33e9c483' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140245</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wind</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre wind speed</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wind</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre wind speed</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wmb</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cc050fd1-bf53-44bc-ad2d-b65a4925c14e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc050fd1-bf53-44bc-ad2d-b65a4925c14e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e784c9e6-4b38-49dd-b07a-6699baf553c5' class='xr-var-data-in' type='checkbox'><label for='data-e784c9e6-4b38-49dd-b07a-6699baf553c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140219</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wmb</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Model bathymetry</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wmb</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Model bathymetry</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wstar</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-68895f68-4f1f-440a-9d3e-409b3e188601' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68895f68-4f1f-440a-9d3e-409b3e188601' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37b8255-3e8d-40a9-8d2b-a82db87af10e' class='xr-var-data-in' type='checkbox'><label for='data-d37b8255-3e8d-40a9-8d2b-a82db87af10e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140208</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wstar</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Free convective velocity over the oceans</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wstar</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Free convective velocity over the oceans</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f7d85a4-fc6a-4813-831c-4a7bf3c49722' class='xr-section-summary-in' type='checkbox'  ><label for='section-7f7d85a4-fc6a-4813-831c-4a7bf3c49722' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8401f7d-2872-4a5b-bf08-6e38359f9c32' class='xr-index-data-in' type='checkbox'/><label for='index-b8401f7d-2872-4a5b-bf08-6e38359f9c32' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c007c16e-5b23-4c25-8f3a-bf89112ccbbc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c007c16e-5b23-4c25-8f3a-bf89112ccbbc' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 13
+    },
+    {
+      "id": "a936cb8e-b821-46a2-8fba-e1619e12f097",
+      "cell_type": "markdown",
+      "source": "The data can be visualized as demonstrated below. For example, the 10 metre wind speed on **2020-07-31 at 00:00:00 UTC** is plotted.",
+      "metadata": {}
+    },
+    {
+      "id": "44680327-cd31-4720-b075-55d1f031489a",
+      "cell_type": "code",
+      "source": "earthkit.plots.quickmap.plot(\n    ds_wave[\"wind\"].sel(time=\"2020-07-31T00:00:00\"),\n    x=\"lon\", y=\"lat\",\n);",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 700x600 with 2 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 14
+    },
+    {
+      "id": "ee03b214-8009-4d44-83e2-0783892050a1",
+      "cell_type": "code",
+      "source": "",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
     }
-   ],
-   "source": [
-    "earthkit.plots.quickmap.plot(\n",
-    "    ds_wave[\"wind\"].sel(time=\"2020-07-31T00:00:00\"),\n",
-    "    x=\"lon\", y=\"lat\",\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ee03b214-8009-4d44-83e2-0783892050a1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  ]
+}
\ No newline at end of file
diff --git a/04-example-datasets/02-OpenIFS.ipynb b/04-example-datasets/02-OpenIFS.ipynb
index 36cc2ad..97e66f6 100644
--- a/04-example-datasets/02-OpenIFS.ipynb
+++ b/04-example-datasets/02-OpenIFS.ipynb
@@ -1,6433 +1,265 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
-   "metadata": {},
-   "source": [
-    "# OpenIFS 43r3 data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
-   "metadata": {},
-   "source": [
-    "> [!NOTE]\n",
-    "> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n",
-    "\n",
-    "> [!CAUTION]\n",
-    "> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "463bf2c0-ac3f-4786-9687-362393802dce",
-   "metadata": {},
-   "source": [
-    "This notebook is a comprehensive guide to accessing NetDCF files for an OpenIFS simulation. It includes details about the available parameters, levels, and steps, along with instructions for using the `load_OpenIFS_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## Experiment Details and Available Data Fields\n",
-    "\n",
-    "The dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point output from the OpenIFS 43r3 model.\n",
-    "The simulation was conducted on a reduced Gaussian octahedral grid (Tco1279), corresponding to a horizontal resolution of approximately 9 km.\n",
-    "The 5-day forecast was run with a time step length of 600 seconds using a HighResMIP based output configuration. \n",
-    "The total volume of the available dataset is about 258 GB.\n",
-    "\n",
-    "\n",
-    "There is a single NetCDF file for the surface level fields and separate files for each pressure level field.\n",
-    "\n",
-    "### Pressure Level Parameters\n",
-    "\n",
-    "The following parameters are available as pressure level fields:\n",
-    "```python\n",
-    "[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n",
-    "```\n",
-    "\n",
-    "These parameters are stored at the following pressure levels (in Pa):\n",
-    "```python\n",
-    "\n",
-    "[100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 25000, 20000, 15000, 10000, 7000, 5000, 3000, 2000, 1000, 500, 100]\n",
-    "\n",
-    "```\n",
-    "The available time steps start at 2009-10-15 18:00:00 UTC, increasing in 6-hour steps to 2009-10-20 12:00:00 UTC.\n",
-    "\n",
-    "### Surface Level Parameters\n",
-    "\n",
-    "The following parameters are available as surface level fields:\n",
-    "```python\n",
-    "[\"10u\", \"10v\", \"2d\", \"2t\", \"cp\", \"e\", \"es\", \"ewss\", \"msl\", \"nsss\", \"ro\", \"rsn\", \"sd\", \"sf\", \"skt\", \"slhf\", \"smlt\", \"sp\", \"sro\", \"sshf\", \"ssr\", \"ssrc\", \"ssrd\", \"stl1\", \"stl2\", \"stl3\", \"stl4\", \"str\", \"strc\", \"strd\", \"swvl1\", \"swvl2\", \"swvl3\", \"swvl4\", \"tcc\", \"tciw\", \"tclw\", \"tcwv\", \"tisr\", \"tp\", \"tsn\", \"tsr\", \"tsrc\", \"ttr\", \"ttrc\"]\n",
-    "```\n",
-    "\n",
-    "Here, the available time steps start at 2009-10-15 15:00:00 UTC, increasing in 3-hour steps to 2009-10-20 12:00:00 UTC."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
-   "metadata": {},
-   "source": [
-    "## Using the `load_OpenIFS_data` Function\n",
-    "\n",
-    "To load the NetCDF files, you can use the following function:\n",
-    "\n",
-    "```python\n",
-    "load_OpenIFS_data(leveltype=None, param=None)\n",
-    "```\n",
-    "\n",
-    "### Parameters\n",
-    "\n",
-    "- **`leveltype`**: Defines the type of data to load. Choose one of the following:\n",
-    "  - `\"pl\"`: Pressure level fields.\n",
-    "  - `\"sfc\"`: Surface level fields.\n",
-    "- **`param`**: Specifies the parameter of the pressure level data. Options include: `\"q\"`, `\"r\"`, `\"t\"`, `\"u\"`, `\"v\"`, `\"vo\"`, `\"w\"`, `\"v\"` and `\"z\"`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# URL of S3 bucket\n",
-    "BASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
-   "metadata": {},
-   "source": [
-    "First, the package `zarr` (modern dataset format that is specifically designed for chunked access) and its dependencies need to be imported for the remote access."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Python(8725) MallocStackLogging: can't turn off malloc stack logging because it was not enabled.\n"
-     ]
+  "metadata": {
+    "kernelspec": {
+      "name": "python",
+      "display_name": "Python (Pyodide)",
+      "language": "python"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "python",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8"
     }
-   ],
-   "source": [
-    "import aiohttp\n",
-    "import dask\n",
-    "import fsspec\n",
-    "import zarr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5885a858-f6a6-429b-ae83-69e3f965f032",
-   "metadata": {},
-   "source": [
-    "The `load_OpenIFS_data` function simplifies remote access to NetCDF datasets stored on the S3 bucket. It validates input parameters, constructs the correct URL for the requested data, and loads it efficiently as an xarray dataset using the corresponding `.ref` file.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3a68e663-c6c8-49c2-9678-929d87f772b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "import requests\n",
-    "import xarray as xr\n",
-    "\n",
-    "\n",
-    "def load_OpenIFS_data(leveltype=None, param=None):\n",
-    "    if leveltype not in {\"pl\", \"sfc\"}:\n",
-    "        raise ValueError(f\"Invalid leveltype: '{leveltype}'. Available leveltypes: pl, sfc\")\n",
-    "\n",
-    "    if leveltype == \"pl\" and not param:\n",
-    "        raise ValueError(f\"Param is required for leveltype '{leveltype}'. Available params: q, r, t, u, v, vo, w, z\")\n",
-    "\n",
-    "    if leveltype == \"sfc\" and param:\n",
-    "        print(\"Warning: Specifying 'param' is unnecessary for sfc level and will be ignored.\")\n",
-    "\n",
-    "    if leveltype == \"sfc\":\n",
-    "        url = f\"{BASE_URL}/OpenIFS/HighResMIP_3h_reduced_sfc.nc\"\n",
-    "    elif leveltype == \"pl\":\n",
-    "        if param not in {\"q\", \"r\", \"t\", \"u\", \"v\", \"vo\", \"w\", \"z\"}:\n",
-    "            raise ValueError(f\"Incorrect param: '{param}'. Available: q, r, t, u, v, vo, w, z\")\n",
-    "\n",
-    "        url = f\"{BASE_URL}/OpenIFS/HighResMIP_6h_reduced_pl_{param}.nc\"\n",
-    "        \n",
-    "    ref = requests.get(f\"{url}.ref\").json()\n",
-    "\n",
-    "    print(f\"Loading dataset {url}\")\n",
-    "\n",
-    "    return xr.open_dataset(\n",
-    "        \"reference://\", \n",
-    "        engine=\"zarr\", \n",
-    "        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n",
-    "        consolidated=False, \n",
-    "        chunks=dict(),\n",
-    "    )"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "10b46f55-ec2a-45ea-87c6-46173ce58696",
-   "metadata": {},
-   "source": [
-    "## Example Usage\n",
-    "\n",
-    "To load NetCDF file for pressure level parameters, you can use the following example. This will load the **specific humidity (q)** data. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "37c31fe0-661a-4b71-afb0-61f7fb804f5f",
-   "metadata": {},
-   "outputs": [
+  "nbformat_minor": 5,
+  "nbformat": 4,
+  "cells": [
+    {
+      "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
+      "cell_type": "markdown",
+      "source": "# OpenIFS 43r3 data",
+      "metadata": {}
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/HighResMIP_6h_reduced_pl_q.nc\n"
-     ]
+      "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
+      "cell_type": "markdown",
+      "source": "> [!NOTE]\n> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n\n> [!CAUTION]\n> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep.",
+      "metadata": {}
     },
     {
-     "data": {
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-       "\n",
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-       "\n",
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-       ".xr-section-summary-in:checked + label > span {\n",
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-       "\n",
-       ".xr-section-summary,\n",
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-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
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-       "\n",
-       ".xr-array-data,\n",
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-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
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-       "  display: inline-block;\n",
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-       "\n",
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-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
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-       ".xr-dim-list li {\n",
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-       "\n",
-       ".xr-var-list,\n",
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-       "\n",
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-       ".xr-var-list > li:nth-child(odd) > div,\n",
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-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
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-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:              (cell: 6599680, nvertex: 4, pressure_levels: 19,\n",
-       "                          time_counter: 20, axis_nbounds: 2)\n",
-       "Coordinates:\n",
-       "    lat                  (cell) float32 dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n",
-       "    lon                  (cell) float32 dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n",
-       "  * pressure_levels      (pressure_levels) float32 1e+05 9.25e+04 ... 100.0\n",
-       "  * time_counter         (time_counter) datetime64[ns] 2009-10-15T18:00:00 .....\n",
-       "    time_instant         (time_counter) datetime64[ns] dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "Dimensions without coordinates: cell, nvertex, axis_nbounds\n",
-       "Data variables:\n",
-       "    bounds_lat           (cell, nvertex) float32 dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n",
-       "    bounds_lon           (cell, nvertex) float32 dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n",
-       "    q                    (time_counter, pressure_levels, cell) float64 dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;\n",
-       "    time_counter_bounds  (time_counter, axis_nbounds) datetime64[ns] dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
-       "    time_instant_bounds  (time_counter, axis_nbounds) datetime64[ns] dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.6\n",
-       "    description:  Created by xios\n",
-       "    name:         HighResMIP_6h_reduced_pl_q\n",
-       "    timeStamp:    2024-Apr-05 11:05:21 GMT\n",
-       "    title:        Created by xios\n",
-       "    uuid:         25d5f0a2-ceae-47e1-936f-5b1804228ca6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a3dbf7a5-1290-49dc-b5ce-804d9d5b8257' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3dbf7a5-1290-49dc-b5ce-804d9d5b8257' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span class='xr-has-index'>pressure_levels</span>: 19</li><li><span class='xr-has-index'>time_counter</span>: 20</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-003d3ffc-41af-412d-8282-c28e0d909707' class='xr-section-summary-in' type='checkbox'  checked><label for='section-003d3ffc-41af-412d-8282-c28e0d909707' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-f1221d5e-ac6c-4eff-985d-d30200fa229c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1221d5e-ac6c-4eff-985d-d30200fa229c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0a3d300-b280-433e-b54b-6fb006da5b8e' class='xr-var-data-in' type='checkbox'><label for='data-f0a3d300-b280-433e-b54b-6fb006da5b8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 25.18 MiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6599680,) </td>\n",
-       "                        <td> (824960,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-b86c525f-f0df-4417-b2b2-058519d411c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b86c525f-f0df-4417-b2b2-058519d411c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de4a86da-e826-4399-b62a-c189d3dbbf12' class='xr-var-data-in' type='checkbox'><label for='data-de4a86da-e826-4399-b62a-c189d3dbbf12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 25.18 MiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6599680,) </td>\n",
-       "                        <td> (824960,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "\n",
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-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure_levels</span></div><div class='xr-var-dims'>(pressure_levels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.25e+04 ... 500.0 100.0</div><input id='attrs-0160eb34-72d4-48df-83c9-b36189647df8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0160eb34-72d4-48df-83c9-b36189647df8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23095e8a-6380-4c29-9211-f5c58b14c8b8' class='xr-var-data-in' type='checkbox'><label for='data-23095e8a-6380-4c29-9211-f5c58b14c8b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertical pressure levels</dd><dt><span>name :</span></dt><dd>pressure_levels</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  92500.,  85000.,  70000.,  60000.,  50000.,  40000.,  30000.,\n",
-       "        25000.,  20000.,  15000.,  10000.,   7000.,   5000.,   3000.,   2000.,\n",
-       "         1000.,    500.,    100.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T18:00:00 ... 2009-10-...</div><input id='attrs-1adb0af2-a504-4725-ae83-c800b2a8d104' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1adb0af2-a504-4725-ae83-c800b2a8d104' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffe681b4-1de9-4c0a-88b3-6a8bbbafd935' class='xr-var-data-in' type='checkbox'><label for='data-ffe681b4-1de9-4c0a-88b3-6a8bbbafd935' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T18:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-16T06:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-16T18:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-17T06:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-17T18:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-18T06:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-18T18:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-19T06:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-19T18:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-20T06:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-c043edfb-6c49-4c4b-bce5-f896e8f35868' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c043edfb-6c49-4c4b-bce5-f896e8f35868' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c6b2b62-b00b-42c3-843b-22d6db116289' class='xr-var-data-in' type='checkbox'><label for='data-7c6b2b62-b00b-42c3-843b-22d6db116289' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 160 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (20,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
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-       "        <td>\n",
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-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e514a4c0-ebeb-4fc2-8bc1-de8fc78d5f66' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e514a4c0-ebeb-4fc2-8bc1-de8fc78d5f66' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-3f2ad75b-ea3d-4959-ac54-3c151da308ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f2ad75b-ea3d-4959-ac54-3c151da308ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b229b163-4ec3-4cf5-99d8-cd9294f471c7' class='xr-var-data-in' type='checkbox'><label for='data-b229b163-4ec3-4cf5-99d8-cd9294f471c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100.70 MiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6599680, 4) </td>\n",
-       "                        <td> (206240, 4) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-b9eabdda-1011-449c-ab70-f076032d9bfe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9eabdda-1011-449c-ab70-f076032d9bfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d4f087e-e876-48bd-b1cb-2985a701b9d6' class='xr-var-data-in' type='checkbox'><label for='data-5d4f087e-e876-48bd-b1cb-2985a701b9d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100.70 MiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6599680, 4) </td>\n",
-       "                        <td> (206240, 4) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time_counter, pressure_levels, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;</div><input id='attrs-9e82060b-0645-4bb8-ac93-07bc148ab1fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e82060b-0645-4bb8-ac93-07bc148ab1fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49469b49-46fe-4bbc-80d1-91428c2ace84' class='xr-var-data-in' type='checkbox'><label for='data-49469b49-46fe-4bbc-80d1-91428c2ace84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>6 h</dd><dt><span>interval_write :</span></dt><dd>6 h</dd><dt><span>long_name :</span></dt><dd>specific humidity</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg*kg-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.69 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (20, 19, 6599680) </td>\n",
-       "                        <td> (1, 1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 6080 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-094d23e1-c351-494f-a2a0-7c6e9071c4be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-094d23e1-c351-494f-a2a0-7c6e9071c4be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae728cd4-3bfd-449d-b7a1-634c95dd3cd5' class='xr-var-data-in' type='checkbox'><label for='data-ae728cd4-3bfd-449d-b7a1-634c95dd3cd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 320 B </td>\n",
-       "                        <td> 16 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (20, 2) </td>\n",
-       "                        <td> (1, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-98ead1c0-fcc5-45b0-acdc-42d8ea4f5400' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98ead1c0-fcc5-45b0-acdc-42d8ea4f5400' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d33fe9a-fa8a-4d1c-9f9b-0c97aec637ab' class='xr-var-data-in' type='checkbox'><label for='data-4d33fe9a-fa8a-4d1c-9f9b-0c97aec637ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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-       "            <table style=\"border-collapse: collapse;\">\n",
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-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 320 B </td>\n",
-       "                        <td> 16 B </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6bd8db64-1956-4212-9907-0a3a480e4aac' class='xr-section-summary-in' type='checkbox'  ><label for='section-6bd8db64-1956-4212-9907-0a3a480e4aac' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>pressure_levels</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ab522574-e230-47c6-a2e4-a67c685a8dbf' class='xr-index-data-in' type='checkbox'/><label for='index-ab522574-e230-47c6-a2e4-a67c685a8dbf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000.0,  92500.0,  85000.0,  70000.0,  60000.0,  50000.0,  40000.0,\n",
-       "        30000.0,  25000.0,  20000.0,  15000.0,  10000.0,   7000.0,   5000.0,\n",
-       "         3000.0,   2000.0,   1000.0,    500.0,    100.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;pressure_levels&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2542cba3-3c5c-4e6d-8ed5-424804fd58f1' class='xr-index-data-in' type='checkbox'/><label for='index-2542cba3-3c5c-4e6d-8ed5-424804fd58f1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 18:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n",
-       "               &#x27;2009-10-16 06:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n",
-       "               &#x27;2009-10-16 18:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n",
-       "               &#x27;2009-10-17 06:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n",
-       "               &#x27;2009-10-17 18:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n",
-       "               &#x27;2009-10-18 06:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n",
-       "               &#x27;2009-10-18 18:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n",
-       "               &#x27;2009-10-19 06:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n",
-       "               &#x27;2009-10-19 18:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n",
-       "               &#x27;2009-10-20 06:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-72a56cd9-6dcd-49b5-9968-18e0aea4efb5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-72a56cd9-6dcd-49b5-9968-18e0aea4efb5' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_6h_reduced_pl_q</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 11:05:21 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>25d5f0a2-ceae-47e1-936f-5b1804228ca6</dd></dl></div></li></ul></div></div>"
+      "id": "463bf2c0-ac3f-4786-9687-362393802dce",
+      "cell_type": "markdown",
+      "source": "This notebook is a comprehensive guide to accessing NetDCF files for an OpenIFS simulation. It includes details about the available parameters, levels, and steps, along with instructions for using the `load_OpenIFS_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point output from the OpenIFS 43r3 model.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco1279), corresponding to a horizontal resolution of approximately 9 km.\nThe 5-day forecast was run with a time step length of 600 seconds using a HighResMIP based output configuration. \nThe total volume of the available dataset is about 258 GB.\n\n\nThere is a single NetCDF file for the surface level fields and separate files for each pressure level field.\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in Pa):\n```python\n\n[100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 25000, 20000, 15000, 10000, 7000, 5000, 3000, 2000, 1000, 500, 100]\n\n```\nThe available time steps start at 2009-10-15 18:00:00 UTC, increasing in 6-hour steps to 2009-10-20 12:00:00 UTC.\n\n### Surface Level Parameters\n\nThe following parameters are available as surface level fields:\n```python\n[\"10u\", \"10v\", \"2d\", \"2t\", \"cp\", \"e\", \"es\", \"ewss\", \"msl\", \"nsss\", \"ro\", \"rsn\", \"sd\", \"sf\", \"skt\", \"slhf\", \"smlt\", \"sp\", \"sro\", \"sshf\", \"ssr\", \"ssrc\", \"ssrd\", \"stl1\", \"stl2\", \"stl3\", \"stl4\", \"str\", \"strc\", \"strd\", \"swvl1\", \"swvl2\", \"swvl3\", \"swvl4\", \"tcc\", \"tciw\", \"tclw\", \"tcwv\", \"tisr\", \"tp\", \"tsn\", \"tsr\", \"tsrc\", \"ttr\", \"ttrc\"]\n```\n\nHere, the available time steps start at 2009-10-15 15:00:00 UTC, increasing in 3-hour steps to 2009-10-20 12:00:00 UTC.",
+      "metadata": {}
+    },
+    {
+      "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
+      "cell_type": "markdown",
+      "source": "## Using the `load_OpenIFS_data` Function\n\nTo load the NetCDF files, you can use the following function:\n\n```python\nload_OpenIFS_data(leveltype=None, param=None)\n```\n\n### Parameters\n\n- **`leveltype`**: Defines the type of data to load. Choose one of the following:\n  - `\"pl\"`: Pressure level fields.\n  - `\"sfc\"`: Surface level fields.\n- **`param`**: Specifies the parameter of the pressure level data. Options include: `\"q\"`, `\"r\"`, `\"t\"`, `\"u\"`, `\"v\"`, `\"vo\"`, `\"w\"`, `\"v\"` and `\"z\"`.\n",
+      "metadata": {}
+    },
+    {
+      "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
+      "cell_type": "code",
+      "source": "# URL of S3 bucket\nBASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\"",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": 1
+    },
+    {
+      "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
+      "cell_type": "markdown",
+      "source": "First, the package `zarr` (modern dataset format that is specifically designed for chunked access) and its dependencies need to be imported for the remote access.",
+      "metadata": {}
+    },
+    {
+      "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
+      "cell_type": "code",
+      "source": "import aiohttp\nimport dask\nimport fsspec\nimport zarr",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Loading Jinja2, MarkupSafe, aiohttp, aiosignal, asciitree, async-timeout, attrs, click, cloudpickle, dask, frozenlist, fsspec, importlib_metadata, locket, msgpack, multidict, numcodecs, numpy, partd, propcache, pyyaml, toolz, yarl, zarr, zipp\n[pyodide]: Loaded Jinja2, MarkupSafe, aiohttp, aiosignal, asciitree, async-timeout, attrs, click, cloudpickle, dask, frozenlist, fsspec, importlib_metadata, locket, msgpack, multidict, numcodecs, numpy, partd, propcache, pyyaml, toolz, yarl, zarr, zipp\n[pyodide]: Memory usage has grown to 71.9MiB (from 49.9MiB) for this notebook\n",
+          "output_type": "stream"
+        }
       ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:              (cell: 6599680, nvertex: 4, pressure_levels: 19,\n",
-       "                          time_counter: 20, axis_nbounds: 2)\n",
-       "Coordinates:\n",
-       "    lat                  (cell) float32 dask.array<chunksize=(824960,), meta=np.ndarray>\n",
-       "    lon                  (cell) float32 dask.array<chunksize=(824960,), meta=np.ndarray>\n",
-       "  * pressure_levels      (pressure_levels) float32 1e+05 9.25e+04 ... 100.0\n",
-       "  * time_counter         (time_counter) datetime64[ns] 2009-10-15T18:00:00 .....\n",
-       "    time_instant         (time_counter) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "Dimensions without coordinates: cell, nvertex, axis_nbounds\n",
-       "Data variables:\n",
-       "    bounds_lat           (cell, nvertex) float32 dask.array<chunksize=(206240, 4), meta=np.ndarray>\n",
-       "    bounds_lon           (cell, nvertex) float32 dask.array<chunksize=(206240, 4), meta=np.ndarray>\n",
-       "    q                    (time_counter, pressure_levels, cell) float64 dask.array<chunksize=(1, 1, 412480), meta=np.ndarray>\n",
-       "    time_counter_bounds  (time_counter, axis_nbounds) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
-       "    time_instant_bounds  (time_counter, axis_nbounds) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.6\n",
-       "    description:  Created by xios\n",
-       "    name:         HighResMIP_6h_reduced_pl_q\n",
-       "    timeStamp:    2024-Apr-05 11:05:21 GMT\n",
-       "    title:        Created by xios\n",
-       "    uuid:         25d5f0a2-ceae-47e1-936f-5b1804228ca6"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_pl_q = load_OpenIFS_data(leveltype=\"pl\", param=\"q\")\n",
-    "ds_pl_q"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33418b16-9735-4eb9-b540-942325d552df",
-   "metadata": {},
-   "source": [
-    "The `earthkit` package is used for plotting the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "169008a5-cfa4-4078-8bad-32510c9a91e7",
-   "metadata": {},
-   "outputs": [
+      "execution_count": 2
+    },
+    {
+      "id": "5885a858-f6a6-429b-ae83-69e3f965f032",
+      "cell_type": "markdown",
+      "source": "The `load_OpenIFS_data` function simplifies remote access to NetCDF datasets stored on the S3 bucket. It validates input parameters, constructs the correct URL for the requested data, and loads it efficiently as an xarray dataset using the corresponding `.ref` file.\n",
+      "metadata": {}
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[pyodide]: Loading Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cftime, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n",
-      "[pyodide]: Loaded Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cftime, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n"
-     ]
+      "id": "3a68e663-c6c8-49c2-9678-929d87f772b4",
+      "cell_type": "code",
+      "source": "import json\n\nimport requests\nimport xarray as xr\n\n\ndef load_OpenIFS_data(leveltype=None, param=None):\n    if leveltype not in {\"pl\", \"sfc\"}:\n        raise ValueError(f\"Invalid leveltype: '{leveltype}'. Available leveltypes: pl, sfc\")\n\n    if leveltype == \"pl\" and not param:\n        raise ValueError(f\"Param is required for leveltype '{leveltype}'. Available params: q, r, t, u, v, vo, w, z\")\n\n    if leveltype == \"sfc\" and param:\n        print(\"Warning: Specifying 'param' is unnecessary for sfc level and will be ignored.\")\n\n    if leveltype == \"sfc\":\n        url = f\"{BASE_URL}/OpenIFS/HighResMIP_3h_reduced_sfc.nc\"\n    elif leveltype == \"pl\":\n        if param not in {\"q\", \"r\", \"t\", \"u\", \"v\", \"vo\", \"w\", \"z\"}:\n            raise ValueError(f\"Incorrect param: '{param}'. Available: q, r, t, u, v, vo, w, z\")\n\n        url = f\"{BASE_URL}/OpenIFS/HighResMIP_6h_reduced_pl_{param}.nc\"\n        \n    ref = requests.get(f\"{url}.ref\").json()\n\n    print(f\"Loading dataset {url}\")\n\n    return xr.open_dataset(\n        \"reference://\", \n        engine=\"zarr\", \n        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n        consolidated=False, \n        chunks=dict(),\n    )",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Loading pandas, python-dateutil, pytz, tzdata, xarray\n[pyodide]: Loaded pandas, python-dateutil, pytz, tzdata, xarray\n[pyodide]: Memory usage has grown to 124.2MiB (from 71.9MiB) for this notebook\n",
+          "output_type": "stream"
+        }
+      ],
+      "execution_count": 3
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/lib/python3.12/site-packages/earthkit/regrid/utils/caching.py:27: UserWarning: only soft file lock is available\n",
-      "  from filelock import FileLock\n"
-     ]
+      "id": "10b46f55-ec2a-45ea-87c6-46173ce58696",
+      "cell_type": "markdown",
+      "source": "## Example Usage\n\nTo load NetCDF file for pressure level parameters, you can use the following example. This will load the **specific humidity (q)** data. \n",
+      "metadata": {}
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[pyodide]: Memory usage has grown to 309.4MiB (from 124.2MiB) for this notebook\n"
-     ]
-    }
-   ],
-   "source": [
-    "import earthkit.plots\n",
-    "import earthkit.plots.quickmap"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6ee55166-f151-474c-b36c-1e07bc516906",
-   "metadata": {},
-   "source": [
-    "The data can be visualized as demonstrated below. For example, the specific humidity at **pressure level 1e5Pa** on **2009-10-20 at 12:00:00 UTC** is plotted. `[::10]` applies slicing to the data being plotted (syntax: `[start:stop:step]`) in order to reduce computational load by selecting only every 10th data point.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2e12e256",
-   "metadata": {},
-   "outputs": [
+      "id": "37c31fe0-661a-4b71-afb0-61f7fb804f5f",
+      "cell_type": "code",
+      "source": "ds_pl_q = load_OpenIFS_data(leveltype=\"pl\", param=\"q\")\nds_pl_q",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/HighResMIP_6h_reduced_pl_q.nc\n",
+          "output_type": "stream"
+        },
+        {
+          "execution_count": 4,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "<xarray.Dataset> Size: 20GB\nDimensions:              (cell: 6599680, nvertex: 4, pressure_levels: 19,\n                          time_counter: 20, axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array<chunksize=(824960,), meta=np.ndarray>\n    lon                  (cell) float32 26MB dask.array<chunksize=(824960,), meta=np.ndarray>\n  * pressure_levels      (pressure_levels) float32 76B 1e+05 9.25e+04 ... 100.0\n  * time_counter         (time_counter) datetime64[ns] 160B 2009-10-15T18:00:...\n    time_instant         (time_counter) datetime64[ns] 160B dask.array<chunksize=(1,), meta=np.ndarray>\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables:\n    bounds_lat           (cell, nvertex) float32 106MB dask.array<chunksize=(206240, 4), meta=np.ndarray>\n    bounds_lon           (cell, nvertex) float32 106MB dask.array<chunksize=(206240, 4), meta=np.ndarray>\n    q                    (time_counter, pressure_levels, cell) float64 20GB dask.array<chunksize=(1, 1, 412480), meta=np.ndarray>\n    time_counter_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array<chunksize=(1, 2), meta=np.ndarray>\n    time_instant_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array<chunksize=(1, 2), meta=np.ndarray>\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_6h_reduced_pl_q\n    timeStamp:    2024-Apr-05 11:05:21 GMT\n    title:        Created by xios\n    uuid:         25d5f0a2-ceae-47e1-936f-5b1804228ca6",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 20GB\nDimensions:              (cell: 6599680, nvertex: 4, pressure_levels: 19,\n                          time_counter: 20, axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n  * pressure_levels      (pressure_levels) float32 76B 1e+05 9.25e+04 ... 100.0\n  * time_counter         (time_counter) datetime64[ns] 160B 2009-10-15T18:00:...\n    time_instant         (time_counter) datetime64[ns] 160B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables:\n    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    q                    (time_counter, pressure_levels, cell) float64 20GB dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;\n    time_counter_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n    time_instant_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_6h_reduced_pl_q\n    timeStamp:    2024-Apr-05 11:05:21 GMT\n    title:        Created by xios\n    uuid:         25d5f0a2-ceae-47e1-936f-5b1804228ca6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e42b74ad-13ad-40b7-a6fa-913d8ca96a7b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e42b74ad-13ad-40b7-a6fa-913d8ca96a7b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span class='xr-has-index'>pressure_levels</span>: 19</li><li><span class='xr-has-index'>time_counter</span>: 20</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-42639ae1-97c6-4617-b9b9-bf6ff48b7046' class='xr-section-summary-in' type='checkbox'  checked><label for='section-42639ae1-97c6-4617-b9b9-bf6ff48b7046' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-86bd71be-18d0-486a-8197-b865e384e743' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86bd71be-18d0-486a-8197-b865e384e743' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3228934-0686-4c5c-b7da-4bd1d7ca2a1e' class='xr-var-data-in' type='checkbox'><label for='data-f3228934-0686-4c5c-b7da-4bd1d7ca2a1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-22bc7e6f-7824-42cf-af27-e98a9d1aac6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22bc7e6f-7824-42cf-af27-e98a9d1aac6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6e94758-2804-4f05-9b1c-5a1ec542e82b' class='xr-var-data-in' type='checkbox'><label for='data-c6e94758-2804-4f05-9b1c-5a1ec542e82b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure_levels</span></div><div class='xr-var-dims'>(pressure_levels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.25e+04 ... 500.0 100.0</div><input id='attrs-30f50269-45de-40e2-b70f-727a84d0a1bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30f50269-45de-40e2-b70f-727a84d0a1bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad55ae81-a575-4aff-9b57-144b6e0aaf84' class='xr-var-data-in' type='checkbox'><label for='data-ad55ae81-a575-4aff-9b57-144b6e0aaf84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertical pressure levels</dd><dt><span>name :</span></dt><dd>pressure_levels</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  92500.,  85000.,  70000.,  60000.,  50000.,  40000.,  30000.,\n        25000.,  20000.,  15000.,  10000.,   7000.,   5000.,   3000.,   2000.,\n         1000.,    500.,    100.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T18:00:00 ... 2009-10-...</div><input id='attrs-39057a92-d87a-406f-a3a4-eb16e81b3cfd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39057a92-d87a-406f-a3a4-eb16e81b3cfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a5d1f4d-49ab-4f11-9c2a-8eb85b36ad98' class='xr-var-data-in' type='checkbox'><label for='data-0a5d1f4d-49ab-4f11-9c2a-8eb85b36ad98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T18:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n       &#x27;2009-10-16T06:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n       &#x27;2009-10-16T18:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n       &#x27;2009-10-17T06:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n       &#x27;2009-10-17T18:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n       &#x27;2009-10-18T06:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n       &#x27;2009-10-18T18:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n       &#x27;2009-10-19T06:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n       &#x27;2009-10-19T18:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n       &#x27;2009-10-20T06:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-84bf27d4-e162-445b-9a60-aa788595c479' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84bf27d4-e162-445b-9a60-aa788595c479' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ba09abb-f474-41d6-b78c-2d8e12b1638b' class='xr-var-data-in' type='checkbox'><label for='data-5ba09abb-f474-41d6-b78c-2d8e12b1638b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 160 B </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"84\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"34\" style=\"stroke-width:2\" />\n  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"34\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"34\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"34\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"34\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"34\" />\n  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"34\" />\n  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"34\" />\n  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"34\" />\n  <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"34\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"34\" />\n  <line x1=\"66\" y1=\"0\" x2=\"66\" y2=\"34\" />\n  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"34\" />\n  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"34\" />\n  <line x1=\"84\" y1=\"0\" x2=\"84\" y2=\"34\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"34\" />\n  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"34\" />\n  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"34\" />\n  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"34\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,34.50101617379934 0.0,34.50101617379934\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"54.501016\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >20</text>\n  <text x=\"140.000000\" y=\"17.250508\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,17.250508)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6e5fd37-085e-4167-9317-0356d58eff60' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6e5fd37-085e-4167-9317-0356d58eff60' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-14588162-7924-4116-81b8-2a203f4877ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14588162-7924-4116-81b8-2a203f4877ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-945af095-deb0-4e97-8bda-a3007d11cf26' class='xr-var-data-in' type='checkbox'><label for='data-945af095-deb0-4e97-8bda-a3007d11cf26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-41235bd4-b89b-40cc-8895-61e207931ae5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41235bd4-b89b-40cc-8895-61e207931ae5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3607e7d-559d-4393-9001-d83ff41ab62f' class='xr-var-data-in' type='checkbox'><label for='data-e3607e7d-559d-4393-9001-d83ff41ab62f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time_counter, pressure_levels, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;</div><input id='attrs-8a01df3a-3357-4d5b-90ac-cf8d19c70aba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a01df3a-3357-4d5b-90ac-cf8d19c70aba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fe841b1-3566-4b38-b4cc-88b0af48a17f' class='xr-var-data-in' type='checkbox'><label for='data-1fe841b1-3566-4b38-b4cc-88b0af48a17f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>6 h</dd><dt><span>interval_write :</span></dt><dd>6 h</dd><dt><span>long_name :</span></dt><dd>specific humidity</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg*kg-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.69 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 19, 6599680) </td>\n                        <td> (1, 1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 6080 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"1\" x2=\"24\" y2=\"16\" />\n  <line x1=\"10\" y1=\"2\" x2=\"24\" 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x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">19</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-2ad7239e-1173-4712-be56-8d2a6d721b73' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ad7239e-1173-4712-be56-8d2a6d721b73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f49a5407-4f34-4cf4-9cfe-ddd41034eabd' class='xr-var-data-in' type='checkbox'><label for='data-f49a5407-4f34-4cf4-9cfe-ddd41034eabd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"38\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" 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style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"19.298432\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"58.596863\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,58.596863,60.000000)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-bbc9df44-d05d-4fbc-91a6-7a25cf55d9ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bbc9df44-d05d-4fbc-91a6-7a25cf55d9ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32f95e28-23e6-4d61-80fe-b99568464454' class='xr-var-data-in' type='checkbox'><label for='data-32f95e28-23e6-4d61-80fe-b99568464454' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"38\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"38\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"38\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"38\" y2=\"54\" />\n  <line x1=\"0\" y1=\"60\" x2=\"38\" y2=\"60\" />\n  <line x1=\"0\" y1=\"66\" x2=\"38\" y2=\"66\" />\n  <line x1=\"0\" y1=\"72\" x2=\"38\" y2=\"72\" />\n  <line x1=\"0\" y1=\"78\" x2=\"38\" y2=\"78\" />\n  <line x1=\"0\" y1=\"84\" x2=\"38\" y2=\"84\" />\n  <line x1=\"0\" y1=\"90\" x2=\"38\" y2=\"90\" />\n  <line x1=\"0\" y1=\"96\" x2=\"38\" y2=\"96\" />\n  <line x1=\"0\" y1=\"102\" x2=\"38\" y2=\"102\" />\n  <line x1=\"0\" y1=\"108\" x2=\"38\" y2=\"108\" />\n  <line x1=\"0\" y1=\"120\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 38.596863036086,0.0 38.596863036086,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"19.298432\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"58.596863\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,58.596863,60.000000)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7951bec1-6e13-4106-8210-ad8fa7734ca8' class='xr-section-summary-in' type='checkbox'  ><label for='section-7951bec1-6e13-4106-8210-ad8fa7734ca8' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>pressure_levels</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-da077648-6a7f-45d7-8d98-351697bd0c41' class='xr-index-data-in' type='checkbox'/><label for='index-da077648-6a7f-45d7-8d98-351697bd0c41' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000.0,  92500.0,  85000.0,  70000.0,  60000.0,  50000.0,  40000.0,\n        30000.0,  25000.0,  20000.0,  15000.0,  10000.0,   7000.0,   5000.0,\n         3000.0,   2000.0,   1000.0,    500.0,    100.0],\n      dtype=&#x27;float32&#x27;, name=&#x27;pressure_levels&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b6f0cac-da6c-442f-948a-197db11624c2' class='xr-index-data-in' type='checkbox'/><label for='index-9b6f0cac-da6c-442f-948a-197db11624c2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 18:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n               &#x27;2009-10-16 06:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n               &#x27;2009-10-16 18:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n               &#x27;2009-10-17 06:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n               &#x27;2009-10-17 18:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n               &#x27;2009-10-18 06:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n               &#x27;2009-10-18 18:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n               &#x27;2009-10-19 06:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n               &#x27;2009-10-19 18:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n               &#x27;2009-10-20 06:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4c6c9ce-ce03-4b7c-9764-b8dfb432cba3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f4c6c9ce-ce03-4b7c-9764-b8dfb432cba3' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_6h_reduced_pl_q</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 11:05:21 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>25d5f0a2-ceae-47e1-936f-5b1804228ca6</dd></dl></div></li></ul></div></div>"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 4
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip\n",
-      "  warnings.warn(f'Downloading: {url}', DownloadWarning)\n"
-     ]
+      "id": "33418b16-9735-4eb9-b540-942325d552df",
+      "cell_type": "markdown",
+      "source": "The `earthkit` package is used for plotting the data.",
+      "metadata": {}
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[pyodide]: Memory usage has grown to 735.2MiB (from 309.4MiB) for this notebook\n"
-     ]
+      "id": "169008a5-cfa4-4078-8bad-32510c9a91e7",
+      "cell_type": "code",
+      "source": "import earthkit.plots\nimport earthkit.plots.quickmap",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Loading Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cftime, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n[pyodide]: Loaded Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cftime, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stderr",
+          "text": "/lib/python3.12/site-packages/earthkit/regrid/utils/caching.py:27: UserWarning: only soft file lock is available\n  from filelock import FileLock\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 309.4MiB (from 124.2MiB) for this notebook\n",
+          "output_type": "stream"
+        }
+      ],
+      "execution_count": 5
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "earthkit.plots.quickmap.plot(\n",
-    "    ds_pl_q[\"q\"].rename(dict(time_counter=\"time\")).sel(\n",
-    "        time=\"2009-10-20T12:00:00\", pressure_levels=1e5,\n",
-    "    )[::10], x=\"lon\", y=\"lat\",\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f2d66bcd-cb4b-4a03-970e-3ce49d68380e",
-   "metadata": {},
-   "source": [
-    "The surface level data contains all available parameters and can can be loaded as follows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "5a574d92-3c0a-429b-97e8-d024d78c5daf",
-   "metadata": {},
-   "outputs": [
+      "id": "6ee55166-f151-474c-b36c-1e07bc516906",
+      "cell_type": "markdown",
+      "source": "The data can be visualized as demonstrated below. For example, the specific humidity at **pressure level 1e5Pa** on **2009-10-20 at 12:00:00 UTC** is plotted. `[::10]` applies slicing to the data being plotted (syntax: `[start:stop:step]`) in order to reduce computational load by selecting only every 10th data point.\n",
+      "metadata": {}
+    },
+    {
+      "id": "2e12e256",
+      "cell_type": "code",
+      "source": "earthkit.plots.quickmap.plot(\n    ds_pl_q[\"q\"].rename(dict(time_counter=\"time\")).sel(\n        time=\"2009-10-20T12:00:00\", pressure_levels=1e5,\n    )[::10], x=\"lon\", y=\"lat\",\n);",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "text": "/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip\n  warnings.warn(f'Downloading: {url}', DownloadWarning)\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 735.2MiB (from 309.4MiB) for this notebook\n",
+          "output_type": "stream"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 700x600 with 2 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 6
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/HighResMIP_3h_reduced_sfc.nc\n"
-     ]
+      "id": "f2d66bcd-cb4b-4a03-970e-3ce49d68380e",
+      "cell_type": "markdown",
+      "source": "The surface level data contains all available parameters and can can be loaded as follows.",
+      "metadata": {}
     },
     {
-     "data": {
-      "text/html": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 95GB\n",
-       "Dimensions:              (time_counter: 40, cell: 6599680, nvertex: 4,\n",
-       "                          axis_nbounds: 2)\n",
-       "Coordinates:\n",
-       "    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n",
-       "    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n",
-       "  * time_counter         (time_counter) datetime64[ns] 320B 2009-10-15T15:00:...\n",
-       "    time_instant         (time_counter) datetime64[ns] 320B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "Dimensions without coordinates: cell, nvertex, axis_nbounds\n",
-       "Data variables: (12/49)\n",
-       "    10u                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
-       "    10v                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
-       "    2d                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
-       "    2t                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
-       "    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n",
-       "    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n",
-       "    ...                   ...\n",
-       "    tp                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
-       "    tsn                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
-       "    tsr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
-       "    tsrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
-       "    ttr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
-       "    ttrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.6\n",
-       "    description:  Created by xios\n",
-       "    name:         HighResMIP_3h_reduced_sfc\n",
-       "    timeStamp:    2024-Apr-05 10:55:20 GMT\n",
-       "    title:        Created by xios\n",
-       "    uuid:         dbac5691-165e-4465-bd75-e403aa400071</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e2b79126-156b-4971-aa40-1f7fb1c848b8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e2b79126-156b-4971-aa40-1f7fb1c848b8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time_counter</span>: 40</li><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-098082a9-5e61-4791-88ea-dc6794b684a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-098082a9-5e61-4791-88ea-dc6794b684a8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-2d8bf04b-ffea-4d8a-8b11-77dc8ecfcb9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d8bf04b-ffea-4d8a-8b11-77dc8ecfcb9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-debdaf7f-90ca-499b-9309-f71a161d7022' class='xr-var-data-in' type='checkbox'><label for='data-debdaf7f-90ca-499b-9309-f71a161d7022' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 25.18 MiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6599680,) </td>\n",
-       "                        <td> (824960,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-f645be90-f42a-4992-b922-4dfc016b7ba6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f645be90-f42a-4992-b922-4dfc016b7ba6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f12bb7ad-e881-4a93-a896-960aceb4e6d0' class='xr-var-data-in' type='checkbox'><label for='data-f12bb7ad-e881-4a93-a896-960aceb4e6d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 25.18 MiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6599680,) </td>\n",
-       "                        <td> (824960,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T15:00:00 ... 2009-10-...</div><input id='attrs-2605a8bc-a9b5-49a2-a50a-926088d0ba57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2605a8bc-a9b5-49a2-a50a-926088d0ba57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cb07a8e-782f-4d1a-b021-8a5b3aa86454' class='xr-var-data-in' type='checkbox'><label for='data-5cb07a8e-782f-4d1a-b021-8a5b3aa86454' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T15:00:00.000000000&#x27;, &#x27;2009-10-15T18:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-15T21:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-16T03:00:00.000000000&#x27;, &#x27;2009-10-16T06:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-16T09:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-16T15:00:00.000000000&#x27;, &#x27;2009-10-16T18:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-16T21:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-17T03:00:00.000000000&#x27;, &#x27;2009-10-17T06:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-17T09:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-17T15:00:00.000000000&#x27;, &#x27;2009-10-17T18:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-17T21:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-18T03:00:00.000000000&#x27;, &#x27;2009-10-18T06:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-18T09:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-18T15:00:00.000000000&#x27;, &#x27;2009-10-18T18:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-18T21:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-19T03:00:00.000000000&#x27;, &#x27;2009-10-19T06:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-19T09:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-19T15:00:00.000000000&#x27;, &#x27;2009-10-19T18:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-19T21:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-20T03:00:00.000000000&#x27;, &#x27;2009-10-20T06:00:00.000000000&#x27;,\n",
-       "       &#x27;2009-10-20T09:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-912895ab-5744-4a6a-92ce-c960664dd168' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-912895ab-5744-4a6a-92ce-c960664dd168' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-624716ce-dd69-4ca3-857f-94fed0763e5a' class='xr-var-data-in' type='checkbox'><label for='data-624716ce-dd69-4ca3-857f-94fed0763e5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 320 B </td>\n",
-       "                        <td> 8 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae47d7b9-fe7f-45ea-a255-1fb225804b0c' class='xr-section-summary-in' type='checkbox'  ><label for='section-ae47d7b9-fe7f-45ea-a255-1fb225804b0c' class='xr-section-summary' >Data variables: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-86f8230c-4e78-4330-9cf7-d1c269ac871a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86f8230c-4e78-4330-9cf7-d1c269ac871a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9fd55a1-7ad6-4dd5-911d-038a5daa527b' class='xr-var-data-in' type='checkbox'><label for='data-e9fd55a1-7ad6-4dd5-911d-038a5daa527b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>u-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-94e2738e-e816-4a6c-b8a0-f111ae62bddc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94e2738e-e816-4a6c-b8a0-f111ae62bddc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65fc61ac-006b-4bcf-8f0e-d2b827625b2d' class='xr-var-data-in' type='checkbox'><label for='data-65fc61ac-006b-4bcf-8f0e-d2b827625b2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>v-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-173a46ff-f5fc-4ad0-8922-8c5f7f0593b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-173a46ff-f5fc-4ad0-8922-8c5f7f0593b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d6abb52-89bf-4e89-81c4-f4d187ccce79' class='xr-var-data-in' type='checkbox'><label for='data-4d6abb52-89bf-4e89-81c4-f4d187ccce79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m dewpoint temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-6afa68b0-3d0f-4481-9cbf-13d1989cbb7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6afa68b0-3d0f-4481-9cbf-13d1989cbb7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58e41a57-4519-43fb-ac55-312d07b1ef66' class='xr-var-data-in' type='checkbox'><label for='data-58e41a57-4519-43fb-ac55-312d07b1ef66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m air temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-5a488474-8be9-4473-9bae-bc3c450aec3a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a488474-8be9-4473-9bae-bc3c450aec3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17b40d48-180e-4c40-afd3-63d050b649bb' class='xr-var-data-in' type='checkbox'><label for='data-17b40d48-180e-4c40-afd3-63d050b649bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100.70 MiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6599680, 4) </td>\n",
-       "                        <td> (206240, 4) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
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-       "        </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-d7ce6ada-3279-4dab-b793-f8c208faf313' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7ce6ada-3279-4dab-b793-f8c208faf313' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d3f1fbf-992f-4443-b778-66a8ed0a3389' class='xr-var-data-in' type='checkbox'><label for='data-5d3f1fbf-992f-4443-b778-66a8ed0a3389' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 100.70 MiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (6599680, 4) </td>\n",
-       "                        <td> (206240, 4) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-d13022c1-ec9f-4eff-9e24-f3e8d348a1ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d13022c1-ec9f-4eff-9e24-f3e8d348a1ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5e42c02-f2a6-499f-b623-1b1b625e4d9a' class='xr-var-data-in' type='checkbox'><label for='data-d5e42c02-f2a6-499f-b623-1b1b625e4d9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>convective precipitation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-542fa6ce-dadb-4395-858e-020f2e95fec0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-542fa6ce-dadb-4395-858e-020f2e95fec0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bffde147-ec0a-46ec-a692-412d56a8aab2' class='xr-var-data-in' type='checkbox'><label for='data-bffde147-ec0a-46ec-a692-412d56a8aab2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
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-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4f277d3e-36d3-4e1b-b4c1-e1544f1066d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f277d3e-36d3-4e1b-b4c1-e1544f1066d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-849ad489-f6a6-4355-98f2-aac2c418b194' class='xr-var-data-in' type='checkbox'><label for='data-849ad489-f6a6-4355-98f2-aac2c418b194' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
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-       "        </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a055dd72-e844-49a1-ab61-31dc9160bdcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a055dd72-e844-49a1-ab61-31dc9160bdcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc8a62ff-d14f-4862-8b9d-2c981a19a622' class='xr-var-data-in' type='checkbox'><label for='data-bc8a62ff-d14f-4862-8b9d-2c981a19a622' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>eastward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_eastward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "        </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-97e2311a-0f15-4536-a164-28e73f1bf0c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97e2311a-0f15-4536-a164-28e73f1bf0c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b98a218d-dd83-4b26-8299-743a35eff53a' class='xr-var-data-in' type='checkbox'><label for='data-b98a218d-dd83-4b26-8299-743a35eff53a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_vel</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
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-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-1b3b4226-8a8b-4ec4-afac-da8b0fff17e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b3b4226-8a8b-4ec4-afac-da8b0fff17e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae67002d-65d6-4556-8fb6-abb3eba3bf83' class='xr-var-data-in' type='checkbox'><label for='data-ae67002d-65d6-4556-8fb6-abb3eba3bf83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>northward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_northward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-9a8b7df4-858d-4430-8783-e579725bbd17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a8b7df4-858d-4430-8783-e579725bbd17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2660eafb-fa12-4236-9e6a-9bc8b84031d0' class='xr-var-data-in' type='checkbox'><label for='data-2660eafb-fa12-4236-9e6a-9bc8b84031d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a8469d2c-af14-4797-aa2c-4d2ec27cf11d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8469d2c-af14-4797-aa2c-4d2ec27cf11d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf5fe558-954f-487d-9cef-60352c5c8328' class='xr-var-data-in' type='checkbox'><label for='data-cf5fe558-954f-487d-9cef-60352c5c8328' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow density</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_density</dd><dt><span>units :</span></dt><dd>kg m-3</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-b513bbc5-24c4-490c-8e0e-3ef29adec6c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b513bbc5-24c4-490c-8e0e-3ef29adec6c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dca2ce8-8caf-45fc-9db4-ea43d17b5069' class='xr-var-data-in' type='checkbox'><label for='data-1dca2ce8-8caf-45fc-9db4-ea43d17b5069' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow depth</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-efb1c85c-045b-43a5-a9e6-6a1fd5fb5a49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efb1c85c-045b-43a5-a9e6-6a1fd5fb5a49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bce49b71-51bb-4c4d-8076-7599e74adf39' class='xr-var-data-in' type='checkbox'><label for='data-bce49b71-51bb-4c4d-8076-7599e74adf39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snowfall</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-357373c0-db53-4a20-9472-e518d0b918fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-357373c0-db53-4a20-9472-e518d0b918fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f20d7b6d-39bc-489f-9622-695ce64371e9' class='xr-var-data-in' type='checkbox'><label for='data-f20d7b6d-39bc-489f-9622-695ce64371e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>skin temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>sea_surface_skin_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-036c2139-54b7-46b7-b2f1-bb26b9674c79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-036c2139-54b7-46b7-b2f1-bb26b9674c79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2460536-7c1d-4124-9f24-30d5dcd7e572' class='xr-var-data-in' type='checkbox'><label for='data-c2460536-7c1d-4124-9f24-30d5dcd7e572' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_latent_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
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-       "        </td>\n",
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-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "        <td>\n",
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-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0f87fb97-0929-48c4-a087-f0d7a0b73315' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f87fb97-0929-48c4-a087-f0d7a0b73315' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-637e4153-85d0-47a7-a5ee-f7faf7f12001' class='xr-var-data-in' type='checkbox'><label for='data-637e4153-85d0-47a7-a5ee-f7faf7f12001' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5ec7b9b6-7c51-428c-8dab-2ba5fb47eddd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ec7b9b6-7c51-428c-8dab-2ba5fb47eddd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0e28e5f-2c0e-42c9-a79c-a88bc2330b58' class='xr-var-data-in' type='checkbox'><label for='data-e0e28e5f-2c0e-42c9-a79c-a88bc2330b58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_sensible_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a329139c-4039-4c34-aca2-4cfa52f3f602' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a329139c-4039-4c34-aca2-4cfa52f3f602' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-685080c6-40e0-4f03-895c-c81b472f4403' class='xr-var-data-in' type='checkbox'><label for='data-685080c6-40e0-4f03-895c-c81b472f4403' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-393369dd-1a1a-47b8-bf5b-e1991d5014b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-393369dd-1a1a-47b8-bf5b-e1991d5014b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ea6b0db-43c0-488a-84ee-ebe6af913b7a' class='xr-var-data-in' type='checkbox'><label for='data-5ea6b0db-43c0-488a-84ee-ebe6af913b7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-03abaf99-7b5e-4350-b9b1-c33464341a2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03abaf99-7b5e-4350-b9b1-c33464341a2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92b03756-1170-404f-8b98-d4a5d6a1f66a' class='xr-var-data-in' type='checkbox'><label for='data-92b03756-1170-404f-8b98-d4a5d6a1f66a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "        <td>\n",
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-       "                    <tr>\n",
-       "                        <td> </td>\n",
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-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 412480) </td>\n",
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-       "                    <tr>\n",
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-       "                    <tr>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-7b2cc906-e816-4625-992d-f710d604f9bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b2cc906-e816-4625-992d-f710d604f9bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-171f56f9-3918-4072-b132-7171861678c4' class='xr-var-data-in' type='checkbox'><label for='data-171f56f9-3918-4072-b132-7171861678c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_2</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-629ff378-180f-49b0-a51e-980ade2154bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-629ff378-180f-49b0-a51e-980ade2154bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77e47166-7cba-4a8d-ad36-dc7853f23f3b' class='xr-var-data-in' type='checkbox'><label for='data-77e47166-7cba-4a8d-ad36-dc7853f23f3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_3</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
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-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "        </td>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
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-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f20a0022-1038-4c28-b357-3267d6f21240' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f20a0022-1038-4c28-b357-3267d6f21240' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d088dee2-8b8e-4fa3-8e90-82de946ab2f5' class='xr-var-data-in' type='checkbox'><label for='data-d088dee2-8b8e-4fa3-8e90-82de946ab2f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
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-       "        </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-8ce69777-d36a-4b95-abb4-bbbcb9507b90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ce69777-d36a-4b95-abb4-bbbcb9507b90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-647b9523-736a-4602-9cc8-5a562415c4ab' class='xr-var-data-in' type='checkbox'><label for='data-647b9523-736a-4602-9cc8-5a562415c4ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-64c47e28-84ed-4513-8e6e-9a7a75489ff6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64c47e28-84ed-4513-8e6e-9a7a75489ff6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db9e10e4-d127-431a-87dc-83bc62e0b715' class='xr-var-data-in' type='checkbox'><label for='data-db9e10e4-d127-431a-87dc-83bc62e0b715' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_1</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5f6b2a79-d3ed-488f-9e7e-6df6e5cba7cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f6b2a79-d3ed-488f-9e7e-6df6e5cba7cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e667dda-eaee-4c42-bc3c-0d85b260ce63' class='xr-var-data-in' type='checkbox'><label for='data-0e667dda-eaee-4c42-bc3c-0d85b260ce63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_2</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
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-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c2875b65-6869-48d0-8b34-0a16cb479851' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2875b65-6869-48d0-8b34-0a16cb479851' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6d52773-2018-4214-ae0f-fa740e64c02c' class='xr-var-data-in' type='checkbox'><label for='data-e6d52773-2018-4214-ae0f-fa740e64c02c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 4</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_4</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-b3e10d08-c8b2-4b8f-848a-45ea43b6c229' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3e10d08-c8b2-4b8f-848a-45ea43b6c229' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f81c7393-febf-4945-82fa-81f97abb521c' class='xr-var-data-in' type='checkbox'><label for='data-f81c7393-febf-4945-82fa-81f97abb521c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
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-       "        </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-121d43f8-6826-40d5-b5d3-360a7b0e4686' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-121d43f8-6826-40d5-b5d3-360a7b0e4686' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51854346-65ad-45aa-9778-ee303a3189e8' class='xr-var-data-in' type='checkbox'><label for='data-51854346-65ad-45aa-9778-ee303a3189e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column ice water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_ice_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f69300b8-cda3-4aef-9ab2-16718377d77f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f69300b8-cda3-4aef-9ab2-16718377d77f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cf5fa5a-87bd-4385-bec3-1d66d9900a9f' class='xr-var-data-in' type='checkbox'><label for='data-5cf5fa5a-87bd-4385-bec3-1d66d9900a9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column liquid water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_liquid_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcwv</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-06f6d7d3-64db-4987-be4f-8ac84d5775d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06f6d7d3-64db-4987-be4f-8ac84d5775d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faf5ce68-8c5a-4a5a-bac9-275fa8ba239f' class='xr-var-data-in' type='checkbox'><label for='data-faf5ce68-8c5a-4a5a-bac9-275fa8ba239f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column water vapor</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_water_vapor</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-6f4c0dfd-85e8-4655-823f-1cfd47b8d69f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f4c0dfd-85e8-4655-823f-1cfd47b8d69f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12ebda5e-736c-4862-ab2e-72b2d9a0be81' class='xr-var-data-in' type='checkbox'><label for='data-12ebda5e-736c-4862-ab2e-72b2d9a0be81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 640 B </td>\n",
-       "                        <td> 16 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 2) </td>\n",
-       "                        <td> (1, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-e803d08a-b8c3-4e5d-bb57-cde7c7549d3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e803d08a-b8c3-4e5d-bb57-cde7c7549d3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79ec794c-0c2f-47e5-b49c-b32901433ea8' class='xr-var-data-in' type='checkbox'><label for='data-79ec794c-0c2f-47e5-b49c-b32901433ea8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 640 B </td>\n",
-       "                        <td> 16 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 2) </td>\n",
-       "                        <td> (1, 2) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f418e7e4-f321-4c2a-b0eb-514c6a49b7fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f418e7e4-f321-4c2a-b0eb-514c6a49b7fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b60e65c2-68e1-4791-a02c-922d2f8543e5' class='xr-var-data-in' type='checkbox'><label for='data-b60e65c2-68e1-4791-a02c-922d2f8543e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>toa incident solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_incoming_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
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-       "        </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c24b7fb7-4750-4040-a595-76cc4e616919' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c24b7fb7-4750-4040-a595-76cc4e616919' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe8a9e16-65b3-4088-a9f8-d20de5fcd6f5' class='xr-var-data-in' type='checkbox'><label for='data-fe8a9e16-65b3-4088-a9f8-d20de5fcd6f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total precipitation (rain+snow)</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-987805cf-8107-411d-9214-40db160b7f08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-987805cf-8107-411d-9214-40db160b7f08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-700c5e9f-130b-49a0-bf8a-81a05868df83' class='xr-var-data-in' type='checkbox'><label for='data-700c5e9f-130b-49a0-bf8a-81a05868df83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>temperature of snow layer</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-314da8c8-a820-4134-a4ce-a8d5a69d48e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-314da8c8-a820-4134-a4ce-a8d5a69d48e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a71d23b-8f92-44f7-bb50-6034e0444667' class='xr-var-data-in' type='checkbox'><label for='data-0a71d23b-8f92-44f7-bb50-6034e0444667' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-b89e6a0a-0180-41c9-8014-c5066972cd8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b89e6a0a-0180-41c9-8014-c5066972cd8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e834dc13-177f-4ffc-8999-723874f05499' class='xr-var-data-in' type='checkbox'><label for='data-e834dc13-177f-4ffc-8999-723874f05499' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f4e0620e-d378-4fec-81c5-e247e87da331' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4e0620e-d378-4fec-81c5-e247e87da331' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa56b1cf-25ed-4c0e-9e18-73e3156974fd' class='xr-var-data-in' type='checkbox'><label for='data-aa56b1cf-25ed-4c0e-9e18-73e3156974fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.97 GiB </td>\n",
-       "                        <td> 3.15 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (40, 6599680) </td>\n",
-       "                        <td> (1, 412480) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
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-       "                        <td> 3.15 MiB </td>\n",
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-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n",
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-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "               &#x27;2009-10-15 21:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n",
-       "               &#x27;2009-10-16 03:00:00&#x27;, &#x27;2009-10-16 06:00:00&#x27;,\n",
-       "               &#x27;2009-10-16 09:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n",
-       "               &#x27;2009-10-16 15:00:00&#x27;, &#x27;2009-10-16 18:00:00&#x27;,\n",
-       "               &#x27;2009-10-16 21:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n",
-       "               &#x27;2009-10-17 03:00:00&#x27;, &#x27;2009-10-17 06:00:00&#x27;,\n",
-       "               &#x27;2009-10-17 09:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n",
-       "               &#x27;2009-10-17 15:00:00&#x27;, &#x27;2009-10-17 18:00:00&#x27;,\n",
-       "               &#x27;2009-10-17 21:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n",
-       "               &#x27;2009-10-18 03:00:00&#x27;, &#x27;2009-10-18 06:00:00&#x27;,\n",
-       "               &#x27;2009-10-18 09:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n",
-       "               &#x27;2009-10-18 15:00:00&#x27;, &#x27;2009-10-18 18:00:00&#x27;,\n",
-       "               &#x27;2009-10-18 21:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n",
-       "               &#x27;2009-10-19 03:00:00&#x27;, &#x27;2009-10-19 06:00:00&#x27;,\n",
-       "               &#x27;2009-10-19 09:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n",
-       "               &#x27;2009-10-19 15:00:00&#x27;, &#x27;2009-10-19 18:00:00&#x27;,\n",
-       "               &#x27;2009-10-19 21:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n",
-       "               &#x27;2009-10-20 03:00:00&#x27;, &#x27;2009-10-20 06:00:00&#x27;,\n",
-       "               &#x27;2009-10-20 09:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-136a4085-e291-49ba-a426-e5f7f809b5d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-136a4085-e291-49ba-a426-e5f7f809b5d0' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_3h_reduced_sfc</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 10:55:20 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>dbac5691-165e-4465-bd75-e403aa400071</dd></dl></div></li></ul></div></div>"
+      "id": "5a574d92-3c0a-429b-97e8-d024d78c5daf",
+      "cell_type": "code",
+      "source": "ds_sfc = load_OpenIFS_data(leveltype=\"sfc\")\nds_sfc",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/HighResMIP_3h_reduced_sfc.nc\n",
+          "output_type": "stream"
+        },
+        {
+          "execution_count": 7,
+          "output_type": "execute_result",
+          "data": {
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2;\n}\n\n.xr-array-data,\n.xr-array-in:checked ~ .xr-array-preview {\n  display: none;\n}\n\n.xr-array-in:checked ~ .xr-array-data,\n.xr-array-preview {\n  display: inline-block;\n}\n\n.xr-dim-list {\n  display: inline-block !important;\n  list-style: none;\n  padding: 0 !important;\n  margin: 0;\n}\n\n.xr-dim-list li {\n  display: inline-block;\n  padding: 0;\n  margin: 0;\n}\n\n.xr-dim-list:before {\n  content: '(';\n}\n\n.xr-dim-list:after {\n  content: ')';\n}\n\n.xr-dim-list li:not(:last-child):after {\n  content: ',';\n  padding-right: 5px;\n}\n\n.xr-has-index {\n  font-weight: bold;\n}\n\n.xr-var-list,\n.xr-var-item {\n  display: contents;\n}\n\n.xr-var-item > div,\n.xr-var-item label,\n.xr-var-item > .xr-var-name span {\n  background-color: var(--xr-background-color-row-even);\n  margin-bottom: 0;\n}\n\n.xr-var-item > .xr-var-name:hover span {\n  padding-right: 5px;\n}\n\n.xr-var-list > li:nth-child(odd) > div,\n.xr-var-list > li:nth-child(odd) > label,\n.xr-var-list > li:nth-child(odd) > .xr-var-name span {\n  background-color: var(--xr-background-color-row-odd);\n}\n\n.xr-var-name {\n  grid-column: 1;\n}\n\n.xr-var-dims {\n  grid-column: 2;\n}\n\n.xr-var-dtype {\n  grid-column: 3;\n  text-align: right;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-preview {\n  grid-column: 4;\n}\n\n.xr-index-preview {\n  grid-column: 2 / 5;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-name,\n.xr-var-dims,\n.xr-var-dtype,\n.xr-preview,\n.xr-attrs dt {\n  white-space: nowrap;\n  overflow: hidden;\n  text-overflow: ellipsis;\n  padding-right: 10px;\n}\n\n.xr-var-name:hover,\n.xr-var-dims:hover,\n.xr-var-dtype:hover,\n.xr-attrs dt:hover {\n  overflow: visible;\n  width: auto;\n  z-index: 1;\n}\n\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  display: none;\n  background-color: var(--xr-background-color) !important;\n  padding-bottom: 5px !important;\n}\n\n.xr-var-attrs-in:checked ~ .xr-var-attrs,\n.xr-var-data-in:checked ~ .xr-var-data,\n.xr-index-data-in:checked ~ .xr-index-data {\n  display: block;\n}\n\n.xr-var-data > table {\n  float: right;\n}\n\n.xr-var-name span,\n.xr-var-data,\n.xr-index-name div,\n.xr-index-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2,\n.xr-no-icon {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 95GB\nDimensions:              (time_counter: 40, cell: 6599680, nvertex: 4,\n                          axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n  * time_counter         (time_counter) datetime64[ns] 320B 2009-10-15T15:00:...\n    time_instant         (time_counter) datetime64[ns] 320B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables: (12/49)\n    10u                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    10v                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    2d                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    2t                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    ...                   ...\n    tp                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsn                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    ttr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    ttrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_3h_reduced_sfc\n    timeStamp:    2024-Apr-05 10:55:20 GMT\n    title:        Created by xios\n    uuid:         dbac5691-165e-4465-bd75-e403aa400071</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cf5502f2-0115-4bbd-a1c7-b28c3d4815f6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf5502f2-0115-4bbd-a1c7-b28c3d4815f6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time_counter</span>: 40</li><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-414a3ac7-246a-4e05-a3ac-8df64fe03777' class='xr-section-summary-in' type='checkbox'  checked><label for='section-414a3ac7-246a-4e05-a3ac-8df64fe03777' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-c967b173-254e-491b-87ab-20d4a140bd5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c967b173-254e-491b-87ab-20d4a140bd5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-733d373f-d92f-4713-92b8-dc4dd5d6f9ab' class='xr-var-data-in' type='checkbox'><label for='data-733d373f-d92f-4713-92b8-dc4dd5d6f9ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-d1be764c-db39-4dc7-a7f5-ab21b07c1919' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1be764c-db39-4dc7-a7f5-ab21b07c1919' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-208b83ce-73c3-4ce6-9998-c3d652c1e481' class='xr-var-data-in' type='checkbox'><label for='data-208b83ce-73c3-4ce6-9998-c3d652c1e481' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T15:00:00 ... 2009-10-...</div><input id='attrs-6f5b7c67-eb3c-48d7-984d-17c0bda6f33f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f5b7c67-eb3c-48d7-984d-17c0bda6f33f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b76b7aa-2d61-4029-ac7c-5cd88e859ffc' class='xr-var-data-in' type='checkbox'><label for='data-1b76b7aa-2d61-4029-ac7c-5cd88e859ffc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T15:00:00.000000000&#x27;, &#x27;2009-10-15T18:00:00.000000000&#x27;,\n       &#x27;2009-10-15T21:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n       &#x27;2009-10-16T03:00:00.000000000&#x27;, &#x27;2009-10-16T06:00:00.000000000&#x27;,\n       &#x27;2009-10-16T09:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n       &#x27;2009-10-16T15:00:00.000000000&#x27;, &#x27;2009-10-16T18:00:00.000000000&#x27;,\n       &#x27;2009-10-16T21:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n       &#x27;2009-10-17T03:00:00.000000000&#x27;, &#x27;2009-10-17T06:00:00.000000000&#x27;,\n       &#x27;2009-10-17T09:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n       &#x27;2009-10-17T15:00:00.000000000&#x27;, &#x27;2009-10-17T18:00:00.000000000&#x27;,\n       &#x27;2009-10-17T21:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n       &#x27;2009-10-18T03:00:00.000000000&#x27;, &#x27;2009-10-18T06:00:00.000000000&#x27;,\n       &#x27;2009-10-18T09:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n       &#x27;2009-10-18T15:00:00.000000000&#x27;, &#x27;2009-10-18T18:00:00.000000000&#x27;,\n       &#x27;2009-10-18T21:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n       &#x27;2009-10-19T03:00:00.000000000&#x27;, &#x27;2009-10-19T06:00:00.000000000&#x27;,\n       &#x27;2009-10-19T09:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n       &#x27;2009-10-19T15:00:00.000000000&#x27;, &#x27;2009-10-19T18:00:00.000000000&#x27;,\n       &#x27;2009-10-19T21:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n       &#x27;2009-10-20T03:00:00.000000000&#x27;, &#x27;2009-10-20T06:00:00.000000000&#x27;,\n       &#x27;2009-10-20T09:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-f52f73b8-5d51-4eb5-80f1-128b4ac1dc62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f52f73b8-5d51-4eb5-80f1-128b4ac1dc62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60b136c9-1ce8-4ef1-95c9-5e75d0328ea6' class='xr-var-data-in' type='checkbox'><label for='data-60b136c9-1ce8-4ef1-95c9-5e75d0328ea6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"80\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"30\" style=\"stroke-width:2\" />\n  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"30\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"30\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"30\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"30\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"30\" />\n  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"30\" />\n  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"30\" />\n  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"30\" />\n  <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"30\" />\n  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"30\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"30\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"30\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"30\" />\n  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"30\" />\n  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"30\" />\n  <line x1=\"99\" y1=\"0\" x2=\"99\" y2=\"30\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"30\" />\n  <line x1=\"111\" y1=\"0\" x2=\"111\" y2=\"30\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"30\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,30.311399683662877 0.0,30.311399683662877\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"50.311400\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >40</text>\n  <text x=\"140.000000\" y=\"15.155700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,15.155700)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-058b2bb9-765b-45fe-8f59-ce9453d838b7' class='xr-section-summary-in' type='checkbox'  ><label for='section-058b2bb9-765b-45fe-8f59-ce9453d838b7' class='xr-section-summary' >Data variables: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-053884d9-001f-4365-8ca1-f035229a0712' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-053884d9-001f-4365-8ca1-f035229a0712' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-901280bc-70ee-4337-8d0a-23c0f2210af1' class='xr-var-data-in' type='checkbox'><label for='data-901280bc-70ee-4337-8d0a-23c0f2210af1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>u-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n 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x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-942e454b-813f-41d1-a2fc-2d2085f83c11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-942e454b-813f-41d1-a2fc-2d2085f83c11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02aa3e49-e0ce-4a20-922f-304debf47b25' class='xr-var-data-in' type='checkbox'><label for='data-02aa3e49-e0ce-4a20-922f-304debf47b25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>v-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0eeb0160-bc7d-4f32-a67c-11c8bb56f8bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0eeb0160-bc7d-4f32-a67c-11c8bb56f8bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fcd9587-1a53-40d7-950c-6043a0ccc17d' class='xr-var-data-in' type='checkbox'><label for='data-6fcd9587-1a53-40d7-950c-6043a0ccc17d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m dewpoint temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-2c4796ff-cf69-48fa-be6f-6116700229c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c4796ff-cf69-48fa-be6f-6116700229c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5886429d-61ee-4816-a543-bfa077858e9b' class='xr-var-data-in' type='checkbox'><label for='data-5886429d-61ee-4816-a543-bfa077858e9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m air temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-86eaef34-fdd5-4849-b9eb-f440f34fb3d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86eaef34-fdd5-4849-b9eb-f440f34fb3d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e4c2a71-0845-4e1b-b06b-30df16bbabb3' class='xr-var-data-in' type='checkbox'><label for='data-8e4c2a71-0845-4e1b-b06b-30df16bbabb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-b0182711-03bd-4c7e-815a-4ceb7cbae700' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0182711-03bd-4c7e-815a-4ceb7cbae700' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ee00f31-a2a4-4183-a4fb-03803e80a544' class='xr-var-data-in' type='checkbox'><label for='data-9ee00f31-a2a4-4183-a4fb-03803e80a544' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e5506d9b-4838-4ce1-9a4e-8398a79604c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5506d9b-4838-4ce1-9a4e-8398a79604c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2ee0ead-31f9-4ef5-9784-3f012790330b' class='xr-var-data-in' type='checkbox'><label for='data-e2ee0ead-31f9-4ef5-9784-3f012790330b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>convective precipitation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" 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x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c1039620-a6f6-4f48-879e-c0a83c71af12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1039620-a6f6-4f48-879e-c0a83c71af12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e32fab3e-cb99-40e8-80c7-79db8cc9f202' class='xr-var-data-in' type='checkbox'><label for='data-e32fab3e-cb99-40e8-80c7-79db8cc9f202' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-9416e0d0-d691-4604-8488-e1a3058de6ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9416e0d0-d691-4604-8488-e1a3058de6ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-462d6d88-7a98-4e40-8ce5-84d7271e2b0b' class='xr-var-data-in' type='checkbox'><label for='data-462d6d88-7a98-4e40-8ce5-84d7271e2b0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e1f34204-ef0c-437b-bd8b-1ee2d08b16a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1f34204-ef0c-437b-bd8b-1ee2d08b16a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b17b8c8-ce70-4eec-bfc2-8cdd57622cf2' class='xr-var-data-in' type='checkbox'><label for='data-5b17b8c8-ce70-4eec-bfc2-8cdd57622cf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>eastward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_eastward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-050e584d-6288-4b23-a68a-90bb162d0a63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-050e584d-6288-4b23-a68a-90bb162d0a63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a04866c0-6d04-467f-b4d5-2abb01265fd9' class='xr-var-data-in' type='checkbox'><label for='data-a04866c0-6d04-467f-b4d5-2abb01265fd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_vel</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-da32fc80-a666-4c7b-a6d3-66e473f2f463' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da32fc80-a666-4c7b-a6d3-66e473f2f463' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f8a58e3-1f90-43cc-aca6-f48a656947c3' class='xr-var-data-in' type='checkbox'><label for='data-7f8a58e3-1f90-43cc-aca6-f48a656947c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>northward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_northward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-24f5dcb0-f905-4f7e-bf27-6412efebb26c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24f5dcb0-f905-4f7e-bf27-6412efebb26c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7651e487-3e95-4cac-a188-56ef39cadd05' class='xr-var-data-in' type='checkbox'><label for='data-7651e487-3e95-4cac-a188-56ef39cadd05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-53e802d7-18f3-475a-a8d9-5ee00b29401a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53e802d7-18f3-475a-a8d9-5ee00b29401a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f53b93d-1bf8-4a97-97fd-e25a539f85f1' class='xr-var-data-in' type='checkbox'><label for='data-6f53b93d-1bf8-4a97-97fd-e25a539f85f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow density</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_density</dd><dt><span>units :</span></dt><dd>kg m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e1e171fb-8811-44f2-b988-61c8764a54ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1e171fb-8811-44f2-b988-61c8764a54ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94b96bfa-5215-4be1-a065-fc930551510b' class='xr-var-data-in' type='checkbox'><label for='data-94b96bfa-5215-4be1-a065-fc930551510b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow depth</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  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y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-05b8c2fc-213b-47f1-944d-acf570082be7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05b8c2fc-213b-47f1-944d-acf570082be7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-303d0201-6fbe-4b45-84c6-81d98d65fc09' class='xr-var-data-in' type='checkbox'><label for='data-303d0201-6fbe-4b45-84c6-81d98d65fc09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snowfall</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-fca4af65-1436-4763-ac14-d8c046fb9d5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fca4af65-1436-4763-ac14-d8c046fb9d5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d240581a-07fc-4b56-ae79-6304e9fa472e' class='xr-var-data-in' type='checkbox'><label for='data-d240581a-07fc-4b56-ae79-6304e9fa472e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>skin temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>sea_surface_skin_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-7fad9fff-a35f-4bdc-ac9d-136cefb291eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fad9fff-a35f-4bdc-ac9d-136cefb291eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-973378be-6302-456e-87ea-56af88dd22df' class='xr-var-data-in' type='checkbox'><label for='data-973378be-6302-456e-87ea-56af88dd22df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_latent_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0ba66fdf-fbf8-407f-9ae2-79cf0856178c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ba66fdf-fbf8-407f-9ae2-79cf0856178c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007d7ef3-5e3c-4dd4-bf22-d5ed8a8e3f91' class='xr-var-data-in' type='checkbox'><label for='data-007d7ef3-5e3c-4dd4-bf22-d5ed8a8e3f91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow melt</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_snow_melt_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5192b1ac-4795-4a3c-b93a-fdaeed38e9e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5192b1ac-4795-4a3c-b93a-fdaeed38e9e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cebd13c6-e7cf-43d5-9043-812317bcbdd2' class='xr-var-data-in' type='checkbox'><label for='data-cebd13c6-e7cf-43d5-9043-812317bcbdd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-ed2de033-e004-4ad5-a810-c0202fb2aa43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed2de033-e004-4ad5-a810-c0202fb2aa43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b239018-c5bd-46b0-8f5f-81ae306472a2' class='xr-var-data-in' type='checkbox'><label for='data-8b239018-c5bd-46b0-8f5f-81ae306472a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  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<text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e6b15a3f-8987-4437-bb8a-a9f4f9460852' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6b15a3f-8987-4437-bb8a-a9f4f9460852' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c21b8f5e-7136-4b7d-9f90-fc57e890ef78' class='xr-var-data-in' type='checkbox'><label for='data-c21b8f5e-7136-4b7d-9f90-fc57e890ef78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_sensible_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-dcc94509-75cf-48ac-b8c1-3a3fd6f19198' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcc94509-75cf-48ac-b8c1-3a3fd6f19198' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eca27506-fe62-4431-9d57-3870080f26d3' class='xr-var-data-in' type='checkbox'><label for='data-eca27506-fe62-4431-9d57-3870080f26d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-41e22af4-efdd-4bea-9e77-6f1e457c13cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41e22af4-efdd-4bea-9e77-6f1e457c13cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f1053c8-e687-42b1-a8af-aee95ec75dcf' class='xr-var-data-in' type='checkbox'><label for='data-6f1053c8-e687-42b1-a8af-aee95ec75dcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-dc2fe04e-7baf-4f89-bed5-2ae327621b09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc2fe04e-7baf-4f89-bed5-2ae327621b09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62b8e45b-5368-4c6b-82c5-4d69ec293905' class='xr-var-data-in' type='checkbox'><label for='data-62b8e45b-5368-4c6b-82c5-4d69ec293905' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-47a10975-0f76-4dd7-bda0-99ef9003e54f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47a10975-0f76-4dd7-bda0-99ef9003e54f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d445a8b-a747-4216-9c7f-e75fc2560a2c' class='xr-var-data-in' type='checkbox'><label for='data-5d445a8b-a747-4216-9c7f-e75fc2560a2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_1</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-71c72911-7467-4e0e-8640-b4fadf50e96c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71c72911-7467-4e0e-8640-b4fadf50e96c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0b5f07b-2ea5-44cb-a3d6-67adb500ed3e' class='xr-var-data-in' type='checkbox'><label for='data-f0b5f07b-2ea5-44cb-a3d6-67adb500ed3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_2</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-37c547ca-fc5a-42d9-bd3f-74e8826faa1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37c547ca-fc5a-42d9-bd3f-74e8826faa1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d62c142-1403-40b9-98e6-012966b05929' class='xr-var-data-in' type='checkbox'><label for='data-7d62c142-1403-40b9-98e6-012966b05929' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_3</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-56d9895f-3580-4035-b12b-ba91143b0e36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56d9895f-3580-4035-b12b-ba91143b0e36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69dc5f23-3cf5-442d-af82-fc84697e17a6' class='xr-var-data-in' type='checkbox'><label for='data-69dc5f23-3cf5-442d-af82-fc84697e17a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 4</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_4</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4271045a-0013-4bf9-922b-a92c9ef3eb37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4271045a-0013-4bf9-922b-a92c9ef3eb37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e675fdc-7934-488a-b6ec-7107480b4ef3' class='xr-var-data-in' type='checkbox'><label for='data-1e675fdc-7934-488a-b6ec-7107480b4ef3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c754f1ce-352e-49a9-9a8f-0c5cfae0c73b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c754f1ce-352e-49a9-9a8f-0c5cfae0c73b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1680699-7fef-4017-bff7-76167aa2c879' class='xr-var-data-in' type='checkbox'><label for='data-b1680699-7fef-4017-bff7-76167aa2c879' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-609ca016-313d-40ea-8099-8fdf57aacfbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-609ca016-313d-40ea-8099-8fdf57aacfbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a02dcd40-6583-4e18-9c09-cc935cdd1fc6' class='xr-var-data-in' type='checkbox'><label for='data-a02dcd40-6583-4e18-9c09-cc935cdd1fc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-642b8ed0-3df9-4791-a385-136fc10561c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-642b8ed0-3df9-4791-a385-136fc10561c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51786d20-b7b4-45ae-95d7-b1c017da68d3' class='xr-var-data-in' type='checkbox'><label for='data-51786d20-b7b4-45ae-95d7-b1c017da68d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_1</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-caa734b6-24bb-4bdd-9a76-4f24ec65e89a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-caa734b6-24bb-4bdd-9a76-4f24ec65e89a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d8b25e2-06f5-44d3-8a8d-98d03689ae82' class='xr-var-data-in' type='checkbox'><label for='data-6d8b25e2-06f5-44d3-8a8d-98d03689ae82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_2</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-3ccedc0b-2c86-40d3-b62e-8708383ed5f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ccedc0b-2c86-40d3-b62e-8708383ed5f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac32b567-d3f7-4be4-a935-2373121c41c8' class='xr-var-data-in' type='checkbox'><label for='data-ac32b567-d3f7-4be4-a935-2373121c41c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_3</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-09bb25a1-e1f6-4f71-a03c-13046517e497' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09bb25a1-e1f6-4f71-a03c-13046517e497' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17c11ac2-db48-4dba-9462-7ab657d2ac6c' class='xr-var-data-in' type='checkbox'><label for='data-17c11ac2-db48-4dba-9462-7ab657d2ac6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 4</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_4</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-15689a4e-f740-40da-8507-3077d647104e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15689a4e-f740-40da-8507-3077d647104e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3054d05-af23-4022-9878-e4038380c529' class='xr-var-data-in' type='checkbox'><label for='data-f3054d05-af23-4022-9878-e4038380c529' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-fa5a8f40-109f-4f26-acf8-c09f21cdc9f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa5a8f40-109f-4f26-acf8-c09f21cdc9f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92796599-c1eb-4fbe-9078-13fea0eec446' class='xr-var-data-in' type='checkbox'><label for='data-92796599-c1eb-4fbe-9078-13fea0eec446' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column ice water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_ice_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-8d4f6c4a-2ef9-4e0e-8d3b-3e41db534a67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d4f6c4a-2ef9-4e0e-8d3b-3e41db534a67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ff3fd96-5550-4202-8ed6-d61596607fc3' class='xr-var-data-in' type='checkbox'><label for='data-6ff3fd96-5550-4202-8ed6-d61596607fc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column liquid water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_liquid_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-361d9942-08af-4af6-877a-64c012164d63' class='xr-var-data-in' type='checkbox'><label for='data-361d9942-08af-4af6-877a-64c012164d63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column water vapor</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_water_vapor</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-f8ca7cca-c016-492a-a11e-9bf1029da298' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8ca7cca-c016-492a-a11e-9bf1029da298' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23865c11-a561-4514-af57-a0323bb93ee7' class='xr-var-data-in' type='checkbox'><label for='data-23865c11-a561-4514-af57-a0323bb93ee7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"34\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"34\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"34\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"34\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"34\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"34\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"34\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"34\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"34\" y2=\"54\" />\n  <line x1=\"0\" y1=\"63\" x2=\"34\" y2=\"63\" />\n  <line x1=\"0\" y1=\"69\" x2=\"34\" y2=\"69\" />\n  <line x1=\"0\" y1=\"75\" x2=\"34\" y2=\"75\" />\n  <line x1=\"0\" y1=\"81\" x2=\"34\" y2=\"81\" />\n  <line x1=\"0\" y1=\"87\" x2=\"34\" y2=\"87\" />\n  <line x1=\"0\" y1=\"93\" x2=\"34\" y2=\"93\" />\n  <line x1=\"0\" y1=\"99\" x2=\"34\" y2=\"99\" />\n  <line x1=\"0\" y1=\"105\" x2=\"34\" y2=\"105\" />\n  <line x1=\"0\" y1=\"111\" x2=\"34\" y2=\"111\" />\n  <line x1=\"0\" y1=\"120\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 34.50101617379934,0.0 34.50101617379934,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"17.250508\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"54.501016\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,54.501016,60.000000)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-f6d0505e-c216-46d6-a1b4-0d038dbfce58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6d0505e-c216-46d6-a1b4-0d038dbfce58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dd5d10a-cbff-4eaf-8f14-47923699bb03' class='xr-var-data-in' type='checkbox'><label for='data-1dd5d10a-cbff-4eaf-8f14-47923699bb03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"34\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"34\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"34\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"34\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"34\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"34\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"34\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"34\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"34\" y2=\"54\" />\n  <line x1=\"0\" y1=\"63\" x2=\"34\" y2=\"63\" />\n  <line x1=\"0\" y1=\"69\" x2=\"34\" y2=\"69\" />\n  <line x1=\"0\" y1=\"75\" x2=\"34\" y2=\"75\" />\n  <line x1=\"0\" y1=\"81\" x2=\"34\" y2=\"81\" />\n  <line x1=\"0\" y1=\"87\" x2=\"34\" y2=\"87\" />\n  <line x1=\"0\" y1=\"93\" x2=\"34\" y2=\"93\" />\n  <line x1=\"0\" y1=\"99\" x2=\"34\" y2=\"99\" />\n  <line x1=\"0\" y1=\"105\" x2=\"34\" y2=\"105\" />\n  <line x1=\"0\" y1=\"111\" x2=\"34\" y2=\"111\" />\n  <line x1=\"0\" y1=\"120\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 34.50101617379934,0.0 34.50101617379934,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"17.250508\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"54.501016\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,54.501016,60.000000)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-d8a9bec6-bfaf-40c1-9844-78c786b968e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8a9bec6-bfaf-40c1-9844-78c786b968e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68e4b142-638e-4a95-9376-69374ce758de' class='xr-var-data-in' type='checkbox'><label for='data-68e4b142-638e-4a95-9376-69374ce758de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>toa incident solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_incoming_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-feb5a025-e5ee-4e2d-88a2-ea1ca1cc5993' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-feb5a025-e5ee-4e2d-88a2-ea1ca1cc5993' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b6f95da-54cb-49bd-8336-cc77ce57fc97' class='xr-var-data-in' type='checkbox'><label for='data-6b6f95da-54cb-49bd-8336-cc77ce57fc97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total precipitation (rain+snow)</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a080f8fc-92f2-47ef-ad25-a7af27a19829' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a080f8fc-92f2-47ef-ad25-a7af27a19829' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e22f3cb0-f5dc-4d2d-93ea-ba7a3a25751e' class='xr-var-data-in' type='checkbox'><label for='data-e22f3cb0-f5dc-4d2d-93ea-ba7a3a25751e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>temperature of snow layer</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-264e742c-5c6f-4571-9ee1-3266adb473d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-264e742c-5c6f-4571-9ee1-3266adb473d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c912ce87-249d-4f5e-805d-26bd6ff1e8af' class='xr-var-data-in' type='checkbox'><label for='data-c912ce87-249d-4f5e-805d-26bd6ff1e8af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  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 <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c01bd702-0705-4348-885e-6717b8b08d8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c01bd702-0705-4348-885e-6717b8b08d8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09818294-467f-4aa3-b827-27e23a1dd7da' class='xr-var-data-in' type='checkbox'><label for='data-09818294-467f-4aa3-b827-27e23a1dd7da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-34c8e6d2-dede-4c63-9d4d-94c9018c3bc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34c8e6d2-dede-4c63-9d4d-94c9018c3bc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cf331b9-f124-4459-85ad-8a2d7f746ec9' class='xr-var-data-in' type='checkbox'><label for='data-6cf331b9-f124-4459-85ad-8a2d7f746ec9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f8630536-e736-4741-adaf-41bfe5652d92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8630536-e736-4741-adaf-41bfe5652d92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e23e1eb6-07dc-45c3-b991-5badc849d380' class='xr-var-data-in' type='checkbox'><label for='data-e23e1eb6-07dc-45c3-b991-5badc849d380' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" 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for='section-40390ac9-1934-4f07-ad95-7386b01eb161' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5c8e01fc-3316-4860-985d-43792c6479ac' class='xr-index-data-in' type='checkbox'/><label for='index-5c8e01fc-3316-4860-985d-43792c6479ac' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 15:00:00&#x27;, &#x27;2009-10-15 18:00:00&#x27;,\n               &#x27;2009-10-15 21:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n               &#x27;2009-10-16 03:00:00&#x27;, &#x27;2009-10-16 06:00:00&#x27;,\n               &#x27;2009-10-16 09:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n               &#x27;2009-10-16 15:00:00&#x27;, &#x27;2009-10-16 18:00:00&#x27;,\n               &#x27;2009-10-16 21:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n               &#x27;2009-10-17 03:00:00&#x27;, &#x27;2009-10-17 06:00:00&#x27;,\n               &#x27;2009-10-17 09:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n               &#x27;2009-10-17 15:00:00&#x27;, &#x27;2009-10-17 18:00:00&#x27;,\n               &#x27;2009-10-17 21:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n               &#x27;2009-10-18 03:00:00&#x27;, &#x27;2009-10-18 06:00:00&#x27;,\n               &#x27;2009-10-18 09:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n               &#x27;2009-10-18 15:00:00&#x27;, &#x27;2009-10-18 18:00:00&#x27;,\n               &#x27;2009-10-18 21:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n               &#x27;2009-10-19 03:00:00&#x27;, &#x27;2009-10-19 06:00:00&#x27;,\n               &#x27;2009-10-19 09:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n               &#x27;2009-10-19 15:00:00&#x27;, &#x27;2009-10-19 18:00:00&#x27;,\n               &#x27;2009-10-19 21:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n               &#x27;2009-10-20 03:00:00&#x27;, &#x27;2009-10-20 06:00:00&#x27;,\n               &#x27;2009-10-20 09:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8cf39ece-daca-46bc-904e-f6463873d9a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8cf39ece-daca-46bc-904e-f6463873d9a5' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_3h_reduced_sfc</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 10:55:20 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>dbac5691-165e-4465-bd75-e403aa400071</dd></dl></div></li></ul></div></div>"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 95GB\n",
-       "Dimensions:              (time_counter: 40, cell: 6599680, nvertex: 4,\n",
-       "                          axis_nbounds: 2)\n",
-       "Coordinates:\n",
-       "    lat                  (cell) float32 26MB dask.array<chunksize=(824960,), meta=np.ndarray>\n",
-       "    lon                  (cell) float32 26MB dask.array<chunksize=(824960,), meta=np.ndarray>\n",
-       "  * time_counter         (time_counter) datetime64[ns] 320B 2009-10-15T15:00:...\n",
-       "    time_instant         (time_counter) datetime64[ns] 320B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "Dimensions without coordinates: cell, nvertex, axis_nbounds\n",
-       "Data variables: (12/49)\n",
-       "    10u                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
-       "    10v                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
-       "    2d                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
-       "    2t                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
-       "    bounds_lat           (cell, nvertex) float32 106MB dask.array<chunksize=(206240, 4), meta=np.ndarray>\n",
-       "    bounds_lon           (cell, nvertex) float32 106MB dask.array<chunksize=(206240, 4), meta=np.ndarray>\n",
-       "    ...                   ...\n",
-       "    tp                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
-       "    tsn                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
-       "    tsr                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
-       "    tsrc                 (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
-       "    ttr                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
-       "    ttrc                 (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.6\n",
-       "    description:  Created by xios\n",
-       "    name:         HighResMIP_3h_reduced_sfc\n",
-       "    timeStamp:    2024-Apr-05 10:55:20 GMT\n",
-       "    title:        Created by xios\n",
-       "    uuid:         dbac5691-165e-4465-bd75-e403aa400071"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_sfc = load_OpenIFS_data(leveltype=\"sfc\")\n",
-    "ds_sfc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32f314be-e6dc-4a81-b12c-c740fcc043d9",
-   "metadata": {},
-   "source": [
-    "The data can be visualized as demonstrated below. For example, the 2 metre temperature on **2009-10-20 at 12:00:00 UTC** is plotted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "36e11d63-baec-4897-a5d1-26195cf5545f",
-   "metadata": {},
-   "outputs": [
+      "execution_count": 7
+    },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "id": "32f314be-e6dc-4a81-b12c-c740fcc043d9",
+      "cell_type": "markdown",
+      "source": "The data can be visualized as demonstrated below. For example, the 2 metre temperature on **2009-10-20 at 12:00:00 UTC** is plotted.",
+      "metadata": {}
+    },
+    {
+      "id": "36e11d63-baec-4897-a5d1-26195cf5545f",
+      "cell_type": "code",
+      "source": "earthkit.plots.quickmap.plot(\n    ds_sfc[\"2t\"].rename(dict(time_counter=\"time\")).sel(\n        time=\"2009-10-20T12:00:00\",\n    )[::10], x=\"lon\", y=\"lat\",\n);",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 700x600 with 2 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 8
+    },
+    {
+      "id": "e909c9f3-42f8-46d6-8d04-8f0a15106545",
+      "cell_type": "code",
+      "source": "",
+      "metadata": {},
+      "outputs": [],
+      "execution_count": null
     }
-   ],
-   "source": [
-    "earthkit.plots.quickmap.plot(\n",
-    "    ds_sfc[\"2t\"].rename(dict(time_counter=\"time\")).sel(\n",
-    "        time=\"2009-10-20T12:00:00\",\n",
-    "    )[::10], x=\"lon\", y=\"lat\",\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e909c9f3-42f8-46d6-8d04-8f0a15106545",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  ]
+}
\ No newline at end of file

From d07ee3a332a7607057d3e918b5a1b996b916d16b Mon Sep 17 00:00:00 2001
From: Sara Faghih-Naini <sara.faghihnaini@ecmwf.int>
Date: Mon, 9 Dec 2024 11:36:32 +0100
Subject: [PATCH 3/8] Fix Typo

---
 04-example-datasets/02-OpenIFS.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/04-example-datasets/02-OpenIFS.ipynb b/04-example-datasets/02-OpenIFS.ipynb
index 97e66f6..bcb9a0f 100644
--- a/04-example-datasets/02-OpenIFS.ipynb
+++ b/04-example-datasets/02-OpenIFS.ipynb
@@ -36,7 +36,7 @@
     {
       "id": "463bf2c0-ac3f-4786-9687-362393802dce",
       "cell_type": "markdown",
-      "source": "This notebook is a comprehensive guide to accessing NetDCF files for an OpenIFS simulation. It includes details about the available parameters, levels, and steps, along with instructions for using the `load_OpenIFS_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point output from the OpenIFS 43r3 model.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco1279), corresponding to a horizontal resolution of approximately 9 km.\nThe 5-day forecast was run with a time step length of 600 seconds using a HighResMIP based output configuration. \nThe total volume of the available dataset is about 258 GB.\n\n\nThere is a single NetCDF file for the surface level fields and separate files for each pressure level field.\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in Pa):\n```python\n\n[100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 25000, 20000, 15000, 10000, 7000, 5000, 3000, 2000, 1000, 500, 100]\n\n```\nThe available time steps start at 2009-10-15 18:00:00 UTC, increasing in 6-hour steps to 2009-10-20 12:00:00 UTC.\n\n### Surface Level Parameters\n\nThe following parameters are available as surface level fields:\n```python\n[\"10u\", \"10v\", \"2d\", \"2t\", \"cp\", \"e\", \"es\", \"ewss\", \"msl\", \"nsss\", \"ro\", \"rsn\", \"sd\", \"sf\", \"skt\", \"slhf\", \"smlt\", \"sp\", \"sro\", \"sshf\", \"ssr\", \"ssrc\", \"ssrd\", \"stl1\", \"stl2\", \"stl3\", \"stl4\", \"str\", \"strc\", \"strd\", \"swvl1\", \"swvl2\", \"swvl3\", \"swvl4\", \"tcc\", \"tciw\", \"tclw\", \"tcwv\", \"tisr\", \"tp\", \"tsn\", \"tsr\", \"tsrc\", \"ttr\", \"ttrc\"]\n```\n\nHere, the available time steps start at 2009-10-15 15:00:00 UTC, increasing in 3-hour steps to 2009-10-20 12:00:00 UTC.",
+      "source": "This notebook is a comprehensive guide to accessing NetCDF files for an OpenIFS simulation. It includes details about the available parameters, levels, and steps, along with instructions for using the `load_OpenIFS_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point output from the OpenIFS 43r3 model.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco1279), corresponding to a horizontal resolution of approximately 9 km.\nThe 5-day forecast was run with a time step length of 600 seconds using a HighResMIP based output configuration. \nThe total volume of the available dataset is about 258 GB.\n\n\nThere is a single NetCDF file for the surface level fields and separate files for each pressure level field.\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in Pa):\n```python\n\n[100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 25000, 20000, 15000, 10000, 7000, 5000, 3000, 2000, 1000, 500, 100]\n\n```\nThe available time steps start at 2009-10-15 18:00:00 UTC, increasing in 6-hour steps to 2009-10-20 12:00:00 UTC.\n\n### Surface Level Parameters\n\nThe following parameters are available as surface level fields:\n```python\n[\"10u\", \"10v\", \"2d\", \"2t\", \"cp\", \"e\", \"es\", \"ewss\", \"msl\", \"nsss\", \"ro\", \"rsn\", \"sd\", \"sf\", \"skt\", \"slhf\", \"smlt\", \"sp\", \"sro\", \"sshf\", \"ssr\", \"ssrc\", \"ssrd\", \"stl1\", \"stl2\", \"stl3\", \"stl4\", \"str\", \"strc\", \"strd\", \"swvl1\", \"swvl2\", \"swvl3\", \"swvl4\", \"tcc\", \"tciw\", \"tclw\", \"tcwv\", \"tisr\", \"tp\", \"tsn\", \"tsr\", \"tsrc\", \"ttr\", \"ttrc\"]\n```\n\nHere, the available time steps start at 2009-10-15 15:00:00 UTC, increasing in 3-hour steps to 2009-10-20 12:00:00 UTC.",
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@@ -123,7 +123,7 @@
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 20GB\nDimensions:              (cell: 6599680, nvertex: 4, pressure_levels: 19,\n                          time_counter: 20, axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n  * pressure_levels      (pressure_levels) float32 76B 1e+05 9.25e+04 ... 100.0\n  * time_counter         (time_counter) datetime64[ns] 160B 2009-10-15T18:00:...\n    time_instant         (time_counter) datetime64[ns] 160B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables:\n    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    q                    (time_counter, pressure_levels, cell) float64 20GB dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;\n    time_counter_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n    time_instant_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_6h_reduced_pl_q\n    timeStamp:    2024-Apr-05 11:05:21 GMT\n    title:        Created by xios\n    uuid:         25d5f0a2-ceae-47e1-936f-5b1804228ca6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e42b74ad-13ad-40b7-a6fa-913d8ca96a7b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e42b74ad-13ad-40b7-a6fa-913d8ca96a7b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span class='xr-has-index'>pressure_levels</span>: 19</li><li><span class='xr-has-index'>time_counter</span>: 20</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-42639ae1-97c6-4617-b9b9-bf6ff48b7046' class='xr-section-summary-in' type='checkbox'  checked><label for='section-42639ae1-97c6-4617-b9b9-bf6ff48b7046' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div 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 <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-22bc7e6f-7824-42cf-af27-e98a9d1aac6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22bc7e6f-7824-42cf-af27-e98a9d1aac6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6e94758-2804-4f05-9b1c-5a1ec542e82b' class='xr-var-data-in' type='checkbox'><label for='data-c6e94758-2804-4f05-9b1c-5a1ec542e82b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure_levels</span></div><div class='xr-var-dims'>(pressure_levels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.25e+04 ... 500.0 100.0</div><input id='attrs-30f50269-45de-40e2-b70f-727a84d0a1bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30f50269-45de-40e2-b70f-727a84d0a1bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad55ae81-a575-4aff-9b57-144b6e0aaf84' class='xr-var-data-in' type='checkbox'><label for='data-ad55ae81-a575-4aff-9b57-144b6e0aaf84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertical pressure levels</dd><dt><span>name :</span></dt><dd>pressure_levels</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  92500.,  85000.,  70000.,  60000.,  50000.,  40000.,  30000.,\n        25000.,  20000.,  15000.,  10000.,   7000.,   5000.,   3000.,   2000.,\n         1000.,    500.,    100.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T18:00:00 ... 2009-10-...</div><input id='attrs-39057a92-d87a-406f-a3a4-eb16e81b3cfd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39057a92-d87a-406f-a3a4-eb16e81b3cfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a5d1f4d-49ab-4f11-9c2a-8eb85b36ad98' class='xr-var-data-in' type='checkbox'><label for='data-0a5d1f4d-49ab-4f11-9c2a-8eb85b36ad98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T18:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n       &#x27;2009-10-16T06:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n       &#x27;2009-10-16T18:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n       &#x27;2009-10-17T06:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n       &#x27;2009-10-17T18:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n       &#x27;2009-10-18T06:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n       &#x27;2009-10-18T18:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n       &#x27;2009-10-19T06:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n       &#x27;2009-10-19T18:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n       &#x27;2009-10-20T06:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-84bf27d4-e162-445b-9a60-aa788595c479' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84bf27d4-e162-445b-9a60-aa788595c479' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ba09abb-f474-41d6-b78c-2d8e12b1638b' class='xr-var-data-in' type='checkbox'><label for='data-5ba09abb-f474-41d6-b78c-2d8e12b1638b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 160 B </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"84\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"34\" style=\"stroke-width:2\" />\n  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"34\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"34\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"34\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"34\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"34\" />\n  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"34\" />\n  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"34\" />\n  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"34\" />\n  <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"34\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"34\" />\n  <line x1=\"66\" y1=\"0\" x2=\"66\" y2=\"34\" />\n  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"34\" />\n  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"34\" />\n  <line x1=\"84\" y1=\"0\" x2=\"84\" y2=\"34\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"34\" />\n  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"34\" />\n  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"34\" />\n  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"34\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,34.50101617379934 0.0,34.50101617379934\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"54.501016\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >20</text>\n  <text x=\"140.000000\" y=\"17.250508\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,17.250508)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6e5fd37-085e-4167-9317-0356d58eff60' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6e5fd37-085e-4167-9317-0356d58eff60' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-14588162-7924-4116-81b8-2a203f4877ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14588162-7924-4116-81b8-2a203f4877ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-945af095-deb0-4e97-8bda-a3007d11cf26' class='xr-var-data-in' type='checkbox'><label for='data-945af095-deb0-4e97-8bda-a3007d11cf26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-41235bd4-b89b-40cc-8895-61e207931ae5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41235bd4-b89b-40cc-8895-61e207931ae5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3607e7d-559d-4393-9001-d83ff41ab62f' class='xr-var-data-in' type='checkbox'><label for='data-e3607e7d-559d-4393-9001-d83ff41ab62f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" 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x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time_counter, pressure_levels, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;</div><input id='attrs-8a01df3a-3357-4d5b-90ac-cf8d19c70aba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a01df3a-3357-4d5b-90ac-cf8d19c70aba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fe841b1-3566-4b38-b4cc-88b0af48a17f' class='xr-var-data-in' type='checkbox'><label for='data-1fe841b1-3566-4b38-b4cc-88b0af48a17f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>6 h</dd><dt><span>interval_write :</span></dt><dd>6 h</dd><dt><span>long_name :</span></dt><dd>specific humidity</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg*kg-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.69 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 19, 6599680) </td>\n                        <td> (1, 1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 6080 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"1\" x2=\"24\" y2=\"16\" />\n  <line x1=\"10\" y1=\"2\" x2=\"24\" 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<line x1=\"69\" y1=\"14\" x2=\"69\" y2=\"40\" />\n  <line x1=\"77\" y1=\"14\" x2=\"77\" y2=\"40\" />\n  <line x1=\"84\" y1=\"14\" x2=\"84\" y2=\"40\" />\n  <line x1=\"92\" y1=\"14\" x2=\"92\" y2=\"40\" />\n  <line x1=\"99\" y1=\"14\" x2=\"99\" y2=\"40\" />\n  <line x1=\"107\" y1=\"14\" x2=\"107\" y2=\"40\" />\n  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"40\" />\n  <line x1=\"122\" y1=\"14\" x2=\"122\" y2=\"40\" />\n  <line x1=\"129\" y1=\"14\" x2=\"129\" y2=\"40\" />\n  <line x1=\"137\" y1=\"14\" x2=\"137\" y2=\"40\" />\n  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,40.36121446433689 24.9485979497544,40.36121446433689\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">19</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-2ad7239e-1173-4712-be56-8d2a6d721b73' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ad7239e-1173-4712-be56-8d2a6d721b73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f49a5407-4f34-4cf4-9cfe-ddd41034eabd' class='xr-var-data-in' type='checkbox'><label for='data-f49a5407-4f34-4cf4-9cfe-ddd41034eabd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"38\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"38\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"38\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"38\" y2=\"54\" />\n  <line x1=\"0\" y1=\"60\" x2=\"38\" y2=\"60\" />\n  <line x1=\"0\" y1=\"66\" x2=\"38\" y2=\"66\" />\n  <line x1=\"0\" y1=\"72\" x2=\"38\" y2=\"72\" />\n  <line x1=\"0\" y1=\"78\" x2=\"38\" y2=\"78\" />\n  <line x1=\"0\" y1=\"84\" x2=\"38\" y2=\"84\" />\n  <line x1=\"0\" y1=\"90\" x2=\"38\" y2=\"90\" />\n  <line x1=\"0\" y1=\"96\" x2=\"38\" y2=\"96\" />\n  <line x1=\"0\" y1=\"102\" x2=\"38\" y2=\"102\" />\n  <line x1=\"0\" y1=\"108\" x2=\"38\" y2=\"108\" />\n  <line x1=\"0\" y1=\"120\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 38.596863036086,0.0 38.596863036086,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"19.298432\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"58.596863\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,58.596863,60.000000)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-bbc9df44-d05d-4fbc-91a6-7a25cf55d9ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bbc9df44-d05d-4fbc-91a6-7a25cf55d9ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32f95e28-23e6-4d61-80fe-b99568464454' class='xr-var-data-in' type='checkbox'><label for='data-32f95e28-23e6-4d61-80fe-b99568464454' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"38\" 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0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"19.298432\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"58.596863\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,58.596863,60.000000)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7951bec1-6e13-4106-8210-ad8fa7734ca8' class='xr-section-summary-in' type='checkbox'  ><label for='section-7951bec1-6e13-4106-8210-ad8fa7734ca8' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>pressure_levels</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-da077648-6a7f-45d7-8d98-351697bd0c41' class='xr-index-data-in' type='checkbox'/><label for='index-da077648-6a7f-45d7-8d98-351697bd0c41' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000.0,  92500.0,  85000.0,  70000.0,  60000.0,  50000.0,  40000.0,\n        30000.0,  25000.0,  20000.0,  15000.0,  10000.0,   7000.0,   5000.0,\n         3000.0,   2000.0,   1000.0,    500.0,    100.0],\n      dtype=&#x27;float32&#x27;, name=&#x27;pressure_levels&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b6f0cac-da6c-442f-948a-197db11624c2' class='xr-index-data-in' type='checkbox'/><label for='index-9b6f0cac-da6c-442f-948a-197db11624c2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 18:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n               &#x27;2009-10-16 06:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n               &#x27;2009-10-16 18:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n               &#x27;2009-10-17 06:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n               &#x27;2009-10-17 18:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n               &#x27;2009-10-18 06:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n               &#x27;2009-10-18 18:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n               &#x27;2009-10-19 06:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n               &#x27;2009-10-19 18:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n               &#x27;2009-10-20 06:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4c6c9ce-ce03-4b7c-9764-b8dfb432cba3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f4c6c9ce-ce03-4b7c-9764-b8dfb432cba3' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_6h_reduced_pl_q</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 11:05:21 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>25d5f0a2-ceae-47e1-936f-5b1804228ca6</dd></dl></div></li></ul></div></div>"
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 20GB\nDimensions:              (cell: 6599680, nvertex: 4, pressure_levels: 19,\n                          time_counter: 20, axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n  * pressure_levels      (pressure_levels) float32 76B 1e+05 9.25e+04 ... 100.0\n  * time_counter         (time_counter) datetime64[ns] 160B 2009-10-15T18:00:...\n    time_instant         (time_counter) datetime64[ns] 160B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables:\n    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    q                    (time_counter, pressure_levels, cell) float64 20GB dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;\n    time_counter_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n    time_instant_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_6h_reduced_pl_q\n    timeStamp:    2024-Apr-05 11:05:21 GMT\n    title:        Created by xios\n    uuid:         25d5f0a2-ceae-47e1-936f-5b1804228ca6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bea1a9f3-f540-43a9-be4c-8ab97ddcd21e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bea1a9f3-f540-43a9-be4c-8ab97ddcd21e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span class='xr-has-index'>pressure_levels</span>: 19</li><li><span class='xr-has-index'>time_counter</span>: 20</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-49c9c384-0b04-4b88-b953-141b2290df25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49c9c384-0b04-4b88-b953-141b2290df25' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-ef3828e5-6f81-4c62-982c-99156a79089b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef3828e5-6f81-4c62-982c-99156a79089b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b4ea86c-2f32-410f-ba7a-eda539454221' class='xr-var-data-in' type='checkbox'><label for='data-8b4ea86c-2f32-410f-ba7a-eda539454221' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-91ad89a3-cd3d-459d-b1b7-45690b571784' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91ad89a3-cd3d-459d-b1b7-45690b571784' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd3ee627-e840-45fc-9af9-c6f908f2af5c' class='xr-var-data-in' type='checkbox'><label for='data-fd3ee627-e840-45fc-9af9-c6f908f2af5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure_levels</span></div><div class='xr-var-dims'>(pressure_levels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.25e+04 ... 500.0 100.0</div><input id='attrs-75f299b8-e823-4c88-92f5-65f2aa1c2d6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75f299b8-e823-4c88-92f5-65f2aa1c2d6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1751944-7c46-4794-b263-8649e59c73ab' class='xr-var-data-in' type='checkbox'><label for='data-b1751944-7c46-4794-b263-8649e59c73ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertical pressure levels</dd><dt><span>name :</span></dt><dd>pressure_levels</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  92500.,  85000.,  70000.,  60000.,  50000.,  40000.,  30000.,\n        25000.,  20000.,  15000.,  10000.,   7000.,   5000.,   3000.,   2000.,\n         1000.,    500.,    100.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T18:00:00 ... 2009-10-...</div><input id='attrs-1a8e5215-742c-4b20-8998-ca234c1f9365' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a8e5215-742c-4b20-8998-ca234c1f9365' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-675da0a7-d5df-4b0c-8399-ab8d79ce6146' class='xr-var-data-in' type='checkbox'><label for='data-675da0a7-d5df-4b0c-8399-ab8d79ce6146' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T18:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n       &#x27;2009-10-16T06:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n       &#x27;2009-10-16T18:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n       &#x27;2009-10-17T06:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n       &#x27;2009-10-17T18:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n       &#x27;2009-10-18T06:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n       &#x27;2009-10-18T18:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n       &#x27;2009-10-19T06:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n       &#x27;2009-10-19T18:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n       &#x27;2009-10-20T06:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-2a6fba6e-e9a9-479f-9857-df4b65f76986' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a6fba6e-e9a9-479f-9857-df4b65f76986' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29a61859-d162-4940-83cd-f3ec27778d85' class='xr-var-data-in' type='checkbox'><label for='data-29a61859-d162-4940-83cd-f3ec27778d85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 160 B </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"84\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"34\" style=\"stroke-width:2\" />\n  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"34\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"34\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"34\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"34\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"34\" />\n  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"34\" />\n  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"34\" />\n  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"34\" />\n  <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"34\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"34\" />\n  <line x1=\"66\" y1=\"0\" x2=\"66\" y2=\"34\" />\n  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"34\" />\n  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"34\" />\n  <line x1=\"84\" y1=\"0\" x2=\"84\" y2=\"34\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"34\" />\n  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"34\" />\n  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"34\" />\n  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"34\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,34.50101617379934 0.0,34.50101617379934\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"54.501016\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >20</text>\n  <text x=\"140.000000\" y=\"17.250508\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,17.250508)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d3e3d3e1-7fba-4ecb-b06e-9d7340e66189' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3e3d3e1-7fba-4ecb-b06e-9d7340e66189' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-a47e1d00-b136-490b-af47-b41dbeedb289' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a47e1d00-b136-490b-af47-b41dbeedb289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d59cc35c-1c97-488d-9811-d775ac3cd1d6' class='xr-var-data-in' type='checkbox'><label for='data-d59cc35c-1c97-488d-9811-d775ac3cd1d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-16fa068e-fdae-4088-b7c1-2c030d70a24a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16fa068e-fdae-4088-b7c1-2c030d70a24a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-394b0ad5-5da3-4605-b690-fdb4307efabd' class='xr-var-data-in' type='checkbox'><label for='data-394b0ad5-5da3-4605-b690-fdb4307efabd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" 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x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time_counter, pressure_levels, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0d955b0b-e47a-4b3f-a4e1-5768b3ed54b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d955b0b-e47a-4b3f-a4e1-5768b3ed54b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34f327da-b097-402f-9e80-b1c1e480b3d7' class='xr-var-data-in' type='checkbox'><label for='data-34f327da-b097-402f-9e80-b1c1e480b3d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>6 h</dd><dt><span>interval_write :</span></dt><dd>6 h</dd><dt><span>long_name :</span></dt><dd>specific humidity</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg*kg-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.69 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 19, 6599680) </td>\n                        <td> (1, 1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 6080 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"1\" x2=\"24\" y2=\"16\" />\n  <line x1=\"10\" y1=\"2\" x2=\"24\" 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x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">19</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-bf10a11c-d841-42d5-86dc-6924bdfa4518' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf10a11c-d841-42d5-86dc-6924bdfa4518' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bea82c6a-3dee-457a-9ca2-7504c95c4b26' class='xr-var-data-in' type='checkbox'><label for='data-bea82c6a-3dee-457a-9ca2-7504c95c4b26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"38\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"38\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"38\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"38\" y2=\"54\" />\n  <line x1=\"0\" y1=\"60\" x2=\"38\" y2=\"60\" />\n  <line x1=\"0\" y1=\"66\" x2=\"38\" y2=\"66\" />\n  <line x1=\"0\" y1=\"72\" x2=\"38\" y2=\"72\" />\n  <line x1=\"0\" y1=\"78\" x2=\"38\" y2=\"78\" />\n  <line x1=\"0\" y1=\"84\" x2=\"38\" y2=\"84\" />\n  <line x1=\"0\" y1=\"90\" x2=\"38\" y2=\"90\" />\n  <line x1=\"0\" y1=\"96\" x2=\"38\" y2=\"96\" />\n  <line x1=\"0\" y1=\"102\" x2=\"38\" y2=\"102\" />\n  <line x1=\"0\" y1=\"108\" x2=\"38\" y2=\"108\" />\n  <line x1=\"0\" y1=\"120\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 38.596863036086,0.0 38.596863036086,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"19.298432\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"58.596863\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,58.596863,60.000000)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-7bb5c160-0c1f-468c-8650-0da89be06ec0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7bb5c160-0c1f-468c-8650-0da89be06ec0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-469e5b83-44f0-4ae4-a9e3-4ca99a8c0cac' class='xr-var-data-in' type='checkbox'><label for='data-469e5b83-44f0-4ae4-a9e3-4ca99a8c0cac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"38\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"38\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"38\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"38\" y2=\"54\" />\n  <line x1=\"0\" y1=\"60\" x2=\"38\" y2=\"60\" />\n  <line x1=\"0\" y1=\"66\" x2=\"38\" y2=\"66\" />\n  <line x1=\"0\" y1=\"72\" x2=\"38\" y2=\"72\" />\n  <line x1=\"0\" y1=\"78\" x2=\"38\" y2=\"78\" />\n  <line x1=\"0\" y1=\"84\" x2=\"38\" y2=\"84\" />\n  <line x1=\"0\" y1=\"90\" x2=\"38\" y2=\"90\" />\n  <line x1=\"0\" y1=\"96\" x2=\"38\" y2=\"96\" />\n  <line x1=\"0\" y1=\"102\" x2=\"38\" y2=\"102\" />\n  <line x1=\"0\" y1=\"108\" x2=\"38\" y2=\"108\" />\n  <line x1=\"0\" y1=\"120\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 38.596863036086,0.0 38.596863036086,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"19.298432\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"58.596863\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,58.596863,60.000000)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6822f1fa-e183-4ead-9bf4-dcaebf4c09e9' class='xr-section-summary-in' type='checkbox'  ><label for='section-6822f1fa-e183-4ead-9bf4-dcaebf4c09e9' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>pressure_levels</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1a32cb15-0053-4488-a983-5ff8ea7011c0' class='xr-index-data-in' type='checkbox'/><label for='index-1a32cb15-0053-4488-a983-5ff8ea7011c0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000.0,  92500.0,  85000.0,  70000.0,  60000.0,  50000.0,  40000.0,\n        30000.0,  25000.0,  20000.0,  15000.0,  10000.0,   7000.0,   5000.0,\n         3000.0,   2000.0,   1000.0,    500.0,    100.0],\n      dtype=&#x27;float32&#x27;, name=&#x27;pressure_levels&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5f7a1eeb-3f63-494a-b5af-dc7a1749f0bc' class='xr-index-data-in' type='checkbox'/><label for='index-5f7a1eeb-3f63-494a-b5af-dc7a1749f0bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 18:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n               &#x27;2009-10-16 06:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n               &#x27;2009-10-16 18:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n               &#x27;2009-10-17 06:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n               &#x27;2009-10-17 18:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n               &#x27;2009-10-18 06:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n               &#x27;2009-10-18 18:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n               &#x27;2009-10-19 06:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n               &#x27;2009-10-19 18:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n               &#x27;2009-10-20 06:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6a16d5f-b5e6-4a38-8948-d8b49bbe3d03' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6a16d5f-b5e6-4a38-8948-d8b49bbe3d03' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_6h_reduced_pl_q</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 11:05:21 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>25d5f0a2-ceae-47e1-936f-5b1804228ca6</dd></dl></div></li></ul></div></div>"
           },
           "metadata": {}
         }
@@ -183,7 +183,7 @@
         },
         {
           "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 735.2MiB (from 309.4MiB) for this notebook\n",
+          "text": "[pyodide]: Memory usage has grown to 735.0MiB (from 309.4MiB) for this notebook\n",
           "output_type": "stream"
         },
         {
@@ -221,7 +221,7 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": "<xarray.Dataset> Size: 95GB\nDimensions:              (time_counter: 40, cell: 6599680, nvertex: 4,\n                          axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array<chunksize=(824960,), meta=np.ndarray>\n    lon                  (cell) float32 26MB dask.array<chunksize=(824960,), meta=np.ndarray>\n  * time_counter         (time_counter) datetime64[ns] 320B 2009-10-15T15:00:...\n    time_instant         (time_counter) datetime64[ns] 320B dask.array<chunksize=(1,), meta=np.ndarray>\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables: (12/49)\n    10u                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    10v                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    2d                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    2t                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    bounds_lat           (cell, nvertex) float32 106MB dask.array<chunksize=(206240, 4), meta=np.ndarray>\n    bounds_lon           (cell, nvertex) float32 106MB dask.array<chunksize=(206240, 4), meta=np.ndarray>\n    ...                   ...\n    tp                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    tsn                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    tsr                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    tsrc                 (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    ttr                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    ttrc                 (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_3h_reduced_sfc\n    timeStamp:    2024-Apr-05 10:55:20 GMT\n    title:        Created by xios\n    uuid:         dbac5691-165e-4465-bd75-e403aa400071",
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.xr-var-data,\n.xr-index-data-in:checked ~ .xr-index-data {\n  display: block;\n}\n\n.xr-var-data > table {\n  float: right;\n}\n\n.xr-var-name span,\n.xr-var-data,\n.xr-index-name div,\n.xr-index-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2,\n.xr-no-icon {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 95GB\nDimensions:              (time_counter: 40, cell: 6599680, nvertex: 4,\n                          axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n  * time_counter         (time_counter) datetime64[ns] 320B 2009-10-15T15:00:...\n    time_instant         (time_counter) datetime64[ns] 320B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables: (12/49)\n    10u                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    10v                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    2d                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    2t                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    ...                   ...\n    tp                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsn                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    ttr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    ttrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_3h_reduced_sfc\n    timeStamp:    2024-Apr-05 10:55:20 GMT\n    title:        Created by xios\n    uuid:         dbac5691-165e-4465-bd75-e403aa400071</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cf5502f2-0115-4bbd-a1c7-b28c3d4815f6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf5502f2-0115-4bbd-a1c7-b28c3d4815f6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time_counter</span>: 40</li><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-414a3ac7-246a-4e05-a3ac-8df64fe03777' class='xr-section-summary-in' type='checkbox'  checked><label for='section-414a3ac7-246a-4e05-a3ac-8df64fe03777' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-c967b173-254e-491b-87ab-20d4a140bd5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c967b173-254e-491b-87ab-20d4a140bd5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-733d373f-d92f-4713-92b8-dc4dd5d6f9ab' class='xr-var-data-in' type='checkbox'><label for='data-733d373f-d92f-4713-92b8-dc4dd5d6f9ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-d1be764c-db39-4dc7-a7f5-ab21b07c1919' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1be764c-db39-4dc7-a7f5-ab21b07c1919' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-208b83ce-73c3-4ce6-9998-c3d652c1e481' class='xr-var-data-in' type='checkbox'><label for='data-208b83ce-73c3-4ce6-9998-c3d652c1e481' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T15:00:00 ... 2009-10-...</div><input id='attrs-6f5b7c67-eb3c-48d7-984d-17c0bda6f33f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f5b7c67-eb3c-48d7-984d-17c0bda6f33f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b76b7aa-2d61-4029-ac7c-5cd88e859ffc' class='xr-var-data-in' type='checkbox'><label for='data-1b76b7aa-2d61-4029-ac7c-5cd88e859ffc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T15:00:00.000000000&#x27;, &#x27;2009-10-15T18:00:00.000000000&#x27;,\n       &#x27;2009-10-15T21:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n       &#x27;2009-10-16T03:00:00.000000000&#x27;, &#x27;2009-10-16T06:00:00.000000000&#x27;,\n       &#x27;2009-10-16T09:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n       &#x27;2009-10-16T15:00:00.000000000&#x27;, &#x27;2009-10-16T18:00:00.000000000&#x27;,\n       &#x27;2009-10-16T21:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n       &#x27;2009-10-17T03:00:00.000000000&#x27;, &#x27;2009-10-17T06:00:00.000000000&#x27;,\n       &#x27;2009-10-17T09:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n       &#x27;2009-10-17T15:00:00.000000000&#x27;, &#x27;2009-10-17T18:00:00.000000000&#x27;,\n       &#x27;2009-10-17T21:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n       &#x27;2009-10-18T03:00:00.000000000&#x27;, &#x27;2009-10-18T06:00:00.000000000&#x27;,\n       &#x27;2009-10-18T09:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n       &#x27;2009-10-18T15:00:00.000000000&#x27;, &#x27;2009-10-18T18:00:00.000000000&#x27;,\n       &#x27;2009-10-18T21:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n       &#x27;2009-10-19T03:00:00.000000000&#x27;, &#x27;2009-10-19T06:00:00.000000000&#x27;,\n       &#x27;2009-10-19T09:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n       &#x27;2009-10-19T15:00:00.000000000&#x27;, &#x27;2009-10-19T18:00:00.000000000&#x27;,\n       &#x27;2009-10-19T21:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n       &#x27;2009-10-20T03:00:00.000000000&#x27;, &#x27;2009-10-20T06:00:00.000000000&#x27;,\n       &#x27;2009-10-20T09:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-f52f73b8-5d51-4eb5-80f1-128b4ac1dc62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f52f73b8-5d51-4eb5-80f1-128b4ac1dc62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60b136c9-1ce8-4ef1-95c9-5e75d0328ea6' class='xr-var-data-in' type='checkbox'><label for='data-60b136c9-1ce8-4ef1-95c9-5e75d0328ea6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"80\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"30\" style=\"stroke-width:2\" />\n  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"30\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"30\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"30\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"30\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"30\" />\n  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"30\" />\n  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"30\" />\n  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"30\" />\n  <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"30\" />\n  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"30\" />\n  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"30\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"30\" />\n  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"30\" />\n  <line x1=\"87\" y1=\"0\" x2=\"87\" y2=\"30\" />\n  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"30\" />\n  <line x1=\"99\" y1=\"0\" x2=\"99\" y2=\"30\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"30\" />\n  <line x1=\"111\" y1=\"0\" x2=\"111\" y2=\"30\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"30\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,30.311399683662877 0.0,30.311399683662877\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"50.311400\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >40</text>\n  <text x=\"140.000000\" y=\"15.155700\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,15.155700)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-058b2bb9-765b-45fe-8f59-ce9453d838b7' class='xr-section-summary-in' type='checkbox'  ><label for='section-058b2bb9-765b-45fe-8f59-ce9453d838b7' class='xr-section-summary' >Data variables: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-053884d9-001f-4365-8ca1-f035229a0712' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-053884d9-001f-4365-8ca1-f035229a0712' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-901280bc-70ee-4337-8d0a-23c0f2210af1' class='xr-var-data-in' type='checkbox'><label for='data-901280bc-70ee-4337-8d0a-23c0f2210af1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>u-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-942e454b-813f-41d1-a2fc-2d2085f83c11' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-942e454b-813f-41d1-a2fc-2d2085f83c11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02aa3e49-e0ce-4a20-922f-304debf47b25' class='xr-var-data-in' type='checkbox'><label for='data-02aa3e49-e0ce-4a20-922f-304debf47b25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>v-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0eeb0160-bc7d-4f32-a67c-11c8bb56f8bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0eeb0160-bc7d-4f32-a67c-11c8bb56f8bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fcd9587-1a53-40d7-950c-6043a0ccc17d' class='xr-var-data-in' type='checkbox'><label for='data-6fcd9587-1a53-40d7-950c-6043a0ccc17d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m dewpoint temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-2c4796ff-cf69-48fa-be6f-6116700229c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c4796ff-cf69-48fa-be6f-6116700229c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5886429d-61ee-4816-a543-bfa077858e9b' class='xr-var-data-in' type='checkbox'><label for='data-5886429d-61ee-4816-a543-bfa077858e9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m air temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" 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x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-86eaef34-fdd5-4849-b9eb-f440f34fb3d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86eaef34-fdd5-4849-b9eb-f440f34fb3d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e4c2a71-0845-4e1b-b06b-30df16bbabb3' class='xr-var-data-in' type='checkbox'><label for='data-8e4c2a71-0845-4e1b-b06b-30df16bbabb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-b0182711-03bd-4c7e-815a-4ceb7cbae700' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0182711-03bd-4c7e-815a-4ceb7cbae700' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ee00f31-a2a4-4183-a4fb-03803e80a544' class='xr-var-data-in' type='checkbox'><label for='data-9ee00f31-a2a4-4183-a4fb-03803e80a544' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e5506d9b-4838-4ce1-9a4e-8398a79604c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5506d9b-4838-4ce1-9a4e-8398a79604c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2ee0ead-31f9-4ef5-9784-3f012790330b' class='xr-var-data-in' type='checkbox'><label for='data-e2ee0ead-31f9-4ef5-9784-3f012790330b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>convective precipitation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c1039620-a6f6-4f48-879e-c0a83c71af12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1039620-a6f6-4f48-879e-c0a83c71af12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e32fab3e-cb99-40e8-80c7-79db8cc9f202' class='xr-var-data-in' type='checkbox'><label for='data-e32fab3e-cb99-40e8-80c7-79db8cc9f202' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-9416e0d0-d691-4604-8488-e1a3058de6ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9416e0d0-d691-4604-8488-e1a3058de6ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-462d6d88-7a98-4e40-8ce5-84d7271e2b0b' class='xr-var-data-in' type='checkbox'><label for='data-462d6d88-7a98-4e40-8ce5-84d7271e2b0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e1f34204-ef0c-437b-bd8b-1ee2d08b16a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1f34204-ef0c-437b-bd8b-1ee2d08b16a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b17b8c8-ce70-4eec-bfc2-8cdd57622cf2' class='xr-var-data-in' type='checkbox'><label for='data-5b17b8c8-ce70-4eec-bfc2-8cdd57622cf2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>eastward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_eastward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-050e584d-6288-4b23-a68a-90bb162d0a63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-050e584d-6288-4b23-a68a-90bb162d0a63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a04866c0-6d04-467f-b4d5-2abb01265fd9' class='xr-var-data-in' type='checkbox'><label for='data-a04866c0-6d04-467f-b4d5-2abb01265fd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_vel</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-da32fc80-a666-4c7b-a6d3-66e473f2f463' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da32fc80-a666-4c7b-a6d3-66e473f2f463' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f8a58e3-1f90-43cc-aca6-f48a656947c3' class='xr-var-data-in' type='checkbox'><label for='data-7f8a58e3-1f90-43cc-aca6-f48a656947c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>northward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_northward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-24f5dcb0-f905-4f7e-bf27-6412efebb26c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24f5dcb0-f905-4f7e-bf27-6412efebb26c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7651e487-3e95-4cac-a188-56ef39cadd05' class='xr-var-data-in' type='checkbox'><label for='data-7651e487-3e95-4cac-a188-56ef39cadd05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-53e802d7-18f3-475a-a8d9-5ee00b29401a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53e802d7-18f3-475a-a8d9-5ee00b29401a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f53b93d-1bf8-4a97-97fd-e25a539f85f1' class='xr-var-data-in' type='checkbox'><label for='data-6f53b93d-1bf8-4a97-97fd-e25a539f85f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow density</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_density</dd><dt><span>units :</span></dt><dd>kg m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e1e171fb-8811-44f2-b988-61c8764a54ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1e171fb-8811-44f2-b988-61c8764a54ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94b96bfa-5215-4be1-a065-fc930551510b' class='xr-var-data-in' type='checkbox'><label for='data-94b96bfa-5215-4be1-a065-fc930551510b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow depth</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  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y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-05b8c2fc-213b-47f1-944d-acf570082be7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05b8c2fc-213b-47f1-944d-acf570082be7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-303d0201-6fbe-4b45-84c6-81d98d65fc09' class='xr-var-data-in' type='checkbox'><label for='data-303d0201-6fbe-4b45-84c6-81d98d65fc09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snowfall</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-fca4af65-1436-4763-ac14-d8c046fb9d5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fca4af65-1436-4763-ac14-d8c046fb9d5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d240581a-07fc-4b56-ae79-6304e9fa472e' class='xr-var-data-in' type='checkbox'><label for='data-d240581a-07fc-4b56-ae79-6304e9fa472e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>skin temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>sea_surface_skin_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-7fad9fff-a35f-4bdc-ac9d-136cefb291eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fad9fff-a35f-4bdc-ac9d-136cefb291eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-973378be-6302-456e-87ea-56af88dd22df' class='xr-var-data-in' type='checkbox'><label for='data-973378be-6302-456e-87ea-56af88dd22df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_latent_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0ba66fdf-fbf8-407f-9ae2-79cf0856178c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ba66fdf-fbf8-407f-9ae2-79cf0856178c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007d7ef3-5e3c-4dd4-bf22-d5ed8a8e3f91' class='xr-var-data-in' type='checkbox'><label for='data-007d7ef3-5e3c-4dd4-bf22-d5ed8a8e3f91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow melt</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_snow_melt_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5192b1ac-4795-4a3c-b93a-fdaeed38e9e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5192b1ac-4795-4a3c-b93a-fdaeed38e9e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cebd13c6-e7cf-43d5-9043-812317bcbdd2' class='xr-var-data-in' type='checkbox'><label for='data-cebd13c6-e7cf-43d5-9043-812317bcbdd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-ed2de033-e004-4ad5-a810-c0202fb2aa43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed2de033-e004-4ad5-a810-c0202fb2aa43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b239018-c5bd-46b0-8f5f-81ae306472a2' class='xr-var-data-in' type='checkbox'><label for='data-8b239018-c5bd-46b0-8f5f-81ae306472a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e6b15a3f-8987-4437-bb8a-a9f4f9460852' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6b15a3f-8987-4437-bb8a-a9f4f9460852' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c21b8f5e-7136-4b7d-9f90-fc57e890ef78' class='xr-var-data-in' type='checkbox'><label for='data-c21b8f5e-7136-4b7d-9f90-fc57e890ef78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_sensible_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-dcc94509-75cf-48ac-b8c1-3a3fd6f19198' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcc94509-75cf-48ac-b8c1-3a3fd6f19198' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eca27506-fe62-4431-9d57-3870080f26d3' class='xr-var-data-in' type='checkbox'><label for='data-eca27506-fe62-4431-9d57-3870080f26d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-41e22af4-efdd-4bea-9e77-6f1e457c13cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41e22af4-efdd-4bea-9e77-6f1e457c13cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f1053c8-e687-42b1-a8af-aee95ec75dcf' class='xr-var-data-in' type='checkbox'><label for='data-6f1053c8-e687-42b1-a8af-aee95ec75dcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-dc2fe04e-7baf-4f89-bed5-2ae327621b09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc2fe04e-7baf-4f89-bed5-2ae327621b09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62b8e45b-5368-4c6b-82c5-4d69ec293905' class='xr-var-data-in' type='checkbox'><label for='data-62b8e45b-5368-4c6b-82c5-4d69ec293905' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-47a10975-0f76-4dd7-bda0-99ef9003e54f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47a10975-0f76-4dd7-bda0-99ef9003e54f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d445a8b-a747-4216-9c7f-e75fc2560a2c' class='xr-var-data-in' type='checkbox'><label for='data-5d445a8b-a747-4216-9c7f-e75fc2560a2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_1</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-71c72911-7467-4e0e-8640-b4fadf50e96c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71c72911-7467-4e0e-8640-b4fadf50e96c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0b5f07b-2ea5-44cb-a3d6-67adb500ed3e' class='xr-var-data-in' type='checkbox'><label for='data-f0b5f07b-2ea5-44cb-a3d6-67adb500ed3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_2</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-37c547ca-fc5a-42d9-bd3f-74e8826faa1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37c547ca-fc5a-42d9-bd3f-74e8826faa1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d62c142-1403-40b9-98e6-012966b05929' class='xr-var-data-in' type='checkbox'><label for='data-7d62c142-1403-40b9-98e6-012966b05929' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_3</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-56d9895f-3580-4035-b12b-ba91143b0e36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56d9895f-3580-4035-b12b-ba91143b0e36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69dc5f23-3cf5-442d-af82-fc84697e17a6' class='xr-var-data-in' type='checkbox'><label for='data-69dc5f23-3cf5-442d-af82-fc84697e17a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 4</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_4</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4271045a-0013-4bf9-922b-a92c9ef3eb37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4271045a-0013-4bf9-922b-a92c9ef3eb37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e675fdc-7934-488a-b6ec-7107480b4ef3' class='xr-var-data-in' type='checkbox'><label for='data-1e675fdc-7934-488a-b6ec-7107480b4ef3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c754f1ce-352e-49a9-9a8f-0c5cfae0c73b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c754f1ce-352e-49a9-9a8f-0c5cfae0c73b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1680699-7fef-4017-bff7-76167aa2c879' class='xr-var-data-in' type='checkbox'><label for='data-b1680699-7fef-4017-bff7-76167aa2c879' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-609ca016-313d-40ea-8099-8fdf57aacfbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-609ca016-313d-40ea-8099-8fdf57aacfbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a02dcd40-6583-4e18-9c09-cc935cdd1fc6' class='xr-var-data-in' type='checkbox'><label for='data-a02dcd40-6583-4e18-9c09-cc935cdd1fc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-642b8ed0-3df9-4791-a385-136fc10561c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-642b8ed0-3df9-4791-a385-136fc10561c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51786d20-b7b4-45ae-95d7-b1c017da68d3' class='xr-var-data-in' type='checkbox'><label for='data-51786d20-b7b4-45ae-95d7-b1c017da68d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_1</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-caa734b6-24bb-4bdd-9a76-4f24ec65e89a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-caa734b6-24bb-4bdd-9a76-4f24ec65e89a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d8b25e2-06f5-44d3-8a8d-98d03689ae82' class='xr-var-data-in' type='checkbox'><label for='data-6d8b25e2-06f5-44d3-8a8d-98d03689ae82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_2</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-3ccedc0b-2c86-40d3-b62e-8708383ed5f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ccedc0b-2c86-40d3-b62e-8708383ed5f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac32b567-d3f7-4be4-a935-2373121c41c8' class='xr-var-data-in' type='checkbox'><label for='data-ac32b567-d3f7-4be4-a935-2373121c41c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_3</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-09bb25a1-e1f6-4f71-a03c-13046517e497' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09bb25a1-e1f6-4f71-a03c-13046517e497' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17c11ac2-db48-4dba-9462-7ab657d2ac6c' class='xr-var-data-in' type='checkbox'><label for='data-17c11ac2-db48-4dba-9462-7ab657d2ac6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 4</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_4</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-15689a4e-f740-40da-8507-3077d647104e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15689a4e-f740-40da-8507-3077d647104e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3054d05-af23-4022-9878-e4038380c529' class='xr-var-data-in' type='checkbox'><label for='data-f3054d05-af23-4022-9878-e4038380c529' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-fa5a8f40-109f-4f26-acf8-c09f21cdc9f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa5a8f40-109f-4f26-acf8-c09f21cdc9f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92796599-c1eb-4fbe-9078-13fea0eec446' class='xr-var-data-in' type='checkbox'><label for='data-92796599-c1eb-4fbe-9078-13fea0eec446' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column ice water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_ice_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-8d4f6c4a-2ef9-4e0e-8d3b-3e41db534a67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d4f6c4a-2ef9-4e0e-8d3b-3e41db534a67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ff3fd96-5550-4202-8ed6-d61596607fc3' class='xr-var-data-in' type='checkbox'><label for='data-6ff3fd96-5550-4202-8ed6-d61596607fc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column liquid water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_liquid_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcwv</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-3f71878e-3677-47bb-bb75-51be123181d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f71878e-3677-47bb-bb75-51be123181d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-361d9942-08af-4af6-877a-64c012164d63' class='xr-var-data-in' type='checkbox'><label for='data-361d9942-08af-4af6-877a-64c012164d63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column water vapor</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_water_vapor</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-f8ca7cca-c016-492a-a11e-9bf1029da298' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8ca7cca-c016-492a-a11e-9bf1029da298' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23865c11-a561-4514-af57-a0323bb93ee7' class='xr-var-data-in' type='checkbox'><label for='data-23865c11-a561-4514-af57-a0323bb93ee7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"34\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"34\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"34\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"34\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"34\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"34\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"34\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"34\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"34\" y2=\"54\" />\n  <line x1=\"0\" y1=\"63\" x2=\"34\" y2=\"63\" />\n  <line x1=\"0\" y1=\"69\" x2=\"34\" y2=\"69\" />\n  <line x1=\"0\" y1=\"75\" x2=\"34\" y2=\"75\" />\n  <line x1=\"0\" y1=\"81\" x2=\"34\" y2=\"81\" />\n  <line x1=\"0\" y1=\"87\" x2=\"34\" y2=\"87\" />\n  <line x1=\"0\" y1=\"93\" x2=\"34\" y2=\"93\" />\n  <line x1=\"0\" y1=\"99\" x2=\"34\" y2=\"99\" />\n  <line x1=\"0\" y1=\"105\" x2=\"34\" y2=\"105\" />\n  <line x1=\"0\" y1=\"111\" x2=\"34\" y2=\"111\" />\n  <line x1=\"0\" y1=\"120\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 34.50101617379934,0.0 34.50101617379934,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"17.250508\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"54.501016\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,54.501016,60.000000)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-f6d0505e-c216-46d6-a1b4-0d038dbfce58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6d0505e-c216-46d6-a1b4-0d038dbfce58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dd5d10a-cbff-4eaf-8f14-47923699bb03' class='xr-var-data-in' type='checkbox'><label for='data-1dd5d10a-cbff-4eaf-8f14-47923699bb03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"34\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"34\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"34\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"34\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"34\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"34\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"34\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"34\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"34\" y2=\"54\" />\n  <line x1=\"0\" y1=\"63\" x2=\"34\" y2=\"63\" />\n  <line x1=\"0\" y1=\"69\" x2=\"34\" y2=\"69\" />\n  <line x1=\"0\" y1=\"75\" x2=\"34\" y2=\"75\" />\n  <line x1=\"0\" y1=\"81\" x2=\"34\" y2=\"81\" />\n  <line x1=\"0\" y1=\"87\" x2=\"34\" y2=\"87\" />\n  <line x1=\"0\" y1=\"93\" x2=\"34\" y2=\"93\" />\n  <line x1=\"0\" y1=\"99\" x2=\"34\" y2=\"99\" />\n  <line x1=\"0\" y1=\"105\" x2=\"34\" y2=\"105\" />\n  <line x1=\"0\" y1=\"111\" x2=\"34\" y2=\"111\" />\n  <line x1=\"0\" y1=\"120\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 34.50101617379934,0.0 34.50101617379934,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"17.250508\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"54.501016\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,54.501016,60.000000)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-d8a9bec6-bfaf-40c1-9844-78c786b968e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8a9bec6-bfaf-40c1-9844-78c786b968e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68e4b142-638e-4a95-9376-69374ce758de' class='xr-var-data-in' type='checkbox'><label for='data-68e4b142-638e-4a95-9376-69374ce758de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>toa incident solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_incoming_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-feb5a025-e5ee-4e2d-88a2-ea1ca1cc5993' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-feb5a025-e5ee-4e2d-88a2-ea1ca1cc5993' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b6f95da-54cb-49bd-8336-cc77ce57fc97' class='xr-var-data-in' type='checkbox'><label for='data-6b6f95da-54cb-49bd-8336-cc77ce57fc97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total precipitation (rain+snow)</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a080f8fc-92f2-47ef-ad25-a7af27a19829' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a080f8fc-92f2-47ef-ad25-a7af27a19829' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e22f3cb0-f5dc-4d2d-93ea-ba7a3a25751e' class='xr-var-data-in' type='checkbox'><label for='data-e22f3cb0-f5dc-4d2d-93ea-ba7a3a25751e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>temperature of snow layer</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-264e742c-5c6f-4571-9ee1-3266adb473d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-264e742c-5c6f-4571-9ee1-3266adb473d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c912ce87-249d-4f5e-805d-26bd6ff1e8af' class='xr-var-data-in' type='checkbox'><label for='data-c912ce87-249d-4f5e-805d-26bd6ff1e8af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c01bd702-0705-4348-885e-6717b8b08d8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c01bd702-0705-4348-885e-6717b8b08d8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09818294-467f-4aa3-b827-27e23a1dd7da' class='xr-var-data-in' type='checkbox'><label for='data-09818294-467f-4aa3-b827-27e23a1dd7da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" 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120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-34c8e6d2-dede-4c63-9d4d-94c9018c3bc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34c8e6d2-dede-4c63-9d4d-94c9018c3bc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cf331b9-f124-4459-85ad-8a2d7f746ec9' class='xr-var-data-in' type='checkbox'><label for='data-6cf331b9-f124-4459-85ad-8a2d7f746ec9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" 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x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f8630536-e736-4741-adaf-41bfe5652d92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8630536-e736-4741-adaf-41bfe5652d92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e23e1eb6-07dc-45c3-b991-5badc849d380' class='xr-var-data-in' type='checkbox'><label for='data-e23e1eb6-07dc-45c3-b991-5badc849d380' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-40390ac9-1934-4f07-ad95-7386b01eb161' class='xr-section-summary-in' type='checkbox'  ><label for='section-40390ac9-1934-4f07-ad95-7386b01eb161' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5c8e01fc-3316-4860-985d-43792c6479ac' class='xr-index-data-in' type='checkbox'/><label for='index-5c8e01fc-3316-4860-985d-43792c6479ac' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 15:00:00&#x27;, &#x27;2009-10-15 18:00:00&#x27;,\n               &#x27;2009-10-15 21:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n               &#x27;2009-10-16 03:00:00&#x27;, &#x27;2009-10-16 06:00:00&#x27;,\n               &#x27;2009-10-16 09:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n               &#x27;2009-10-16 15:00:00&#x27;, &#x27;2009-10-16 18:00:00&#x27;,\n               &#x27;2009-10-16 21:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n               &#x27;2009-10-17 03:00:00&#x27;, &#x27;2009-10-17 06:00:00&#x27;,\n               &#x27;2009-10-17 09:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n               &#x27;2009-10-17 15:00:00&#x27;, &#x27;2009-10-17 18:00:00&#x27;,\n               &#x27;2009-10-17 21:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n               &#x27;2009-10-18 03:00:00&#x27;, &#x27;2009-10-18 06:00:00&#x27;,\n               &#x27;2009-10-18 09:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n               &#x27;2009-10-18 15:00:00&#x27;, &#x27;2009-10-18 18:00:00&#x27;,\n               &#x27;2009-10-18 21:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n               &#x27;2009-10-19 03:00:00&#x27;, &#x27;2009-10-19 06:00:00&#x27;,\n               &#x27;2009-10-19 09:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n               &#x27;2009-10-19 15:00:00&#x27;, &#x27;2009-10-19 18:00:00&#x27;,\n               &#x27;2009-10-19 21:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n               &#x27;2009-10-20 03:00:00&#x27;, &#x27;2009-10-20 06:00:00&#x27;,\n               &#x27;2009-10-20 09:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8cf39ece-daca-46bc-904e-f6463873d9a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8cf39ece-daca-46bc-904e-f6463873d9a5' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_3h_reduced_sfc</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 10:55:20 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>dbac5691-165e-4465-bd75-e403aa400071</dd></dl></div></li></ul></div></div>"
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 95GB\nDimensions:              (time_counter: 40, cell: 6599680, nvertex: 4,\n                          axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n  * time_counter         (time_counter) datetime64[ns] 320B 2009-10-15T15:00:...\n    time_instant         (time_counter) datetime64[ns] 320B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables: (12/49)\n    10u                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    10v                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    2d                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    2t                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    ...                   ...\n    tp                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsn                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    ttr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    ttrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_3h_reduced_sfc\n    timeStamp:    2024-Apr-05 10:55:20 GMT\n    title:        Created by xios\n    uuid:         dbac5691-165e-4465-bd75-e403aa400071</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-93f5b631-d231-4e53-a7c2-ee5596b9eefc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-93f5b631-d231-4e53-a7c2-ee5596b9eefc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time_counter</span>: 40</li><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c7ae1b65-2bab-46b1-a41b-6819c7a102d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c7ae1b65-2bab-46b1-a41b-6819c7a102d5' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-710b5f8d-82f1-40ed-a8d4-867396917854' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-710b5f8d-82f1-40ed-a8d4-867396917854' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fef01ac-5671-4d66-975b-57c92dd78212' class='xr-var-data-in' type='checkbox'><label for='data-1fef01ac-5671-4d66-975b-57c92dd78212' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" 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text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-15c94b6e-4c95-4f6a-86f4-1e0b9e761b2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15c94b6e-4c95-4f6a-86f4-1e0b9e761b2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18cbc34b-ebde-49ba-b8df-5069090f631f' class='xr-var-data-in' type='checkbox'><label for='data-18cbc34b-ebde-49ba-b8df-5069090f631f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T15:00:00 ... 2009-10-...</div><input id='attrs-784ebecb-941d-465b-954d-2a5ddd2c51be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-784ebecb-941d-465b-954d-2a5ddd2c51be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5ec7afe-33ea-4881-a79d-d5e7ba6a2484' class='xr-var-data-in' type='checkbox'><label for='data-a5ec7afe-33ea-4881-a79d-d5e7ba6a2484' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T15:00:00.000000000&#x27;, &#x27;2009-10-15T18:00:00.000000000&#x27;,\n       &#x27;2009-10-15T21:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n       &#x27;2009-10-16T03:00:00.000000000&#x27;, &#x27;2009-10-16T06:00:00.000000000&#x27;,\n       &#x27;2009-10-16T09:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n       &#x27;2009-10-16T15:00:00.000000000&#x27;, &#x27;2009-10-16T18:00:00.000000000&#x27;,\n       &#x27;2009-10-16T21:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n       &#x27;2009-10-17T03:00:00.000000000&#x27;, &#x27;2009-10-17T06:00:00.000000000&#x27;,\n       &#x27;2009-10-17T09:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n       &#x27;2009-10-17T15:00:00.000000000&#x27;, &#x27;2009-10-17T18:00:00.000000000&#x27;,\n       &#x27;2009-10-17T21:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n       &#x27;2009-10-18T03:00:00.000000000&#x27;, &#x27;2009-10-18T06:00:00.000000000&#x27;,\n       &#x27;2009-10-18T09:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n       &#x27;2009-10-18T15:00:00.000000000&#x27;, &#x27;2009-10-18T18:00:00.000000000&#x27;,\n       &#x27;2009-10-18T21:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n       &#x27;2009-10-19T03:00:00.000000000&#x27;, &#x27;2009-10-19T06:00:00.000000000&#x27;,\n       &#x27;2009-10-19T09:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n       &#x27;2009-10-19T15:00:00.000000000&#x27;, &#x27;2009-10-19T18:00:00.000000000&#x27;,\n       &#x27;2009-10-19T21:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n       &#x27;2009-10-20T03:00:00.000000000&#x27;, &#x27;2009-10-20T06:00:00.000000000&#x27;,\n       &#x27;2009-10-20T09:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-3256d8ea-dd3b-4360-a3af-d937806639bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3256d8ea-dd3b-4360-a3af-d937806639bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23b5bf28-ea0a-437f-9c3e-51e1c0dece33' class='xr-var-data-in' type='checkbox'><label for='data-23b5bf28-ea0a-437f-9c3e-51e1c0dece33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"80\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"30\" style=\"stroke-width:2\" />\n  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"30\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"30\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"30\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"30\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"30\" />\n  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"30\" />\n  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"30\" />\n  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"30\" />\n  <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"30\" />\n  <line 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      </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0827f46b-b1da-4d47-b2b6-dc5f50eb5155' class='xr-section-summary-in' type='checkbox'  ><label for='section-0827f46b-b1da-4d47-b2b6-dc5f50eb5155' class='xr-section-summary' >Data variables: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-bd0e7193-f53a-4b88-af35-61c44ffa83d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd0e7193-f53a-4b88-af35-61c44ffa83d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77725653-c67a-4cec-8ead-6ab19722193d' class='xr-var-data-in' type='checkbox'><label for='data-77725653-c67a-4cec-8ead-6ab19722193d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>u-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n 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x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-54124f61-1628-49c8-9c58-b52b293fa367' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54124f61-1628-49c8-9c58-b52b293fa367' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a02acd3-c4ab-48d2-bf5d-e98bbedb16ce' class='xr-var-data-in' type='checkbox'><label for='data-5a02acd3-c4ab-48d2-bf5d-e98bbedb16ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>v-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0794923c-5f9a-40b4-a63d-5f57d4bc44ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0794923c-5f9a-40b4-a63d-5f57d4bc44ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14e560c0-66ef-4e19-8193-9058fe4e1b3f' class='xr-var-data-in' type='checkbox'><label for='data-14e560c0-66ef-4e19-8193-9058fe4e1b3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m dewpoint temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-45308492-e817-47e5-9207-3ad777f802c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45308492-e817-47e5-9207-3ad777f802c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0593fdea-764d-4fec-a97b-9beb6851ae58' class='xr-var-data-in' type='checkbox'><label for='data-0593fdea-764d-4fec-a97b-9beb6851ae58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m air temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-e2e77bcf-899a-4d29-af41-c8f6290c8c7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2e77bcf-899a-4d29-af41-c8f6290c8c7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74ef8eaf-790f-4861-bd68-b1491e0a5d78' class='xr-var-data-in' type='checkbox'><label for='data-74ef8eaf-790f-4861-bd68-b1491e0a5d78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-58cc5438-d7c8-4718-ac0a-622ab889504c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58cc5438-d7c8-4718-ac0a-622ab889504c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f242099d-1020-44bd-8b02-eb9468b46e72' class='xr-var-data-in' type='checkbox'><label for='data-f242099d-1020-44bd-8b02-eb9468b46e72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-d02f5ab7-5779-424e-94a4-a1f130117e3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d02f5ab7-5779-424e-94a4-a1f130117e3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d4076cc-5869-4acc-b6ae-9fb522685661' class='xr-var-data-in' type='checkbox'><label for='data-9d4076cc-5869-4acc-b6ae-9fb522685661' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>convective precipitation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-cafb6306-ceb6-476b-8a98-9c80056cabee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cafb6306-ceb6-476b-8a98-9c80056cabee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d414cd41-b532-44eb-ab17-59548bb2e762' class='xr-var-data-in' type='checkbox'><label for='data-d414cd41-b532-44eb-ab17-59548bb2e762' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-62d4f7a1-c8e1-47c8-9c30-2859daf37c38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62d4f7a1-c8e1-47c8-9c30-2859daf37c38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f94c35d-1566-4bb4-9ebb-b83a7088f3c5' class='xr-var-data-in' type='checkbox'><label for='data-5f94c35d-1566-4bb4-9ebb-b83a7088f3c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-00e9d24a-4d38-4a66-980a-7d75344bc4eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00e9d24a-4d38-4a66-980a-7d75344bc4eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5143919-9865-4425-9fc4-9d8d6483c8f6' class='xr-var-data-in' type='checkbox'><label for='data-d5143919-9865-4425-9fc4-9d8d6483c8f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>eastward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_eastward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-cd5f13d8-7eec-4281-a028-228ee355ccec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd5f13d8-7eec-4281-a028-228ee355ccec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9542286d-4182-445a-94e3-e0f3a5225c66' class='xr-var-data-in' type='checkbox'><label for='data-9542286d-4182-445a-94e3-e0f3a5225c66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_vel</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e87fcf8a-1309-4a9b-b456-b4e3565d244c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e87fcf8a-1309-4a9b-b456-b4e3565d244c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3059715-6e94-4b52-9dbe-0b12d1c9dbbc' class='xr-var-data-in' type='checkbox'><label for='data-e3059715-6e94-4b52-9dbe-0b12d1c9dbbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>northward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_northward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-87305c73-b485-4da9-a193-8ba202ad62a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87305c73-b485-4da9-a193-8ba202ad62a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4326fe14-5b67-4312-9178-81d6e5c36269' class='xr-var-data-in' type='checkbox'><label for='data-4326fe14-5b67-4312-9178-81d6e5c36269' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e697ff14-e5d9-4a17-a31a-b8ecc602265a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e697ff14-e5d9-4a17-a31a-b8ecc602265a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7d46d84-fc0c-4de3-a23e-8a668947b1a2' class='xr-var-data-in' type='checkbox'><label for='data-f7d46d84-fc0c-4de3-a23e-8a668947b1a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow density</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_density</dd><dt><span>units :</span></dt><dd>kg m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4fe6c36e-b666-4c60-8845-4d994743c4bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fe6c36e-b666-4c60-8845-4d994743c4bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b7d7137-23fd-44aa-afed-3a0efe1f55f2' class='xr-var-data-in' type='checkbox'><label for='data-9b7d7137-23fd-44aa-afed-3a0efe1f55f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow depth</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-80ed7e3c-798d-49e7-880a-e977d8c5bf10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80ed7e3c-798d-49e7-880a-e977d8c5bf10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3520188c-e3e8-4a3c-afca-d952c87c8e2f' class='xr-var-data-in' type='checkbox'><label for='data-3520188c-e3e8-4a3c-afca-d952c87c8e2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snowfall</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f98afa4f-5e5c-4d00-8e1c-605b4c2a524e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f98afa4f-5e5c-4d00-8e1c-605b4c2a524e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce2d0bc3-ce0f-4945-8264-daa78f13f63a' class='xr-var-data-in' type='checkbox'><label for='data-ce2d0bc3-ce0f-4945-8264-daa78f13f63a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>skin temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>sea_surface_skin_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-fed11ba3-344a-4067-803c-df853b46236d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fed11ba3-344a-4067-803c-df853b46236d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2560c92-2abf-42b0-8c04-b01c5cf49c9f' class='xr-var-data-in' type='checkbox'><label for='data-c2560c92-2abf-42b0-8c04-b01c5cf49c9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_latent_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-deb511cb-25bb-40cf-bd64-20b49519cfe8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-deb511cb-25bb-40cf-bd64-20b49519cfe8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac16e041-32da-4684-921d-c30734cb734c' class='xr-var-data-in' type='checkbox'><label for='data-ac16e041-32da-4684-921d-c30734cb734c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow melt</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_snow_melt_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-40a81105-fa21-4888-b0ef-1afe102415dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40a81105-fa21-4888-b0ef-1afe102415dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04e5e43c-aeab-47b9-bc0d-c464e37c2d1a' class='xr-var-data-in' type='checkbox'><label for='data-04e5e43c-aeab-47b9-bc0d-c464e37c2d1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5f1ca511-2330-404e-9510-7a0bb05d0bea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f1ca511-2330-404e-9510-7a0bb05d0bea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b7aade0-020f-43ce-9fe4-c16dfbc7729b' class='xr-var-data-in' type='checkbox'><label for='data-8b7aade0-020f-43ce-9fe4-c16dfbc7729b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  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<text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-114e0d0d-0d5a-490b-871d-66877dff5f2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-114e0d0d-0d5a-490b-871d-66877dff5f2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-147ebf2f-cc63-4775-8495-3cb9f896d26a' class='xr-var-data-in' type='checkbox'><label for='data-147ebf2f-cc63-4775-8495-3cb9f896d26a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_sensible_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-b46557bf-9dea-40f3-9a0a-edbee9ff4ef7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b46557bf-9dea-40f3-9a0a-edbee9ff4ef7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c3d3371-bbf0-49ab-a0d3-08d0f2912d1e' class='xr-var-data-in' type='checkbox'><label for='data-1c3d3371-bbf0-49ab-a0d3-08d0f2912d1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-8f1c36d4-6dfd-460a-afad-0d6d60740542' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f1c36d4-6dfd-460a-afad-0d6d60740542' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a246319d-a3b6-4ec6-bf7c-602de987295e' class='xr-var-data-in' type='checkbox'><label for='data-a246319d-a3b6-4ec6-bf7c-602de987295e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-819e1677-3092-4e29-8ac9-a3a6bde89661' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-819e1677-3092-4e29-8ac9-a3a6bde89661' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1458db5b-4208-4de5-8777-b252f2053557' class='xr-var-data-in' type='checkbox'><label for='data-1458db5b-4208-4de5-8777-b252f2053557' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-6b938ec3-6efd-4ed9-9c5f-9f1a128c6051' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b938ec3-6efd-4ed9-9c5f-9f1a128c6051' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5c12a76-5d90-4b35-8e2d-820c1fa164c7' class='xr-var-data-in' type='checkbox'><label for='data-d5c12a76-5d90-4b35-8e2d-820c1fa164c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_1</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-267b930f-33c5-4646-b0fa-48668d7c52ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-267b930f-33c5-4646-b0fa-48668d7c52ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27e297cd-8303-4760-afa5-5d51410fea0c' class='xr-var-data-in' type='checkbox'><label for='data-27e297cd-8303-4760-afa5-5d51410fea0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_2</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line 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font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e5183074-9688-4a60-b75e-b8d96234dc71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5183074-9688-4a60-b75e-b8d96234dc71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35ff5bb7-a575-4be7-aad1-413575bbd180' class='xr-var-data-in' type='checkbox'><label for='data-35ff5bb7-a575-4be7-aad1-413575bbd180' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_3</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e7544bb1-dfc9-45b3-8429-4157a74daccf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7544bb1-dfc9-45b3-8429-4157a74daccf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79cc9068-dc2d-4636-bd8e-aa512ef11633' class='xr-var-data-in' type='checkbox'><label for='data-79cc9068-dc2d-4636-bd8e-aa512ef11633' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 4</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_4</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-53352f66-b897-41e9-bc37-d7ff23de385b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53352f66-b897-41e9-bc37-d7ff23de385b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-385e5315-6e95-48eb-b9d3-089ad81eb391' class='xr-var-data-in' type='checkbox'><label for='data-385e5315-6e95-48eb-b9d3-089ad81eb391' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-eb98a574-520a-40f1-bc4e-de3c37291683' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb98a574-520a-40f1-bc4e-de3c37291683' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f2b56fd-0b23-4fce-a135-27dc089ada6c' class='xr-var-data-in' type='checkbox'><label for='data-2f2b56fd-0b23-4fce-a135-27dc089ada6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-818e3656-f743-4423-8b9c-00e80509bec6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-818e3656-f743-4423-8b9c-00e80509bec6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7cd45bc-15c5-4138-856c-c1b4afa201dc' class='xr-var-data-in' type='checkbox'><label for='data-f7cd45bc-15c5-4138-856c-c1b4afa201dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-026393c4-0ebb-4cbd-a7ed-256c325eb65a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-026393c4-0ebb-4cbd-a7ed-256c325eb65a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95e1d580-0a91-4d69-b00e-6d76820ef19e' class='xr-var-data-in' type='checkbox'><label for='data-95e1d580-0a91-4d69-b00e-6d76820ef19e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_1</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c59378fa-b6cf-4b46-8541-ce780b77ba9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c59378fa-b6cf-4b46-8541-ce780b77ba9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f2c65e9-452b-4edf-bb7a-9a6aa2ce276e' class='xr-var-data-in' type='checkbox'><label for='data-5f2c65e9-452b-4edf-bb7a-9a6aa2ce276e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_2</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a7a44a9b-08bc-486b-900e-e619b0c6bfad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7a44a9b-08bc-486b-900e-e619b0c6bfad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db12561b-d4f9-457c-ae06-523e09305fdb' class='xr-var-data-in' type='checkbox'><label for='data-db12561b-d4f9-457c-ae06-523e09305fdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_3</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-ba856725-29ab-4d91-852a-ca464c824b87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba856725-29ab-4d91-852a-ca464c824b87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eabfdad-ff1a-4bee-9000-de19139981aa' class='xr-var-data-in' type='checkbox'><label for='data-3eabfdad-ff1a-4bee-9000-de19139981aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 4</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_4</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-12320443-fd2a-4af5-bfc6-f15e586e2a67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12320443-fd2a-4af5-bfc6-f15e586e2a67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7418ec53-9da4-4a85-841e-0a966f57a24b' class='xr-var-data-in' type='checkbox'><label for='data-7418ec53-9da4-4a85-841e-0a966f57a24b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-03c6967c-70be-48c2-8b83-01ee3a5cb14b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03c6967c-70be-48c2-8b83-01ee3a5cb14b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-370bccd2-de53-46b4-8686-fa1d06f263fc' class='xr-var-data-in' type='checkbox'><label for='data-370bccd2-de53-46b4-8686-fa1d06f263fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column ice water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_ice_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" 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Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0f5975b8-be06-4155-b08a-4d20ef3637a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f5975b8-be06-4155-b08a-4d20ef3637a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7277af39-8c56-429a-b843-448fcfa6aeeb' class='xr-var-data-in' type='checkbox'><label for='data-7277af39-8c56-429a-b843-448fcfa6aeeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column liquid water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_liquid_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-e20cd6c8-45f1-4c05-9436-fd2511eb4c6d' class='xr-var-data-in' type='checkbox'><label for='data-e20cd6c8-45f1-4c05-9436-fd2511eb4c6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column water vapor</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_water_vapor</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-99e4eeac-a6f1-49c3-a509-71607cc2fe32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99e4eeac-a6f1-49c3-a509-71607cc2fe32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b0821e4-5571-4475-8dbf-7ec6d6897296' class='xr-var-data-in' type='checkbox'><label for='data-4b0821e4-5571-4475-8dbf-7ec6d6897296' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"34\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"34\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"34\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"34\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"34\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"34\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"34\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"34\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"34\" y2=\"54\" />\n  <line x1=\"0\" y1=\"63\" x2=\"34\" y2=\"63\" />\n  <line x1=\"0\" y1=\"69\" x2=\"34\" y2=\"69\" />\n  <line x1=\"0\" y1=\"75\" x2=\"34\" y2=\"75\" />\n  <line x1=\"0\" y1=\"81\" x2=\"34\" y2=\"81\" />\n  <line x1=\"0\" y1=\"87\" x2=\"34\" y2=\"87\" />\n  <line x1=\"0\" y1=\"93\" x2=\"34\" y2=\"93\" />\n  <line x1=\"0\" y1=\"99\" x2=\"34\" y2=\"99\" />\n  <line x1=\"0\" y1=\"105\" x2=\"34\" y2=\"105\" />\n  <line x1=\"0\" y1=\"111\" x2=\"34\" y2=\"111\" />\n  <line x1=\"0\" y1=\"120\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 34.50101617379934,0.0 34.50101617379934,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"17.250508\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"54.501016\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,54.501016,60.000000)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-d3a50cf8-246d-4368-b0af-ffb10a4daf7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3a50cf8-246d-4368-b0af-ffb10a4daf7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e82765a7-1fd7-443c-86c0-1616ab979ba6' class='xr-var-data-in' type='checkbox'><label for='data-e82765a7-1fd7-443c-86c0-1616ab979ba6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"34\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"34\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"34\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"34\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"34\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"34\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"34\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"34\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"34\" y2=\"54\" />\n  <line x1=\"0\" y1=\"63\" x2=\"34\" y2=\"63\" />\n  <line x1=\"0\" y1=\"69\" x2=\"34\" y2=\"69\" />\n  <line x1=\"0\" y1=\"75\" x2=\"34\" y2=\"75\" />\n  <line x1=\"0\" y1=\"81\" x2=\"34\" y2=\"81\" />\n  <line x1=\"0\" y1=\"87\" x2=\"34\" y2=\"87\" />\n  <line x1=\"0\" y1=\"93\" x2=\"34\" y2=\"93\" />\n  <line x1=\"0\" y1=\"99\" x2=\"34\" y2=\"99\" />\n  <line x1=\"0\" y1=\"105\" x2=\"34\" y2=\"105\" />\n  <line x1=\"0\" y1=\"111\" x2=\"34\" y2=\"111\" />\n  <line x1=\"0\" y1=\"120\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 34.50101617379934,0.0 34.50101617379934,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"17.250508\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"54.501016\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,54.501016,60.000000)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-942bf4ef-4452-44cc-8137-e5b331e0558c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-942bf4ef-4452-44cc-8137-e5b331e0558c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-868e83cc-93b9-490a-9556-3d9c7d822709' class='xr-var-data-in' type='checkbox'><label for='data-868e83cc-93b9-490a-9556-3d9c7d822709' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>toa incident solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_incoming_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-34b2d553-e549-44df-b1f5-44bb92a144b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34b2d553-e549-44df-b1f5-44bb92a144b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b757ca5-9535-41c9-bea1-b60de7caaf30' class='xr-var-data-in' type='checkbox'><label for='data-5b757ca5-9535-41c9-bea1-b60de7caaf30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total precipitation (rain+snow)</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4c0cb41b-e1ff-48a4-a9e0-7acfb930ad68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c0cb41b-e1ff-48a4-a9e0-7acfb930ad68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-127f9ce1-818f-4eba-a80c-f7e7276f61be' class='xr-var-data-in' type='checkbox'><label for='data-127f9ce1-818f-4eba-a80c-f7e7276f61be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>temperature of snow layer</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-ebdd66a8-5f19-4211-909f-853d64ba3d4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebdd66a8-5f19-4211-909f-853d64ba3d4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd9a6303-08ce-47ce-a798-f4a3ab820b09' class='xr-var-data-in' type='checkbox'><label for='data-dd9a6303-08ce-47ce-a798-f4a3ab820b09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4ee3570f-1375-46ce-8e09-12ff4cceb5c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ee3570f-1375-46ce-8e09-12ff4cceb5c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81b11628-1336-4d27-949a-997802529550' class='xr-var-data-in' type='checkbox'><label for='data-81b11628-1336-4d27-949a-997802529550' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a171ebb9-3c27-4155-bff5-5456800d7f92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a171ebb9-3c27-4155-bff5-5456800d7f92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-248f1218-3948-4c0d-97dc-cca6c4536990' class='xr-var-data-in' type='checkbox'><label for='data-248f1218-3948-4c0d-97dc-cca6c4536990' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4bfd705f-6583-409d-b7f0-85bd40c51aec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bfd705f-6583-409d-b7f0-85bd40c51aec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20f26542-ba1e-46e4-bb48-6dd6b0e4ccb3' class='xr-var-data-in' type='checkbox'><label for='data-20f26542-ba1e-46e4-bb48-6dd6b0e4ccb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a35cb2a6-4e55-4af7-852a-55c4b9daf67c' class='xr-section-summary-in' type='checkbox'  ><label for='section-a35cb2a6-4e55-4af7-852a-55c4b9daf67c' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0e8f63d1-f826-4286-9792-ac242471765d' class='xr-index-data-in' type='checkbox'/><label for='index-0e8f63d1-f826-4286-9792-ac242471765d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 15:00:00&#x27;, &#x27;2009-10-15 18:00:00&#x27;,\n               &#x27;2009-10-15 21:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n               &#x27;2009-10-16 03:00:00&#x27;, &#x27;2009-10-16 06:00:00&#x27;,\n               &#x27;2009-10-16 09:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n               &#x27;2009-10-16 15:00:00&#x27;, &#x27;2009-10-16 18:00:00&#x27;,\n               &#x27;2009-10-16 21:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n               &#x27;2009-10-17 03:00:00&#x27;, &#x27;2009-10-17 06:00:00&#x27;,\n               &#x27;2009-10-17 09:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n               &#x27;2009-10-17 15:00:00&#x27;, &#x27;2009-10-17 18:00:00&#x27;,\n               &#x27;2009-10-17 21:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n               &#x27;2009-10-18 03:00:00&#x27;, &#x27;2009-10-18 06:00:00&#x27;,\n               &#x27;2009-10-18 09:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n               &#x27;2009-10-18 15:00:00&#x27;, &#x27;2009-10-18 18:00:00&#x27;,\n               &#x27;2009-10-18 21:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n               &#x27;2009-10-19 03:00:00&#x27;, &#x27;2009-10-19 06:00:00&#x27;,\n               &#x27;2009-10-19 09:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n               &#x27;2009-10-19 15:00:00&#x27;, &#x27;2009-10-19 18:00:00&#x27;,\n               &#x27;2009-10-19 21:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n               &#x27;2009-10-20 03:00:00&#x27;, &#x27;2009-10-20 06:00:00&#x27;,\n               &#x27;2009-10-20 09:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-efd6dfe5-3122-4e56-9b00-6d34d7bf4e5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-efd6dfe5-3122-4e56-9b00-6d34d7bf4e5b' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_3h_reduced_sfc</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 10:55:20 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>dbac5691-165e-4465-bd75-e403aa400071</dd></dl></div></li></ul></div></div>"
           },
           "metadata": {}
         }

From 9286cd8bd9a9bec790397171908ce417a561f769 Mon Sep 17 00:00:00 2001
From: Sara Faghih-Naini <sara.faghihnaini@ecmwf.int>
Date: Mon, 9 Dec 2024 14:15:59 +0100
Subject: [PATCH 4/8] Update new notebooks

---
 04-example-datasets/03-NextGEMS.ipynb |  181 +-
 04-example-datasets/04-ICONXPP.ipynb  | 7712 +------------------------
 2 files changed, 419 insertions(+), 7474 deletions(-)

diff --git a/04-example-datasets/03-NextGEMS.ipynb b/04-example-datasets/03-NextGEMS.ipynb
index 5bbc8e2..d00b7f3 100644
--- a/04-example-datasets/03-NextGEMS.ipynb
+++ b/04-example-datasets/03-NextGEMS.ipynb
@@ -25,7 +25,7 @@
    "id": "463bf2c0-ac3f-4786-9687-362393802dce",
    "metadata": {},
    "source": [
-    "This notebook is a comprehensive guide to accessing Zarr directory for an ICON simulation within the 4th model cycle phase of the NextGEMS project. It includes details about the available parameters and steps, along with instructions for using the `load_NextGEMS_data` function to access these data from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/EW3_ICON_ngc4008_90d_Compression_test.zarr/). The Zarr data is not downloaded locally; instead, it is accessed remotely using `zarr`.\n",
+    "This notebook is a comprehensive guide to accessing a Zarr dataset for an ICON simulation within the 4th model cycle phase of the NextGEMS project. It includes details about the available parameters and steps, along with instructions for using the `load_NextGEMS_data` function to access these data from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/EW3_ICON_ngc4008_90d_Compression_test.zarr/). The Zarr data is not downloaded locally; instead, it is accessed remotely using `zarr`.\n",
     "\n",
     "---\n",
     "\n",
@@ -33,8 +33,8 @@
     "\n",
     "The dataset comprises output data from an ICON run within the 4th model cycle phase of the NextGEMS project (NGC4008). This simulation spans a total of 30 years and uses an ssp370-like scenario run from 2020.\n",
     "The simulation was performed using a horizontal grid spacing of approximately 100 km (R2B8/8). \n",
-    "However, only a subset of the data is provided: three months of data for four selected variables at three model levels.\n",
     "A global, coupled simulation covering a time period of 1000 years, starting in 1300, was run. 48-hourly output data was generated throughout the simulation.\n",
+    "However, only a subset of the data is provided: three months of data for four selected variables at three model levels.\n",
     "The data is stored on a HEALPix grid with lossless compression, and the total size of the available dataset is approximately 9 GB.\n",
     "\n",
     "The dataset is contained in a Zarr directory.\n",
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 1,
    "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
    "metadata": {},
    "outputs": [],
@@ -90,11 +90,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 2,
    "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
    "metadata": {},
    "outputs": [],
    "source": [
+    "import aiohttp\n",
+    "import dask\n",
     "import fsspec\n",
     "import zarr"
    ]
@@ -109,13 +111,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 3,
    "id": "0b393415-87ca-4dc7-8c72-4cdd101d6621",
    "metadata": {},
    "outputs": [],
    "source": [
     "import json\n",
     "\n",
+    "import requests\n",
     "import xarray as xr\n",
     "\n",
     "\n",
@@ -140,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 4,
    "id": "510bad61-a98c-495b-a9fa-365887b9f1b6",
    "metadata": {},
    "outputs": [
@@ -527,7 +530,7 @@
        "    hus         (time, level_full, cell) float32 dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;\n",
        "    pr          (time, cell) float32 dask.array&lt;chunksize=(3, 196608), meta=np.ndarray&gt;\n",
        "    ua          (time, level_full, cell) float32 dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;\n",
-       "    va          (time, level_full, cell) float32 dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0fa66641-78df-42f3-b664-a84c5cd05bc2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0fa66641-78df-42f3-b664-a84c5cd05bc2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 90</li><li><span class='xr-has-index'>level_full</span>: 3</li><li><span>cell</span>: 3145728</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-12f8e8a4-d3f4-4b67-9675-51297b97c835' class='xr-section-summary-in' type='checkbox'  checked><label for='section-12f8e8a4-d3f4-4b67-9675-51297b97c835' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level_full</span></div><div class='xr-var-dims'>(level_full)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.0 65.0 55.0</div><input id='attrs-a83b4f42-5cda-4a2f-9901-47affcded87e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a83b4f42-5cda-4a2f-9901-47affcded87e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4ef6f6f-7fe6-4f89-b0b3-284aaad5baad' class='xr-var-data-in' type='checkbox'><label for='data-a4ef6f6f-7fe6-4f89-b0b3-284aaad5baad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>generalized_height</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array([89., 65., 55.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-02 ... 2020-03-31</div><input id='attrs-9eede9bb-a6f1-4dfe-a886-0541c6240bac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9eede9bb-a6f1-4dfe-a886-0541c6240bac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfd65b85-bf30-4cef-85a7-45204ee91014' class='xr-var-data-in' type='checkbox'><label for='data-cfd65b85-bf30-4cef-85a7-45204ee91014' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-02T00:00:00.000000000&#x27;, &#x27;2020-01-03T00:00:00.000000000&#x27;,\n",
+       "    va          (time, level_full, cell) float32 dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-25de3706-ddf7-472c-bc28-9067e9c191b4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-25de3706-ddf7-472c-bc28-9067e9c191b4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 90</li><li><span class='xr-has-index'>level_full</span>: 3</li><li><span>cell</span>: 3145728</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2e239176-5d5d-4919-8edc-248fc538e858' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2e239176-5d5d-4919-8edc-248fc538e858' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level_full</span></div><div class='xr-var-dims'>(level_full)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.0 65.0 55.0</div><input id='attrs-b14e4269-2e46-47f1-9b20-577a6d8bde92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b14e4269-2e46-47f1-9b20-577a6d8bde92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eb1c4f3-f4d7-4a35-b814-1dcb82cf22d7' class='xr-var-data-in' type='checkbox'><label for='data-5eb1c4f3-f4d7-4a35-b814-1dcb82cf22d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>generalized_height</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array([89., 65., 55.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-02 ... 2020-03-31</div><input id='attrs-b82b151e-42a0-4141-953c-4d03c95b569e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b82b151e-42a0-4141-953c-4d03c95b569e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff28a181-c830-423b-b12c-77e05a171322' class='xr-var-data-in' type='checkbox'><label for='data-ff28a181-c830-423b-b12c-77e05a171322' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-02T00:00:00.000000000&#x27;, &#x27;2020-01-03T00:00:00.000000000&#x27;,\n",
        "       &#x27;2020-01-04T00:00:00.000000000&#x27;, &#x27;2020-01-05T00:00:00.000000000&#x27;,\n",
        "       &#x27;2020-01-06T00:00:00.000000000&#x27;, &#x27;2020-01-07T00:00:00.000000000&#x27;,\n",
        "       &#x27;2020-01-08T00:00:00.000000000&#x27;, &#x27;2020-01-09T00:00:00.000000000&#x27;,\n",
@@ -572,7 +575,7 @@
        "       &#x27;2020-03-26T00:00:00.000000000&#x27;, &#x27;2020-03-27T00:00:00.000000000&#x27;,\n",
        "       &#x27;2020-03-28T00:00:00.000000000&#x27;, &#x27;2020-03-29T00:00:00.000000000&#x27;,\n",
        "       &#x27;2020-03-30T00:00:00.000000000&#x27;, &#x27;2020-03-31T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aa73597f-2de8-4fc0-9804-a75986ef72ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa73597f-2de8-4fc0-9804-a75986ef72ac' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>hus</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-4e6755ef-f930-4c1e-9fb7-89dabecd22cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e6755ef-f930-4c1e-9fb7-89dabecd22cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21903b11-166f-4c53-b3e3-d55736ddc878' class='xr-var-data-in' type='checkbox'><label for='data-21903b11-166f-4c53-b3e3-d55736ddc878' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6cee192-1009-4940-9a74-36a694ed3498' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6cee192-1009-4940-9a74-36a694ed3498' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>hus</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-00584207-7b42-4528-810b-6e02d5a1283a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00584207-7b42-4528-810b-6e02d5a1283a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59dd54d4-398b-4e29-9e76-d195640c3b01' class='xr-var-data-in' type='checkbox'><label for='data-59dd54d4-398b-4e29-9e76-d195640c3b01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -720,7 +723,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 196608), meta=np.ndarray&gt;</div><input id='attrs-00281b6b-d8f9-43d1-98e3-a0320d7ec5c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00281b6b-d8f9-43d1-98e3-a0320d7ec5c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89e0fa4f-a00d-4761-8464-4649c0cb7da2' class='xr-var-data-in' type='checkbox'><label for='data-89e0fa4f-a00d-4761-8464-4649c0cb7da2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>precipitation flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>vgrid :</span></dt><dd>surface</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 196608), meta=np.ndarray&gt;</div><input id='attrs-42d80b1b-b4d6-4944-84a9-731e1b51acd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42d80b1b-b4d6-4944-84a9-731e1b51acd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4f51ba8-fa39-4239-9d10-c25f415d0495' class='xr-var-data-in' type='checkbox'><label for='data-a4f51ba8-fa39-4239-9d10-c25f415d0495' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>precipitation flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>vgrid :</span></dt><dd>surface</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -808,7 +811,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ua</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-a53a7e6a-4ae8-4420-9434-e60132a6fbb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a53a7e6a-4ae8-4420-9434-e60132a6fbb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-786b4b4c-e873-4ef0-9752-b6ee0fb271a1' class='xr-var-data-in' type='checkbox'><label for='data-786b4b4c-e873-4ef0-9752-b6ee0fb271a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Zonal wind</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ua</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-03e373de-4afa-4a8f-b3b1-825604f01e62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03e373de-4afa-4a8f-b3b1-825604f01e62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5f1c118-dee8-46f9-8a6f-26bbe9c69546' class='xr-var-data-in' type='checkbox'><label for='data-b5f1c118-dee8-46f9-8a6f-26bbe9c69546' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Zonal wind</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -956,7 +959,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>va</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-4eac3111-d49b-406a-a9e9-9a3dcb9b9844' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4eac3111-d49b-406a-a9e9-9a3dcb9b9844' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23d64f36-22d2-4bea-ab2e-4c2a8beb1f32' class='xr-var-data-in' type='checkbox'><label for='data-23d64f36-22d2-4bea-ab2e-4c2a8beb1f32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Meridional wind</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>va</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-2d89f3e0-b387-4e4a-aae8-7f7f1b663cfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d89f3e0-b387-4e4a-aae8-7f7f1b663cfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbd215b0-3c43-499a-9b92-fb5ab0cbffb5' class='xr-var-data-in' type='checkbox'><label for='data-dbd215b0-3c43-499a-9b92-fb5ab0cbffb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Meridional wind</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1104,7 +1107,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-aeaa7d29-2f1b-4172-874e-a40ac176399e' class='xr-section-summary-in' type='checkbox'  ><label for='section-aeaa7d29-2f1b-4172-874e-a40ac176399e' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level_full</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9a02a5fe-5d13-45fc-bd46-2b02ddcbdc2d' class='xr-index-data-in' type='checkbox'/><label for='index-9a02a5fe-5d13-45fc-bd46-2b02ddcbdc2d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([89.0, 65.0, 55.0], dtype=&#x27;float64&#x27;, name=&#x27;level_full&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-743099c6-8b7e-4ed7-af2a-42b3b299d9b8' class='xr-index-data-in' type='checkbox'/><label for='index-743099c6-8b7e-4ed7-af2a-42b3b299d9b8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-01-02&#x27;, &#x27;2020-01-03&#x27;, &#x27;2020-01-04&#x27;, &#x27;2020-01-05&#x27;,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ed141478-8041-42df-9961-56866c0bae16' class='xr-section-summary-in' type='checkbox'  ><label for='section-ed141478-8041-42df-9961-56866c0bae16' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level_full</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1a260fa4-5683-4686-ae6b-c19b453f6a34' class='xr-index-data-in' type='checkbox'/><label for='index-1a260fa4-5683-4686-ae6b-c19b453f6a34' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([89.0, 65.0, 55.0], dtype=&#x27;float64&#x27;, name=&#x27;level_full&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eb7bd7f0-2756-4c0f-8f33-161c2e3d711c' class='xr-index-data-in' type='checkbox'/><label for='index-eb7bd7f0-2756-4c0f-8f33-161c2e3d711c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-01-02&#x27;, &#x27;2020-01-03&#x27;, &#x27;2020-01-04&#x27;, &#x27;2020-01-05&#x27;,\n",
        "               &#x27;2020-01-06&#x27;, &#x27;2020-01-07&#x27;, &#x27;2020-01-08&#x27;, &#x27;2020-01-09&#x27;,\n",
        "               &#x27;2020-01-10&#x27;, &#x27;2020-01-11&#x27;, &#x27;2020-01-12&#x27;, &#x27;2020-01-13&#x27;,\n",
        "               &#x27;2020-01-14&#x27;, &#x27;2020-01-15&#x27;, &#x27;2020-01-16&#x27;, &#x27;2020-01-17&#x27;,\n",
@@ -1127,7 +1130,7 @@
        "               &#x27;2020-03-22&#x27;, &#x27;2020-03-23&#x27;, &#x27;2020-03-24&#x27;, &#x27;2020-03-25&#x27;,\n",
        "               &#x27;2020-03-26&#x27;, &#x27;2020-03-27&#x27;, &#x27;2020-03-28&#x27;, &#x27;2020-03-29&#x27;,\n",
        "               &#x27;2020-03-30&#x27;, &#x27;2020-03-31&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68410ad6-eb50-4ca5-b62a-c0d5ed5e6c13' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68410ad6-eb50-4ca5-b62a-c0d5ed5e6c13' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48e18508-2e06-4c1e-9f7c-1f9685f854be' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-48e18508-2e06-4c1e-9f7c-1f9685f854be' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
@@ -1143,7 +1146,7 @@
        "    va          (time, level_full, cell) float32 dask.array<chunksize=(3, 1, 196608), meta=np.ndarray>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1153,6 +1156,121 @@
     "ds"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8ca30b3f-8c16-4d2b-a390-0977b318d93f",
+   "metadata": {},
+   "source": [
+    "The `earthkit` package is used for plotting the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0becd6b7-b9c5-4932-aaca-8d1c78dc04cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import earthkit.data\n",
+    "import earthkit.plots.quickmap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e307d560-3306-41a7-a2a4-01e3fd7b0a51",
+   "metadata": {},
+   "source": [
+    "The data can be visualized as demonstrated below. For example, the specific humidity on **2020-02-01 at 00:00:00 UTC** at model level 89 is plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d19dfa96-dcb4-4b82-b4d4-04c81307936c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "chart = earthkit.plots.Map(extent=[-180, 180, -90, 90])\n",
+    "chart.plot(ds[\"hus\"].sel(time=\"2020-02-01T00:00:00\"))\n",
+    "chart.title(\"Specific humidity \")\n",
+    "chart.coastlines()\n",
+    "chart.gridlines()\n",
+    "chart.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb706079-df56-4d5e-978f-f362d96e2d19",
+   "metadata": {},
+   "source": [
+    "Visualizing the precipitation flux on **2020-02-01 at 00:00:00 UTC** is tried below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7e4cc456-744d-42a2-92bd-006dda5b5e03",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "UFuncTypeError",
+     "evalue": "ufunc 'less' did not contain a loop with signature matching types (<class 'numpy.dtypes.TimeDelta64DType'>, <class 'numpy.dtypes.Float64DType'>) -> None",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "File \u001b[0;32m~/Documents/ECMWF/Code/earthkit-plots/src/earthkit/plots/components/subplots.py:396\u001b[0m, in \u001b[0;36mSubplot._extract_plottables\u001b[0;34m(self, method_name, args, data, x, y, z, style, units, every, source_units, extract_domain, **kwargs)\u001b[0m\n\u001b[1;32m    395\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 396\u001b[0m     mappable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_name\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    397\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    398\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[0;32m~/Documents/ECMWF/Code/earthkit-plots/src/earthkit/plots/styles/__init__.py:429\u001b[0m, in \u001b[0;36mStyle.pcolormesh\u001b[0;34m(self, ax, x, y, values, *args, **kwargs)\u001b[0m\n\u001b[1;32m    428\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_pcolormesh_kwargs(values), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs}\n\u001b[0;32m--> 429\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpcolormesh\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[0;32m~/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/cartopy/mpl/geoaxes.py:307\u001b[0m, in \u001b[0;36m_add_transform.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    306\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m transform\n\u001b[0;32m--> 307\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/cartopy/mpl/geoaxes.py:1768\u001b[0m, in \u001b[0;36mGeoAxes.pcolormesh\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1767\u001b[0m args, kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_args(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 1768\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpcolormesh\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1769\u001b[0m \u001b[38;5;66;03m# Wrap the quadrilaterals if necessary\u001b[39;00m\n",
+      "File \u001b[0;32m~/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/matplotlib/__init__.py:1412\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1412\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1414\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m~/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/matplotlib/axes/_axes.py:6058\u001b[0m, in \u001b[0;36mAxes.pcolormesh\u001b[0;34m(self, alpha, norm, cmap, vmin, vmax, shading, antialiased, *args, **kwargs)\u001b[0m\n\u001b[1;32m   6056\u001b[0m kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124medgecolors\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 6058\u001b[0m X, Y, C, shading \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pcolorargs\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpcolormesh\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   6059\u001b[0m \u001b[43m                                    \u001b[49m\u001b[43mshading\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshading\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6060\u001b[0m coords \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mstack([X, Y], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m~/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/matplotlib/axes/_axes.py:5568\u001b[0m, in \u001b[0;36mAxes._pcolorargs\u001b[0;34m(self, funcname, shading, *args, **kwargs)\u001b[0m\n\u001b[1;32m   5567\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (Nx, Ny) \u001b[38;5;241m!=\u001b[39m (ncols \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, nrows \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m-> 5568\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDimensions of C \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m are incompatible with\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   5569\u001b[0m                         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m X (\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m) and/or Y (\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m); see help(\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (\n\u001b[1;32m   5570\u001b[0m                             C\u001b[38;5;241m.\u001b[39mshape, Nx, Ny, funcname))\n\u001b[1;32m   5571\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:    \u001b[38;5;66;03m# ['nearest', 'gouraud']:\u001b[39;00m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Dimensions of C (90, 3145728) are incompatible with X (90) and/or Y (3145728); see help(pcolormesh)",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mDTypePromotionError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;31mDTypePromotionError\u001b[0m: The DType <class 'numpy.dtypes.TimeDelta64DType'> could not be promoted by <class 'numpy.dtypes.Float64DType'>. This means that no common DType exists for the given inputs. For example they cannot be stored in a single array unless the dtype is `object`. The full list of DTypes is: (<class 'numpy.dtypes.TimeDelta64DType'>, <class 'numpy.dtypes.Float64DType'>)",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mUFuncTypeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m chart \u001b[38;5;241m=\u001b[39m earthkit\u001b[38;5;241m.\u001b[39mplots\u001b[38;5;241m.\u001b[39mMap(extent\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m180\u001b[39m, \u001b[38;5;241m180\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m90\u001b[39m, \u001b[38;5;241m90\u001b[39m])\n\u001b[0;32m----> 2\u001b[0m \u001b[43mchart\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m chart\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrecipitation flux \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m chart\u001b[38;5;241m.\u001b[39mcoastlines()\n",
+      "File \u001b[0;32m~/Documents/ECMWF/Code/earthkit-plots/src/earthkit/plots/components/subplots.py:597\u001b[0m, in \u001b[0;36mSubplot.plot\u001b[0;34m(self, style, *args, **kwargs)\u001b[0m\n\u001b[1;32m    595\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    596\u001b[0m     method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblock\n\u001b[0;32m--> 597\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/ECMWF/Code/earthkit-plots/src/earthkit/plots/components/subplots.py:227\u001b[0m, in \u001b[0;36mSubplot.plot_3D.<locals>.decorator.<locals>.wrapper\u001b[0;34m(self, data, x, y, z, style, every, **kwargs)\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    219\u001b[0m     data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    225\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    226\u001b[0m ):\n\u001b[0;32m--> 227\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_extract_plottables\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    228\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod_name\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    229\u001b[0m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    230\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    231\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    232\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    233\u001b[0m \u001b[43m        \u001b[49m\u001b[43mz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mz\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    234\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    235\u001b[0m \u001b[43m        \u001b[49m\u001b[43mevery\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevery\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    236\u001b[0m \u001b[43m        \u001b[49m\u001b[43mextract_domain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextract_domain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    237\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    238\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/ECMWF/Code/earthkit-plots/src/earthkit/plots/components/subplots.py:400\u001b[0m, in \u001b[0;36mSubplot._extract_plottables\u001b[0;34m(self, method_name, args, data, x, y, z, style, units, every, source_units, extract_domain, **kwargs)\u001b[0m\n\u001b[1;32m    396\u001b[0m     mappable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(style, method_name)(\n\u001b[1;32m    397\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39max, x_values, y_values, z_values, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    398\u001b[0m     )\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 400\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mgrids\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_structured\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_values\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    401\u001b[0m         x_values, y_values, z_values \u001b[38;5;241m=\u001b[39m grids\u001b[38;5;241m.\u001b[39minterpolate_unstructured(\n\u001b[1;32m    402\u001b[0m             x_values,\n\u001b[1;32m    403\u001b[0m             y_values,\n\u001b[1;32m    404\u001b[0m             z_values,\n\u001b[1;32m    405\u001b[0m             method\u001b[38;5;241m=\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minterpolation_method\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlinear\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    406\u001b[0m         )\n\u001b[1;32m    407\u001b[0m         mappable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(style, method_name)(\n\u001b[1;32m    408\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39max, x_values, y_values, z_values, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    409\u001b[0m         )\n",
+      "File \u001b[0;32m~/Documents/ECMWF/Code/earthkit-plots/src/earthkit/plots/geo/grids.py:65\u001b[0m, in \u001b[0;36mis_structured\u001b[0;34m(x, y, tol)\u001b[0m\n\u001b[1;32m     62\u001b[0m x_diff \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdiff(x)\n\u001b[1;32m     63\u001b[0m y_diff \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdiff(y)\n\u001b[0;32m---> 65\u001b[0m x_spacing_consistent \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mall(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mabs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_diff\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mx_diff\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtol\u001b[49m)\n\u001b[1;32m     66\u001b[0m y_spacing_consistent \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mall(np\u001b[38;5;241m.\u001b[39mabs(y_diff \u001b[38;5;241m-\u001b[39m y_diff[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;241m<\u001b[39m tol)\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x_spacing_consistent \u001b[38;5;129;01mand\u001b[39;00m y_spacing_consistent\n",
+      "\u001b[0;31mUFuncTypeError\u001b[0m: ufunc 'less' did not contain a loop with signature matching types (<class 'numpy.dtypes.TimeDelta64DType'>, <class 'numpy.dtypes.Float64DType'>) -> None"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAscAAAFtCAYAAADrmcc7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAHKUlEQVR4nO3bMWrjUBhG0ZdRQODC4M14DV6y1+DluBCoMEpzpzUaEidhOKf+eXzlbd7btm3bAAAAxp+fHgAAAL+FOAYAgIhjAACIOAYAgIhjAACIOAYAgIhjAADI+97DZVnGuq6v3AIAAC8zz/M4HA5Pb3bF8bIs43q9jsfj8SXDAADgu03TNC6Xy9NA3hXH67qOx+MxzufzOB6PXzYQAAC+w/1+H7fbbazr+vk4/ut4PI7T6fTpcQAA8Bv5kAcAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAAAQcQwAABHHAACQ9385vt/vr9oBAAAvs7djd8XxPM9jmqZxu90+NQoAAH7KNE1jnuenN2/btm17HluWZazr+iXDAADgu83zPA6Hw9Ob3XEMAAD/Ox/yAAAg4hgAACKOAQAg4hgAACKOAQAg4hgAACKOAQAgH+mHO2YQ+CJYAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "chart = earthkit.plots.Map(extent=[-180, 180, -90, 90])\n",
+    "chart.plot(ds[\"pr\"])\n",
+    "chart.title(\"Precipitation flux \")\n",
+    "chart.coastlines()\n",
+    "chart.gridlines()\n",
+    "chart.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f4521020-47fd-47e9-8254-f83321c920c4",
@@ -1163,10 +1281,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 8,
    "id": "962a9a11-d103-4588-ade3-4373c847540c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "----Start loading pyicon.\n",
+      "----Start loading pyicon.\n",
+      "----Pyicon was loaded successfully.\n",
+      "----Pyicon was loaded successfully.\n"
+     ]
+    }
+   ],
    "source": [
     "import pyicon as pyic"
    ]
@@ -1181,7 +1310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 9,
    "id": "d792634e-3a22-4e7d-92f8-f81f66eabbde",
    "metadata": {},
    "outputs": [
@@ -1197,17 +1326,17 @@
      "data": {
       "text/plain": [
        "(<GeoAxesSubplot:title={'left':'level_full = 89.0 ','right':'2020-02-01T00:00:00'}>,\n",
-       " [<cartopy.mpl.geocollection.GeoQuadMesh at 0x33d4150c0>,\n",
-       "  <matplotlib.colorbar.Colorbar at 0x33d638f40>])"
+       " [<cartopy.mpl.geocollection.GeoQuadMesh at 0x1486d9e10>,\n",
+       "  <matplotlib.colorbar.Colorbar at 0x148ebf460>])"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 751.967x480.314 with 2 Axes>"
       ]
@@ -1234,7 +1363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 10,
    "id": "b0371f7d-f7bf-4dd7-be17-830de8e24db9",
    "metadata": {},
    "outputs": [
@@ -1250,17 +1379,17 @@
      "data": {
       "text/plain": [
        "(<GeoAxesSubplot:title={'right':'2020-03-31T00:00:00'}>,\n",
-       " [<cartopy.mpl.geocollection.GeoQuadMesh at 0x33d41e6b0>,\n",
-       "  <matplotlib.colorbar.Colorbar at 0x3322c7100>])"
+       " [<cartopy.mpl.geocollection.GeoQuadMesh at 0x14f8a5240>,\n",
+       "  <matplotlib.colorbar.Colorbar at 0x149351540>])"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 751.967x480.314 with 2 Axes>"
       ]
diff --git a/04-example-datasets/04-ICONXPP.ipynb b/04-example-datasets/04-ICONXPP.ipynb
index d65a780..7af1996 100644
--- a/04-example-datasets/04-ICONXPP.ipynb
+++ b/04-example-datasets/04-ICONXPP.ipynb
@@ -1,7465 +1,281 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
-   "metadata": {},
-   "source": [
-    "# ICON-XPP data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
-   "metadata": {},
-   "source": [
-    "> [!NOTE]\n",
-    "> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n",
-    "\n",
-    "> [!CAUTION]\n",
-    "> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "463bf2c0-ac3f-4786-9687-362393802dce",
-   "metadata": {},
-   "source": [
-    "This notebook is a comprehensive guide to accessing NetDCF files for an ICON-eXtendedPrediction and Projection (XPP) simulation. It includes details about the available parameters and steps, along with instructions for using the `load_ICONXPP_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/ICON-XPP/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## Experiment Details and Available Data Fields\n",
-    "\n",
-    "The dataset consists of output data from an ICON-XPP run, a current state-of-the-art climate-scale simulation.\n",
-    "The simulation was conducted using a horizontal grid spacings of about 80km (ICON grid R2B5).\n",
-    "A global, coupled simulation covering a time period of 1000 years, starting in 1300, was run. Monthly output data was generated throughout the simulation. \n",
-    "The total volume of the available dataset is about 147 GB.\n",
-    "\n",
-    "The dataset is organized into multiple NetCDF files, with each file containing five years of simulation output along with all available parameters.\n",
-    "\n",
-    "The following 2D parameters are available:\n",
-    "```python\n",
-    "[\"pres_msl\", \"pres_sfc\", \"t_s\", \"clct\", \"tot_prec_rate\", \"tqv\", \"tqv_dia\", \"tqc_dia\", \"tqi_dia\", \"umfl_s\", \"vmfl_s\", \"sp_10m\", \"t_2m\", \"sob_t\", \"sod_t\", \"sou_t\", \"thb_t\", \"sob_s\", \"sou_s\", \"thb_s\", \"shfl_s\", \"lhfl_s\", \"snow_con_rate\", \"snow_gsp_rate\", \"ice_gsp_rate\", \"qifl_s\", \"qhfl_s\", \"t_seasfc\", \"fr_land(ncells) ;\", \"fr_seaice\", \"condhf_ice\", \"meltpot_ice\", \"t_ice\", \"h_ice\", \"albvisdif\", \"albvisdir\", \"albnirdif\", \"albnirdir\"]\n",
-    "```\n",
-    "\n",
-    "The available time steps begin at 1300-02-01 00:00:00 UTC, increasing in 1-month steps until 2300-01-01 00:00:00 UTC.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
-   "metadata": {},
-   "source": [
-    "## Using the `load_ICONXPP_data` Function\n",
-    "\n",
-    "To load the NetCDF files, you can use the following function:\n",
-    "\n",
-    "```python\n",
-    "load_ICONXPP_data(year=None, month=None)\n",
-    "```\n",
-    "\n",
-    "### Parameters\n",
-    "\n",
-    "- **`year`**: An integer specifying the year for which data should be loaded. Available years: 1300 to 2300.\n",
-    "- **`month`**(optional): An integer specifying the month for which data should be loaded.\n",
-    "\n",
-    "The datasets start on February 1st for years ending in `00` or `05`. If you need data for January 1st of these years, be sure to specify the appropriate month to load the correct dataset.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# URL of S3 bucket\n",
-    "BASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
-   "metadata": {},
-   "source": [
-    "First, the package `zarr` (modern dataset format that is specifically designed for chunked access) and its dependencies need to be imported for the remote access."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import aiohttp\n",
-    "import dask\n",
-    "import fsspec\n",
-    "import zarr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7b8524c-1fc2-4fd1-94dc-5c9233adae6b",
-   "metadata": {},
-   "source": [
-    "The `load_ICONXPP_data` function simplifies remote access to NetCDF datasets stored on the S3 bucket. It validates input parameters, constructs the correct URL for the requested data, and loads it efficiently as an xarray dataset using the corresponding `.ref` file.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "0b393415-87ca-4dc7-8c72-4cdd101d6621",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "import requests\n",
-    "import xarray as xr\n",
-    "\n",
-    "\n",
-    "def load_ICONXPP_data(year=None, month=None):\n",
-    "    \n",
-    "    if not isinstance(year, int) or year < 1300 or year > 2300:\n",
-    "        raise ValueError(\"Please specify a valid year as an integer between 1300 and 2300.\")\n",
-    "\n",
-    "    if year == 2300:\n",
-    "        if not month:\n",
-    "            print(f\"Warning: Only data for January 1st is available for 2300.\")\n",
-    "        if month and month != 1:\n",
-    "            raise ValueError(f\"Only data for January 1st is available for 2300.\")\n",
-    "            \n",
-    "    if month:\n",
-    "        if not isinstance(month, int) or month < 1 or month > 12:\n",
-    "            raise ValueError(f\"Please specify a valid month as an integer between 1 and 12.\")\n",
-    "\n",
-    "    file_year = (year // 10) * 10\n",
-    "    if not year % 10 <= 4: \n",
-    "        file_year += 5\n",
-    "\n",
-    "\n",
-    "    if not month and year % 5 == 0:\n",
-    "        if year == 1300:\n",
-    "            print(f\"Warning: Data for January 1st of 1300 is not available as is is the simulation starting time.\")\n",
-    "        else:\n",
-    "            print(f\"Warning: Data for January 1st of '{year}' is stored in the previous file. Please specify the month to load the correct dataset.\")\n",
-    "    elif month == 1 and year % 5 == 0:\n",
-    "        if year == 1300:\n",
-    "            raise ValueError(f\"Data for January 1st of 1300 is not available as this is the simulation starting time.\")\n",
-    "        file_year -= 5\n",
-    "        \n",
-    "    if year == 2300:\n",
-    "        file_year = 2295\n",
-    "\n",
-    "    url = f\"{BASE_URL}/ICON-XPP/slo1802_atm_2d_ml_{file_year}0101T000000Z.nc\"\n",
-    "        \n",
-    "    ref = requests.get(f\"{url}.ref\").json()\n",
-    "\n",
-    "    print(f\"Loading dataset {url}\")\n",
-    "\n",
-    "    return xr.open_dataset(\n",
-    "        \"reference://\", \n",
-    "        engine=\"zarr\", \n",
-    "        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n",
-    "        consolidated=False, \n",
-    "        chunks=dict(),\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
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-   "source": [
-    "## Example Usage\n",
-    "\n",
-    "To load NetCDF file for pressure level parameters, you can use the following example. This will load the data for years 2020 to 2025. "
-   ]
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+    {
+      "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
+      "cell_type": "markdown",
+      "source": "# ICON-XPP data",
+      "metadata": {}
+    },
+    {
+      "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
+      "cell_type": "markdown",
+      "source": "> [!NOTE]\n> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n\n> [!CAUTION]\n> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep.",
+      "metadata": {}
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     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/ICON-XPP/slo1802_atm_2d_ml_20200101T000000Z.nc\n"
-     ]
+      "id": "463bf2c0-ac3f-4786-9687-362393802dce",
+      "cell_type": "markdown",
+      "source": "This notebook is a comprehensive guide to accessing NetCDF files for an ICON-eXtendedPrediction and Projection (XPP) simulation. It includes details about the available parameters and steps, along with instructions for using the `load_ICONXPP_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/ICON-XPP/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset consists of output data from an ICON-XPP run, a current state-of-the-art climate-scale simulation.\nThe simulation was conducted using a horizontal grid spacings of about 80km (ICON grid R2B5).\nA global, coupled simulation covering a time period of 1000 years, starting in 1300, was run. Monthly output data was generated throughout the simulation. \nThe total volume of the available dataset is about 147 GB.\n\nThe dataset is organized into multiple NetCDF files, with each file containing five years of simulation output along with all available parameters.\n\nThe following 2D parameters are available:\n```python\n[\"pres_msl\", \"pres_sfc\", \"t_s\", \"clct\", \"tot_prec_rate\", \"tqv\", \"tqv_dia\", \"tqc_dia\", \"tqi_dia\", \"umfl_s\", \"vmfl_s\", \"sp_10m\", \"t_2m\", \"sob_t\", \"sod_t\", \"sou_t\", \"thb_t\", \"sob_s\", \"sou_s\", \"thb_s\", \"shfl_s\", \"lhfl_s\", \"snow_con_rate\", \"snow_gsp_rate\", \"ice_gsp_rate\", \"qifl_s\", \"qhfl_s\", \"t_seasfc\", \"fr_land(ncells) ;\", \"fr_seaice\", \"condhf_ice\", \"meltpot_ice\", \"t_ice\", \"h_ice\", \"albvisdif\", \"albvisdir\", \"albnirdif\", \"albnirdir\"]\n```\n\nThe available time steps begin at 1300-02-01 00:00:00 UTC, increasing in 1-month steps until 2300-01-01 00:00:00 UTC.\n\n",
+      "metadata": {}
     },
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\n",
-       "Coordinates:\n",
-       "    clat           (ncells) float64 dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n",
-       "    clon           (ncells) float64 dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n",
-       "  * height         (height) float64 10.0\n",
-       "  * height_2       (height_2) float64 2.0\n",
-       "  * time           (time) datetime64[ns] 2020-02-01 2020-03-01 ... 2025-01-01\n",
-       "Dimensions without coordinates: ncells, vertices\n",
-       "Data variables: (12/40)\n",
-       "    albnirdif      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    albnirdir      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    albvisdif      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    albvisdir      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    clat_bnds      (ncells, vertices) float64 dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n",
-       "    clct           (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    ...             ...\n",
-       "    tqc_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    tqi_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    tqv            (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    tqv_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    umfl_s         (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    vmfl_s         (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n",
-       "    Conventions:          CF-1.6\n",
-       "    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n",
-       "    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n",
-       "    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n",
-       "    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n",
-       "    number_of_grid_used:  30\n",
-       "    references:           see MPIM/DWD publications\n",
-       "    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n",
-       "    title:                ICON simulation\n",
-       "    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a37c7394-7017-44ec-828c-e155050aeb23' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a37c7394-7017-44ec-828c-e155050aeb23' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7409a493-541f-4bc6-843c-d790f1774dd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7409a493-541f-4bc6-843c-d790f1774dd8' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-c49475fb-1310-4439-b30c-7bb3819495aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c49475fb-1310-4439-b30c-7bb3819495aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8018c7c-a394-445a-bb40-18bbf23f631a' class='xr-var-data-in' type='checkbox'><label for='data-f8018c7c-a394-445a-bb40-18bbf23f631a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 640.00 kiB </td>\n",
-       "                        <td> 640.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (81920,) </td>\n",
-       "                        <td> (81920,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-abff35d4-4a95-4a18-ac5e-5505c8fbc7e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abff35d4-4a95-4a18-ac5e-5505c8fbc7e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-173ff085-b81c-4b9d-b002-59969ced0ba1' class='xr-var-data-in' type='checkbox'><label for='data-173ff085-b81c-4b9d-b002-59969ced0ba1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 640.00 kiB </td>\n",
-       "                        <td> 640.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (81920,) </td>\n",
-       "                        <td> (81920,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-509db153-9b87-49fc-9c46-75d670c16e01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-509db153-9b87-49fc-9c46-75d670c16e01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbf020a2-cca3-48b9-aa6e-1c4d2daf0288' class='xr-var-data-in' type='checkbox'><label for='data-cbf020a2-cca3-48b9-aa6e-1c4d2daf0288' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-d79dbe36-58cc-48b0-bf74-5f1cf8eff22e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d79dbe36-58cc-48b0-bf74-5f1cf8eff22e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8cd0f1b-88f7-4866-99e5-0f3871abe655' class='xr-var-data-in' type='checkbox'><label for='data-d8cd0f1b-88f7-4866-99e5-0f3871abe655' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-01 ... 2025-01-01</div><input id='attrs-aa9ff3ff-de0c-42df-a8f6-5c3796cff6f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa9ff3ff-de0c-42df-a8f6-5c3796cff6f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4b83c01-a6a8-445c-9fde-1cc41d833778' class='xr-var-data-in' type='checkbox'><label for='data-c4b83c01-a6a8-445c-9fde-1cc41d833778' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;, &#x27;2020-03-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-04-01T00:00:00.000000000&#x27;, &#x27;2020-05-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-07-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-08-01T00:00:00.000000000&#x27;, &#x27;2020-09-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-10-01T00:00:00.000000000&#x27;, &#x27;2020-11-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-12-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2021-02-01T00:00:00.000000000&#x27;, &#x27;2021-03-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2021-04-01T00:00:00.000000000&#x27;, &#x27;2021-05-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2021-06-01T00:00:00.000000000&#x27;, &#x27;2021-07-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2021-08-01T00:00:00.000000000&#x27;, &#x27;2021-09-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2021-10-01T00:00:00.000000000&#x27;, &#x27;2021-11-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2021-12-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-02-01T00:00:00.000000000&#x27;, &#x27;2022-03-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-04-01T00:00:00.000000000&#x27;, &#x27;2022-05-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-06-01T00:00:00.000000000&#x27;, &#x27;2022-07-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-08-01T00:00:00.000000000&#x27;, &#x27;2022-09-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-10-01T00:00:00.000000000&#x27;, &#x27;2022-11-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2022-12-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-02-01T00:00:00.000000000&#x27;, &#x27;2023-03-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-04-01T00:00:00.000000000&#x27;, &#x27;2023-05-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-08-01T00:00:00.000000000&#x27;, &#x27;2023-09-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-10-01T00:00:00.000000000&#x27;, &#x27;2023-11-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-12-01T00:00:00.000000000&#x27;, &#x27;2024-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2024-02-01T00:00:00.000000000&#x27;, &#x27;2024-03-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2024-04-01T00:00:00.000000000&#x27;, &#x27;2024-05-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2024-06-01T00:00:00.000000000&#x27;, &#x27;2024-07-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2024-08-01T00:00:00.000000000&#x27;, &#x27;2024-09-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2024-10-01T00:00:00.000000000&#x27;, &#x27;2024-11-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2024-12-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b665a772-2655-4a90-8fdd-11fcabd812ff' class='xr-section-summary-in' type='checkbox'  ><label for='section-b665a772-2655-4a90-8fdd-11fcabd812ff' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b62d873a-3713-46c4-ac2b-d6798dea9ee4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b62d873a-3713-46c4-ac2b-d6798dea9ee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9a3b4b3-3da7-419e-bb5b-14dd7c91674f' class='xr-var-data-in' type='checkbox'><label for='data-a9a3b4b3-3da7-419e-bb5b-14dd7c91674f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
-       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
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-       "  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8ae7d6a2-f3fe-4b7e-8e90-ae8b08884114' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ae7d6a2-f3fe-4b7e-8e90-ae8b08884114' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dd56738-9415-4f3e-b989-ac05a53a967c' class='xr-var-data-in' type='checkbox'><label for='data-5dd56738-9415-4f3e-b989-ac05a53a967c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-dcdbf2d1-043a-4535-a27e-efa609b69ba4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcdbf2d1-043a-4535-a27e-efa609b69ba4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c581252a-cfb6-401c-ab2b-6ff407dc5b6a' class='xr-var-data-in' type='checkbox'><label for='data-c581252a-cfb6-401c-ab2b-6ff407dc5b6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-863d189e-c7fc-4160-9063-75ca1c371402' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-863d189e-c7fc-4160-9063-75ca1c371402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f8c6622-3a83-4e16-945e-c9f9f3cfa166' class='xr-var-data-in' type='checkbox'><label for='data-5f8c6622-3a83-4e16-945e-c9f9f3cfa166' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 81920) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "        </td>\n",
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-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
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-       "                    <tr>\n",
-       "                        <td> </td>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.88 MiB </td>\n",
-       "                        <td> 1.88 MiB </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (81920, 3) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
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-       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a196810f-6500-4a1e-ae6e-4d0fb1bab414' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a196810f-6500-4a1e-ae6e-4d0fb1bab414' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f2cf741-b7a4-47ca-aaa7-0d05797074b4' class='xr-var-data-in' type='checkbox'><label for='data-4f2cf741-b7a4-47ca-aaa7-0d05797074b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-f34e36b3-6210-41a6-ba82-81f8bf6010f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f34e36b3-6210-41a6-ba82-81f8bf6010f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58c610a9-567e-4cc7-b6fe-b1c54a4d3927' class='xr-var-data-in' type='checkbox'><label for='data-58c610a9-567e-4cc7-b6fe-b1c54a4d3927' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.88 MiB </td>\n",
-       "                        <td> 1.88 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (81920, 3) </td>\n",
-       "                        <td> (81920, 3) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <!-- Vertical lines -->\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-401d958f-5b8e-4059-9df7-616c8c9653c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-401d958f-5b8e-4059-9df7-616c8c9653c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23e0e799-1e93-410d-884e-b7c8a0fc0fd9' class='xr-var-data-in' type='checkbox'><label for='data-23e0e799-1e93-410d-884e-b7c8a0fc0fd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-45fb88d4-970b-4d2d-bebf-8176f808acb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45fb88d4-970b-4d2d-bebf-8176f808acb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f17088b-0f50-4244-8106-ce1d5c4a9f94' class='xr-var-data-in' type='checkbox'><label for='data-6f17088b-0f50-4244-8106-ce1d5c4a9f94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (81920,) </td>\n",
-       "                        <td> (81920,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-bcc956f1-ac10-4020-86f3-e031ebe05ac6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcc956f1-ac10-4020-86f3-e031ebe05ac6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcb59d15-7120-4c1c-9179-51d16096c82d' class='xr-var-data-in' type='checkbox'><label for='data-fcb59d15-7120-4c1c-9179-51d16096c82d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5006ebae-c24e-4cdb-92c4-4d5d0c4e1fe3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5006ebae-c24e-4cdb-92c4-4d5d0c4e1fe3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-272b46b9-4c36-48fd-9843-d6e878c3356c' class='xr-var-data-in' type='checkbox'><label for='data-272b46b9-4c36-48fd-9843-d6e878c3356c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2cbf5e0e-be97-48fe-8810-f5a2203effd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cbf5e0e-be97-48fe-8810-f5a2203effd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0191bd32-a66d-4ccc-a36e-ef0664f28178' class='xr-var-data-in' type='checkbox'><label for='data-0191bd32-a66d-4ccc-a36e-ef0664f28178' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
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-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
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-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-0db33c46-74cd-43a5-955a-f317db4a2079' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0db33c46-74cd-43a5-955a-f317db4a2079' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-088199c8-2a01-4d4c-989a-bed7fe671a11' class='xr-var-data-in' type='checkbox'><label for='data-088199c8-2a01-4d4c-989a-bed7fe671a11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
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-       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
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-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
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-       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
-       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
-       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
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-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-01772518-ba80-4df0-8246-26d990710ac0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01772518-ba80-4df0-8246-26d990710ac0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dc9a5b3-228c-4b0a-ab7f-cec5af31348f' class='xr-var-data-in' type='checkbox'><label for='data-5dc9a5b3-228c-4b0a-ab7f-cec5af31348f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-27d10837-2932-4b6d-9a2d-361d1b8616c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27d10837-2932-4b6d-9a2d-361d1b8616c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ce21909-8908-470f-92d8-eb183798c555' class='xr-var-data-in' type='checkbox'><label for='data-7ce21909-8908-470f-92d8-eb183798c555' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ed6d7eab-3cf7-4aac-8e54-78682962e334' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed6d7eab-3cf7-4aac-8e54-78682962e334' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f21af715-4366-43b0-a895-063ef072f18b' class='xr-var-data-in' type='checkbox'><label for='data-f21af715-4366-43b0-a895-063ef072f18b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-47ca8f79-a6a9-4761-862e-81f9b796e885' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47ca8f79-a6a9-4761-862e-81f9b796e885' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e41cdea-3344-40dd-906f-759f97f405ab' class='xr-var-data-in' type='checkbox'><label for='data-7e41cdea-3344-40dd-906f-759f97f405ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
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-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d58bb3ef-2b72-452f-aef8-94c2f63e7182' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d58bb3ef-2b72-452f-aef8-94c2f63e7182' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc7e1c47-bf51-4fbe-be3b-5bd7b4c2671a' class='xr-var-data-in' type='checkbox'><label for='data-fc7e1c47-bf51-4fbe-be3b-5bd7b4c2671a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-29a2bcda-657f-44ed-8ff8-6bb7ee8588eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29a2bcda-657f-44ed-8ff8-6bb7ee8588eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-388deb59-e1cb-46c8-b91f-6df74de096ed' class='xr-var-data-in' type='checkbox'><label for='data-388deb59-e1cb-46c8-b91f-6df74de096ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-11811f08-be68-4a55-adb9-311fdac9c03f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11811f08-be68-4a55-adb9-311fdac9c03f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-484d9166-a6d4-4032-8245-dcbd07a37cca' class='xr-var-data-in' type='checkbox'><label for='data-484d9166-a6d4-4032-8245-dcbd07a37cca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-da07d793-d743-4918-a361-e49c0fe78396' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da07d793-d743-4918-a361-e49c0fe78396' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fc5aece-665a-4951-a239-398c47f5d7f8' class='xr-var-data-in' type='checkbox'><label for='data-9fc5aece-665a-4951-a239-398c47f5d7f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "        </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1a9b92d8-4704-4b31-b3be-b5f8dcb69ec6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a9b92d8-4704-4b31-b3be-b5f8dcb69ec6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cb820cb-15e1-4e3f-8121-c16b67897a70' class='xr-var-data-in' type='checkbox'><label for='data-3cb820cb-15e1-4e3f-8121-c16b67897a70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-432c10b8-1c24-4190-88ec-5066dc12f272' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-432c10b8-1c24-4190-88ec-5066dc12f272' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6288871-3f52-410f-ac36-2d0b3486c97e' class='xr-var-data-in' type='checkbox'><label for='data-e6288871-3f52-410f-ac36-2d0b3486c97e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
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-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-316d1e02-5ba1-4410-9da1-590a9d28144a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-316d1e02-5ba1-4410-9da1-590a9d28144a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b82bb90-e876-495c-96f0-dc5a821cce30' class='xr-var-data-in' type='checkbox'><label for='data-6b82bb90-e876-495c-96f0-dc5a821cce30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
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-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
-       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
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-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f62f10f8-1c93-43e7-8be3-54c97a5bec4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f62f10f8-1c93-43e7-8be3-54c97a5bec4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52071f28-8c55-4410-b16e-9353e37ae7e4' class='xr-var-data-in' type='checkbox'><label for='data-52071f28-8c55-4410-b16e-9353e37ae7e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-14c82697-6def-4f21-95be-8ddf4a69b061' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14c82697-6def-4f21-95be-8ddf4a69b061' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff7c9e37-2f23-4e93-b780-dfcf3044bd69' class='xr-var-data-in' type='checkbox'><label for='data-ff7c9e37-2f23-4e93-b780-dfcf3044bd69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
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-       "                    <tr>\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 81920) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
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-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8d928bd7-264c-49ea-adc4-19f0fe108408' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d928bd7-264c-49ea-adc4-19f0fe108408' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e306701-2fd9-4312-8707-faf05c537d88' class='xr-var-data-in' type='checkbox'><label for='data-3e306701-2fd9-4312-8707-faf05c537d88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                    \n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d61f4415-54e0-4d4a-b2f6-a33cf476c917' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d61f4415-54e0-4d4a-b2f6-a33cf476c917' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2abc5415-0dea-423d-81f9-8f55007f29ec' class='xr-var-data-in' type='checkbox'><label for='data-2abc5415-0dea-423d-81f9-8f55007f29ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
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-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-21022fda-1530-4e39-b3e5-b0b8bdcd9291' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21022fda-1530-4e39-b3e5-b0b8bdcd9291' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-292a9aef-12bf-42f4-8731-2e1aa9e931cd' class='xr-var-data-in' type='checkbox'><label for='data-292a9aef-12bf-42f4-8731-2e1aa9e931cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
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-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2c5dc45a-69f9-4906-b98b-bfc232ee7d33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c5dc45a-69f9-4906-b98b-bfc232ee7d33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e249d6b2-e94f-4c16-bc91-a05c72bd3797' class='xr-var-data-in' type='checkbox'><label for='data-e249d6b2-e94f-4c16-bc91-a05c72bd3797' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
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-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1237eef7-8584-4850-9272-ad928cc5cab2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1237eef7-8584-4850-9272-ad928cc5cab2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44009e32-8852-462f-9971-7b4b6c33a680' class='xr-var-data-in' type='checkbox'><label for='data-44009e32-8852-462f-9971-7b4b6c33a680' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "        <td>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-068c62b2-5ca1-4882-b631-a2cf0ba69ace' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-068c62b2-5ca1-4882-b631-a2cf0ba69ace' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8aa078b-70a1-4849-986f-0de50fad507d' class='xr-var-data-in' type='checkbox'><label for='data-a8aa078b-70a1-4849-986f-0de50fad507d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4317796d-d418-44ac-a86a-13558dd3a064' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4317796d-d418-44ac-a86a-13558dd3a064' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e7b6a5d-bbfb-4794-a4da-8c63f22383ac' class='xr-var-data-in' type='checkbox'><label for='data-2e7b6a5d-bbfb-4794-a4da-8c63f22383ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-29cb2849-f93a-4ba4-86c8-4263ab1c95f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29cb2849-f93a-4ba4-86c8-4263ab1c95f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-326489e1-c117-4a4c-a4d9-c0f31c1c4cac' class='xr-var-data-in' type='checkbox'><label for='data-326489e1-c117-4a4c-a4d9-c0f31c1c4cac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
-       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
-       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
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-       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
-       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-444e2194-978c-48fc-b53b-d7d31fa02bea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-444e2194-978c-48fc-b53b-d7d31fa02bea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3dd18e8-3cde-414b-a326-f8aa924fb17e' class='xr-var-data-in' type='checkbox'><label for='data-f3dd18e8-3cde-414b-a326-f8aa924fb17e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "        </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4981f804-849f-46b8-9362-f4bcaddcabef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4981f804-849f-46b8-9362-f4bcaddcabef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8bc098d-f354-4bc8-a4ce-26baba7f97ac' class='xr-var-data-in' type='checkbox'><label for='data-e8bc098d-f354-4bc8-a4ce-26baba7f97ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ae098b48-fc41-4bb5-b2c7-11358abb8b5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae098b48-fc41-4bb5-b2c7-11358abb8b5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d0a6478-81d3-4fc8-adde-d30b5f0db687' class='xr-var-data-in' type='checkbox'><label for='data-8d0a6478-81d3-4fc8-adde-d30b5f0db687' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a145e69e-6a3e-4274-9309-6a9c72552923' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a145e69e-6a3e-4274-9309-6a9c72552923' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f6a7153-de7f-48db-be27-938973c2b1e7' class='xr-var-data-in' type='checkbox'><label for='data-4f6a7153-de7f-48db-be27-938973c2b1e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "        </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-55fd2e7f-f3f9-4aee-9fe3-278fbdd1a093' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55fd2e7f-f3f9-4aee-9fe3-278fbdd1a093' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25609a10-1e3c-4811-a291-a65da07c8d46' class='xr-var-data-in' type='checkbox'><label for='data-25609a10-1e3c-4811-a291-a65da07c8d46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
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-       "                        <th> Shape </th>\n",
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-       "                        <td> (1, 81920) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "</svg>\n",
-       "        </td>\n",
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-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-3bacf534-9bbe-4456-a5c7-b2052c3ef8a1' class='xr-section-summary-in' type='checkbox'  ><label for='section-3bacf534-9bbe-4456-a5c7-b2052c3ef8a1' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4b2141d1-92e6-4005-b302-334450fa13a3' class='xr-index-data-in' type='checkbox'/><label for='index-4b2141d1-92e6-4005-b302-334450fa13a3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4f369337-a1c0-47f9-8afe-f698b9cb5a05' class='xr-index-data-in' type='checkbox'/><label for='index-4f369337-a1c0-47f9-8afe-f698b9cb5a05' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a1d1b1e8-2477-4e11-86aa-8188119f69a0' class='xr-index-data-in' type='checkbox'/><label for='index-a1d1b1e8-2477-4e11-86aa-8188119f69a0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-02-01&#x27;, &#x27;2020-03-01&#x27;, &#x27;2020-04-01&#x27;, &#x27;2020-05-01&#x27;,\n",
-       "               &#x27;2020-06-01&#x27;, &#x27;2020-07-01&#x27;, &#x27;2020-08-01&#x27;, &#x27;2020-09-01&#x27;,\n",
-       "               &#x27;2020-10-01&#x27;, &#x27;2020-11-01&#x27;, &#x27;2020-12-01&#x27;, &#x27;2021-01-01&#x27;,\n",
-       "               &#x27;2021-02-01&#x27;, &#x27;2021-03-01&#x27;, &#x27;2021-04-01&#x27;, &#x27;2021-05-01&#x27;,\n",
-       "               &#x27;2021-06-01&#x27;, &#x27;2021-07-01&#x27;, &#x27;2021-08-01&#x27;, &#x27;2021-09-01&#x27;,\n",
-       "               &#x27;2021-10-01&#x27;, &#x27;2021-11-01&#x27;, &#x27;2021-12-01&#x27;, &#x27;2022-01-01&#x27;,\n",
-       "               &#x27;2022-02-01&#x27;, &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;,\n",
-       "               &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;,\n",
-       "               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n",
-       "               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;, &#x27;2023-05-01&#x27;,\n",
-       "               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;, &#x27;2023-08-01&#x27;, &#x27;2023-09-01&#x27;,\n",
-       "               &#x27;2023-10-01&#x27;, &#x27;2023-11-01&#x27;, &#x27;2023-12-01&#x27;, &#x27;2024-01-01&#x27;,\n",
-       "               &#x27;2024-02-01&#x27;, &#x27;2024-03-01&#x27;, &#x27;2024-04-01&#x27;, &#x27;2024-05-01&#x27;,\n",
-       "               &#x27;2024-06-01&#x27;, &#x27;2024-07-01&#x27;, &#x27;2024-08-01&#x27;, &#x27;2024-09-01&#x27;,\n",
-       "               &#x27;2024-10-01&#x27;, &#x27;2024-11-01&#x27;, &#x27;2024-12-01&#x27;, &#x27;2025-01-01&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-86d0ea22-ec28-4617-86b8-3819f504d7a2' class='xr-section-summary-in' type='checkbox'  ><label for='section-86d0ea22-ec28-4617-86b8-3819f504d7a2' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 044638</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
+      "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
+      "cell_type": "markdown",
+      "source": "## Using the `load_ICONXPP_data` Function\n\nTo load the NetCDF files, you can use the following function:\n\n```python\nload_ICONXPP_data(year=None, month=None)\n```\n\n### Parameters\n\n- **`year`**: An integer specifying the year for which data should be loaded. Available years: 1300 to 2300.\n- **`month`**(optional): An integer specifying the month for which data should be loaded.\n\nThe datasets start on February 1st for years ending in `00` or `05`. If you need data for January 1st of these years, be sure to specify the appropriate month to load the correct dataset.\n",
+      "metadata": {}
+    },
+    {
+      "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
+      "cell_type": "code",
+      "source": "# URL of S3 bucket\nBASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\"",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": 1
+    },
+    {
+      "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
+      "cell_type": "markdown",
+      "source": "First, the package `zarr` (modern dataset format that is specifically designed for chunked access) and its dependencies need to be imported for the remote access.",
+      "metadata": {}
+    },
+    {
+      "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
+      "cell_type": "code",
+      "source": "import aiohttp\nimport cftime\nimport dask\nimport fsspec\nimport zarr",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Loading Jinja2, MarkupSafe, aiohttp, aiosignal, asciitree, async-timeout, attrs, cftime, click, cloudpickle, dask, frozenlist, fsspec, importlib_metadata, locket, msgpack, multidict, numcodecs, numpy, partd, propcache, pyyaml, toolz, yarl, zarr, zipp\n[pyodide]: Loaded Jinja2, MarkupSafe, aiohttp, aiosignal, asciitree, async-timeout, attrs, cftime, click, cloudpickle, dask, frozenlist, fsspec, importlib_metadata, locket, msgpack, multidict, numcodecs, numpy, partd, propcache, pyyaml, toolz, yarl, zarr, zipp\n[pyodide]: Memory usage has grown to 71.9MiB (from 49.9MiB) for this notebook\n",
+          "output_type": "stream"
+        }
       ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\n",
-       "Coordinates:\n",
-       "    clat           (ncells) float64 dask.array<chunksize=(81920,), meta=np.ndarray>\n",
-       "    clon           (ncells) float64 dask.array<chunksize=(81920,), meta=np.ndarray>\n",
-       "  * height         (height) float64 10.0\n",
-       "  * height_2       (height_2) float64 2.0\n",
-       "  * time           (time) datetime64[ns] 2020-02-01 2020-03-01 ... 2025-01-01\n",
-       "Dimensions without coordinates: ncells, vertices\n",
-       "Data variables: (12/40)\n",
-       "    albnirdif      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    albnirdir      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    albvisdif      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    albvisdir      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    clat_bnds      (ncells, vertices) float64 dask.array<chunksize=(81920, 3), meta=np.ndarray>\n",
-       "    clct           (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    ...             ...\n",
-       "    tqc_dia        (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    tqi_dia        (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    tqv            (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    tqv_dia        (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    umfl_s         (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    vmfl_s         (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n",
-       "    Conventions:          CF-1.6\n",
-       "    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n",
-       "    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n",
-       "    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n",
-       "    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n",
-       "    number_of_grid_used:  30\n",
-       "    references:           see MPIM/DWD publications\n",
-       "    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n",
-       "    title:                ICON simulation\n",
-       "    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_2020 = load_ICONXPP_data(year=2024)\n",
-    "ds_2020"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4521020-47fd-47e9-8254-f83321c920c4",
-   "metadata": {},
-   "source": [
-    "The `earthkit` package is used for plotting the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "962a9a11-d103-4588-ade3-4373c847540c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import earthkit.plots\n",
-    "import earthkit.plots.quickmap"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "99449502-9fb2-4097-89b0-d63634a0e250",
-   "metadata": {},
-   "source": [
-    "The data can be visualized as demonstrated below. For example, the 2 metre temperature on **2024-12-01 at 00:00:00 UTC** is plotted. `[::10]` applies slicing to the data being plotted (syntax: `[start:stop:step]`) in order to reduce computational load by selecting only every 10th data point.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "d792634e-3a22-4e7d-92f8-f81f66eabbde",
-   "metadata": {},
-   "outputs": [
+      "execution_count": 2
+    },
     {
-     "data": {
-      "text/plain": [
-       "<earthkit.plots.components.maps.Map at 0x17b1d9e70>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+      "id": "b7b8524c-1fc2-4fd1-94dc-5c9233adae6b",
+      "cell_type": "markdown",
+      "source": "The `load_ICONXPP_data` function simplifies remote access to NetCDF datasets stored on the S3 bucket. It validates input parameters, constructs the correct URL for the requested data, and loads it efficiently as an xarray dataset using the corresponding `.ref` file.\n",
+      "metadata": {}
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "earthkit.plots.quickmap.plot(\n",
-    "    ds_2020[\"t_2m\"].sel(time=np.datetime64(\"2024-12-01 00:00:00\"))[::10], \n",
-    "    x=\"clon\", y=\"clat\"\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9ee0a8b1-6ecf-4306-99b0-93f8e65b164b",
-   "metadata": {},
-   "source": [
-    "If interested in data on **2020-01-01 at 00:00:00 UTC** the previous NetCDF file needs to be loaded. This will automatically be done by specifying the month as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "30d45387-9af5-4a4b-b4bd-d026f04e644d",
-   "metadata": {},
-   "outputs": [
+      "id": "0b393415-87ca-4dc7-8c72-4cdd101d6621",
+      "cell_type": "code",
+      "source": "import json\n\nimport requests\nimport xarray as xr\n\n\ndef load_ICONXPP_data(year=None, month=None):\n    \n    if not isinstance(year, int) or year < 1300 or year > 2300:\n        raise ValueError(\"Please specify a valid year as an integer between 1300 and 2300.\")\n\n    if year == 2300:\n        if not month:\n            print(f\"Warning: Only data for January 1st is available for 2300.\")\n        if month and month != 1:\n            raise ValueError(f\"Only data for January 1st is available for 2300.\")\n            \n    if month:\n        if not isinstance(month, int) or month < 1 or month > 12:\n            raise ValueError(f\"Please specify a valid month as an integer between 1 and 12.\")\n\n    file_year = (year // 10) * 10\n    if not year % 10 <= 4: \n        file_year += 5\n\n\n    if not month and year % 5 == 0:\n        if year == 1300:\n            print(f\"Warning: Data for January 1st of 1300 is not available as is is the simulation starting time.\")\n        else:\n            print(f\"Warning: Data for January 1st of '{year}' is stored in the previous file. Please specify the month to load the correct dataset.\")\n    elif month == 1 and year % 5 == 0:\n        if year == 1300:\n            raise ValueError(f\"Data for January 1st of 1300 is not available as this is the simulation starting time.\")\n        file_year -= 5\n        \n    if year == 2300:\n        file_year = 2295\n\n    url = f\"{BASE_URL}/ICON-XPP/slo1802_atm_2d_ml_{file_year}0101T000000Z.nc\"\n        \n    ref = requests.get(f\"{url}.ref\").json()\n\n    print(f\"Loading dataset {url}\")\n\n    return xr.open_dataset(\n        \"reference://\", \n        engine=\"zarr\", \n        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n        consolidated=False, \n        chunks=dict(),\n    )",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Loading pandas, python-dateutil, pytz, tzdata, xarray\n[pyodide]: Loaded pandas, python-dateutil, pytz, tzdata, xarray\n[pyodide]: Memory usage has grown to 124.2MiB (from 71.9MiB) for this notebook\n",
+          "output_type": "stream"
+        }
+      ],
+      "execution_count": 3
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/ICON-XPP/slo1802_atm_2d_ml_20150101T000000Z.nc\n"
-     ]
+      "id": "62adb1fd-1fd1-4c59-a0c3-66710f4a31d1",
+      "cell_type": "markdown",
+      "source": "## Example Usage\n\nTo load NetCDF file for pressure level parameters, you can use the following example. This will load the data for years 2020 to 2025. ",
+      "metadata": {}
     },
     {
-     "data": {
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-       "  padding: 0;\n",
-       "  margin: 0;\n",
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-       "\n",
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-       "\n",
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-       "\n",
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-       "\n",
-       ".xr-icon-database,\n",
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-       ".xr-no-icon {\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\n",
-       "Coordinates:\n",
-       "    clat           (ncells) float64 dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n",
-       "    clon           (ncells) float64 dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n",
-       "  * height         (height) float64 10.0\n",
-       "  * height_2       (height_2) float64 2.0\n",
-       "  * time           (time) datetime64[ns] 2015-02-01 2015-03-01 ... 2020-01-01\n",
-       "Dimensions without coordinates: ncells, vertices\n",
-       "Data variables: (12/40)\n",
-       "    albnirdif      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    albnirdir      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    albvisdif      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    albvisdir      (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    clat_bnds      (ncells, vertices) float64 dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n",
-       "    clct           (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    ...             ...\n",
-       "    tqc_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    tqi_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    tqv            (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    tqv_dia        (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    umfl_s         (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "    vmfl_s         (time, ncells) float32 dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n",
-       "    Conventions:          CF-1.6\n",
-       "    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n",
-       "    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n",
-       "    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n",
-       "    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n",
-       "    number_of_grid_used:  30\n",
-       "    references:           see MPIM/DWD publications\n",
-       "    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n",
-       "    title:                ICON simulation\n",
-       "    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-baf985aa-89da-44df-9907-8f8af73af912' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-baf985aa-89da-44df-9907-8f8af73af912' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-77321b0d-10dd-4974-a72a-9b26edfde0bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-77321b0d-10dd-4974-a72a-9b26edfde0bf' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-4009552f-0132-4ece-b003-d7373f88136b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4009552f-0132-4ece-b003-d7373f88136b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fae88e02-8bba-4f5a-85f9-b0cf90ec0faa' class='xr-var-data-in' type='checkbox'><label for='data-fae88e02-8bba-4f5a-85f9-b0cf90ec0faa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 640.00 kiB </td>\n",
-       "                        <td> 640.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (81920,) </td>\n",
-       "                        <td> (81920,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-8a6da4de-86fa-4ea0-81e3-1c13f42833cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a6da4de-86fa-4ea0-81e3-1c13f42833cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a9d9265-a8f7-45c9-863d-7803d043f581' class='xr-var-data-in' type='checkbox'><label for='data-6a9d9265-a8f7-45c9-863d-7803d043f581' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 640.00 kiB </td>\n",
-       "                        <td> 640.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (81920,) </td>\n",
-       "                        <td> (81920,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-6c66adc7-3e3a-4816-ba4b-5a08a4c0a4db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c66adc7-3e3a-4816-ba4b-5a08a4c0a4db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7af8a6e-cf35-4ced-943f-805285a858a2' class='xr-var-data-in' type='checkbox'><label for='data-a7af8a6e-cf35-4ced-943f-805285a858a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-01e6a118-275e-4829-8657-5678341665f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01e6a118-275e-4829-8657-5678341665f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3549940-875d-4526-a10b-2e836b6fc84f' class='xr-var-data-in' type='checkbox'><label for='data-a3549940-875d-4526-a10b-2e836b6fc84f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-02-01 ... 2020-01-01</div><input id='attrs-ea859484-57e9-4353-b285-725e1253ec91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea859484-57e9-4353-b285-725e1253ec91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1889b282-f6d1-4bb4-b810-e7ecda75eaa0' class='xr-var-data-in' type='checkbox'><label for='data-1889b282-f6d1-4bb4-b810-e7ecda75eaa0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2016-12-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2017-02-01T00:00:00.000000000&#x27;, &#x27;2017-03-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2017-04-01T00:00:00.000000000&#x27;, &#x27;2017-05-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2017-06-01T00:00:00.000000000&#x27;, &#x27;2017-07-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2017-08-01T00:00:00.000000000&#x27;, &#x27;2017-09-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2017-10-01T00:00:00.000000000&#x27;, &#x27;2017-11-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2017-12-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-02-01T00:00:00.000000000&#x27;, &#x27;2018-03-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-04-01T00:00:00.000000000&#x27;, &#x27;2018-05-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-06-01T00:00:00.000000000&#x27;, &#x27;2018-07-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-08-01T00:00:00.000000000&#x27;, &#x27;2018-09-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-10-01T00:00:00.000000000&#x27;, &#x27;2018-11-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-12-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2019-04-01T00:00:00.000000000&#x27;, &#x27;2019-05-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2019-06-01T00:00:00.000000000&#x27;, &#x27;2019-07-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2019-08-01T00:00:00.000000000&#x27;, &#x27;2019-09-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2019-10-01T00:00:00.000000000&#x27;, &#x27;2019-11-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2019-12-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3154bbcc-a59e-4593-88b6-28d78b540ce5' class='xr-section-summary-in' type='checkbox'  ><label for='section-3154bbcc-a59e-4593-88b6-28d78b540ce5' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5a71daad-992f-4770-8a64-145a55740898' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a71daad-992f-4770-8a64-145a55740898' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be42fa0a-3369-4b1a-abcc-3877d9204925' class='xr-var-data-in' type='checkbox'><label for='data-be42fa0a-3369-4b1a-abcc-3877d9204925' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "\n",
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-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-e5aaed40-335a-4c73-8359-849f9bc9f291' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5aaed40-335a-4c73-8359-849f9bc9f291' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb4baeb9-243f-4179-b271-827b220f54b4' class='xr-var-data-in' type='checkbox'><label for='data-cb4baeb9-243f-4179-b271-827b220f54b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4a47e8ef-621c-4796-ba52-fbd1b430045c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a47e8ef-621c-4796-ba52-fbd1b430045c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d9a7ba4-f285-4b0f-a438-46501155138f' class='xr-var-data-in' type='checkbox'><label for='data-7d9a7ba4-f285-4b0f-a438-46501155138f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d2eb229f-aafd-405d-a4a3-7139a8eabc5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2eb229f-aafd-405d-a4a3-7139a8eabc5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b86d05b-fef9-4fbc-b240-b7738f5330d8' class='xr-var-data-in' type='checkbox'><label for='data-6b86d05b-fef9-4fbc-b240-b7738f5330d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
-       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
-       "  <!-- Text -->\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-6e3650b7-2868-407f-8812-d94ac183b565' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e3650b7-2868-407f-8812-d94ac183b565' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f5907ae-ecd6-45e5-a7cc-2e43874624a6' class='xr-var-data-in' type='checkbox'><label for='data-8f5907ae-ecd6-45e5-a7cc-2e43874624a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.88 MiB </td>\n",
-       "                        <td> 1.88 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (81920, 3) </td>\n",
-       "                        <td> (81920, 3) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-e534ce09-86f3-4987-879f-d7d1d0fc740d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e534ce09-86f3-4987-879f-d7d1d0fc740d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bbb69e9-43f0-4255-81b7-d11d8d3baa99' class='xr-var-data-in' type='checkbox'><label for='data-5bbb69e9-43f0-4255-81b7-d11d8d3baa99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
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-       "        </td>\n",
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-       "\n",
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-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-158e3e50-6bea-4afd-8d9a-2111f89dd9b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-158e3e50-6bea-4afd-8d9a-2111f89dd9b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-110c1106-d9af-477a-9170-3195269d18b2' class='xr-var-data-in' type='checkbox'><label for='data-110c1106-d9af-477a-9170-3195269d18b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.88 MiB </td>\n",
-       "                        <td> 1.88 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (81920, 3) </td>\n",
-       "                        <td> (81920, 3) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-44a11c54-cc42-414b-9556-e18d9c05cca0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44a11c54-cc42-414b-9556-e18d9c05cca0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ed0f065-29e4-47bd-8109-6994f33208c0' class='xr-var-data-in' type='checkbox'><label for='data-3ed0f065-29e4-47bd-8109-6994f33208c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n",
-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-ec20f9d4-dbb5-4bdb-8a38-05ad8514248a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec20f9d4-dbb5-4bdb-8a38-05ad8514248a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38adcc3a-cbbc-4a11-b2f4-188ee83c82eb' class='xr-var-data-in' type='checkbox'><label for='data-38adcc3a-cbbc-4a11-b2f4-188ee83c82eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (81920,) </td>\n",
-       "                        <td> (81920,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
-       "  <!-- Text -->\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-20a125fa-afb6-4e52-91ed-2b58e89482dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20a125fa-afb6-4e52-91ed-2b58e89482dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a2e54c8-25ec-47c2-8f72-eb45ae1cc639' class='xr-var-data-in' type='checkbox'><label for='data-7a2e54c8-25ec-47c2-8f72-eb45ae1cc639' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
-       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
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-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
-       "  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n",
-       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
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-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
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-       "  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n",
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-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f83de878-99c0-4b5c-8557-f327b015e0d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f83de878-99c0-4b5c-8557-f327b015e0d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7e14d01-28d6-4b13-8146-199937972672' class='xr-var-data-in' type='checkbox'><label for='data-e7e14d01-28d6-4b13-8146-199937972672' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-54204515-0717-45df-bc23-dd725321caec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54204515-0717-45df-bc23-dd725321caec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3abb627-f43e-473e-aa18-f3319f16eede' class='xr-var-data-in' type='checkbox'><label for='data-e3abb627-f43e-473e-aa18-f3319f16eede' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3b683c65-fcea-4308-93e6-fb62a0d8f0c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b683c65-fcea-4308-93e6-fb62a0d8f0c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0505990-7f9a-4c5c-a55c-8c44990dd07a' class='xr-var-data-in' type='checkbox'><label for='data-b0505990-7f9a-4c5c-a55c-8c44990dd07a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-6afa9071-6891-417f-8932-8c6fc95919f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6afa9071-6891-417f-8932-8c6fc95919f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22de8f57-ca6a-4c03-a961-9bf90349ce49' class='xr-var-data-in' type='checkbox'><label for='data-22de8f57-ca6a-4c03-a961-9bf90349ce49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n",
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-       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
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-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-58af160f-719f-4860-869f-6ed8d0069bd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58af160f-719f-4860-869f-6ed8d0069bd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b513caa2-e465-4527-b7a5-4bff96308294' class='xr-var-data-in' type='checkbox'><label for='data-b513caa2-e465-4527-b7a5-4bff96308294' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-99e224f5-5025-4bc4-9c34-3423c860b882' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99e224f5-5025-4bc4-9c34-3423c860b882' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b38b3e46-4d4b-46df-a701-506ac41cfb0a' class='xr-var-data-in' type='checkbox'><label for='data-b38b3e46-4d4b-46df-a701-506ac41cfb0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a37781b2-d2da-4302-b5b6-6215bf962e26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a37781b2-d2da-4302-b5b6-6215bf962e26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faebc55e-8741-4d02-93da-74ff7bea0340' class='xr-var-data-in' type='checkbox'><label for='data-faebc55e-8741-4d02-93da-74ff7bea0340' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-7a0cd087-6ac2-4a67-a19d-3d7df27c8b7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a0cd087-6ac2-4a67-a19d-3d7df27c8b7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f439afc-bb33-4dc8-a736-f351d1059291' class='xr-var-data-in' type='checkbox'><label for='data-4f439afc-bb33-4dc8-a736-f351d1059291' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "            </table>\n",
-       "        </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-dc099be8-1ff9-4ab1-96f3-f59d3693a818' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc099be8-1ff9-4ab1-96f3-f59d3693a818' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b95b6960-0baa-4bb2-a9e2-b488cc9c49c4' class='xr-var-data-in' type='checkbox'><label for='data-b95b6960-0baa-4bb2-a9e2-b488cc9c49c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ca39c122-9f76-40a9-9294-ef5dfaf55fd5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca39c122-9f76-40a9-9294-ef5dfaf55fd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dacb22f-7960-4434-94f5-99ffe3204fdc' class='xr-var-data-in' type='checkbox'><label for='data-1dacb22f-7960-4434-94f5-99ffe3204fdc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-99b8d6bb-1d3a-4217-984a-81b1d7dd1d9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99b8d6bb-1d3a-4217-984a-81b1d7dd1d9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-149abd9c-0260-4c4f-bb88-d14fb97a3825' class='xr-var-data-in' type='checkbox'><label for='data-149abd9c-0260-4c4f-bb88-d14fb97a3825' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-bed9aab2-11c5-45b6-9414-4cca3e5394c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bed9aab2-11c5-45b6-9414-4cca3e5394c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5760cd25-17a2-4628-a0eb-97f288924838' class='xr-var-data-in' type='checkbox'><label for='data-5760cd25-17a2-4628-a0eb-97f288924838' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-0a6111fa-3d3b-43f2-be3f-ade8dd3a0aea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a6111fa-3d3b-43f2-be3f-ade8dd3a0aea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44215e69-d125-494d-a1aa-c43eab0fce56' class='xr-var-data-in' type='checkbox'><label for='data-44215e69-d125-494d-a1aa-c43eab0fce56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-143250f1-72b8-48bb-82ac-3809d83947fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-143250f1-72b8-48bb-82ac-3809d83947fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e126cd8e-d008-4178-9a6c-0a165df0ee41' class='xr-var-data-in' type='checkbox'><label for='data-e126cd8e-d008-4178-9a6c-0a165df0ee41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
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-       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f522d01b-2487-40eb-b090-335dacb0855d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f522d01b-2487-40eb-b090-335dacb0855d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-270f7d32-b1f3-4d5c-a8dd-32abb7909009' class='xr-var-data-in' type='checkbox'><label for='data-270f7d32-b1f3-4d5c-a8dd-32abb7909009' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n",
-       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
-       "  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n",
-       "  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
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-       "  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n",
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-       "  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
-       "  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n",
-       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
-       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
-       "  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
-       "  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n",
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-       "  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c0d1ff4c-51cb-4786-849e-8b7b424b5a78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0d1ff4c-51cb-4786-849e-8b7b424b5a78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd037c37-81e9-4bf0-8460-aead6463ca40' class='xr-var-data-in' type='checkbox'><label for='data-cd037c37-81e9-4bf0-8460-aead6463ca40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8d0b5aa9-4a35-4775-8661-ba3e34cd388a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d0b5aa9-4a35-4775-8661-ba3e34cd388a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bea07493-320c-4bde-9c06-088019113091' class='xr-var-data-in' type='checkbox'><label for='data-bea07493-320c-4bde-9c06-088019113091' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-343d7728-7be5-4834-88aa-0093656234ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-343d7728-7be5-4834-88aa-0093656234ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18bec288-304d-43bb-a476-b358fe4b7295' class='xr-var-data-in' type='checkbox'><label for='data-18bec288-304d-43bb-a476-b358fe4b7295' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
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-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-292acec5-7f89-4625-af1a-ce5896cb2327' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-292acec5-7f89-4625-af1a-ce5896cb2327' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49904d6f-61c1-4738-8e70-7da39b46774e' class='xr-var-data-in' type='checkbox'><label for='data-49904d6f-61c1-4738-8e70-7da39b46774e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-79221507-2bac-4f26-9e25-f2d4049f35cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79221507-2bac-4f26-9e25-f2d4049f35cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-846d89d2-7276-4b9d-a488-52684e257345' class='xr-var-data-in' type='checkbox'><label for='data-846d89d2-7276-4b9d-a488-52684e257345' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-deecbb0c-5722-4bd0-95dc-3d5a796b8e57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-deecbb0c-5722-4bd0-95dc-3d5a796b8e57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4804befe-e1ae-4d3a-8c95-9395649883ec' class='xr-var-data-in' type='checkbox'><label for='data-4804befe-e1ae-4d3a-8c95-9395649883ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f8a2cf4c-51d4-42b6-bab7-08c82ec5ab46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8a2cf4c-51d4-42b6-bab7-08c82ec5ab46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8df38a7b-b549-4c90-ac98-ede599419609' class='xr-var-data-in' type='checkbox'><label for='data-8df38a7b-b549-4c90-ac98-ede599419609' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-313d5a39-7f7f-4b8f-b3ef-f58fc9637b52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-313d5a39-7f7f-4b8f-b3ef-f58fc9637b52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ceecaf5-c5e9-48d8-adee-2e90f3d230d3' class='xr-var-data-in' type='checkbox'><label for='data-8ceecaf5-c5e9-48d8-adee-2e90f3d230d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-616356f9-fd2e-4e16-a4cc-8ce1062ecc2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-616356f9-fd2e-4e16-a4cc-8ce1062ecc2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14402d26-223f-4dcb-9df4-2a0b85980048' class='xr-var-data-in' type='checkbox'><label for='data-14402d26-223f-4dcb-9df4-2a0b85980048' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3a9f2ef0-2730-431f-be46-eb2b106ad6c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a9f2ef0-2730-431f-be46-eb2b106ad6c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e5865b8-8b29-466d-845d-e2250a9b4527' class='xr-var-data-in' type='checkbox'><label for='data-3e5865b8-8b29-466d-845d-e2250a9b4527' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a7a0d57f-40e8-4766-ad3d-c5cdd65427fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7a0d57f-40e8-4766-ad3d-c5cdd65427fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5be4ea3-1c0d-4f4b-84e0-63a4c034eb54' class='xr-var-data-in' type='checkbox'><label for='data-d5be4ea3-1c0d-4f4b-84e0-63a4c034eb54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-45b3c401-0bd1-4f77-91e6-8d9dcf20fe4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45b3c401-0bd1-4f77-91e6-8d9dcf20fe4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3abeca91-c1f7-4537-98a4-727fc04b9edc' class='xr-var-data-in' type='checkbox'><label for='data-3abeca91-c1f7-4537-98a4-727fc04b9edc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
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-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-06db749b-ed47-4bd9-bf2f-4dc234ffd219' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06db749b-ed47-4bd9-bf2f-4dc234ffd219' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3119589-bc62-4a4c-9112-9fb6b2092d06' class='xr-var-data-in' type='checkbox'><label for='data-c3119589-bc62-4a4c-9112-9fb6b2092d06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n",
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-       "  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n",
-       "  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n",
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-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
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-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ac793310-edf1-4fcf-95ec-07811f950e76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac793310-edf1-4fcf-95ec-07811f950e76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-659c0864-1b1f-4bb2-ba2c-71a9da0c0d75' class='xr-var-data-in' type='checkbox'><label for='data-659c0864-1b1f-4bb2-ba2c-71a9da0c0d75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 18.75 MiB </td>\n",
-       "                        <td> 320.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (60, 81920) </td>\n",
-       "                        <td> (1, 81920) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-261b963c-2849-4a97-a8b0-229e827c581f' class='xr-section-summary-in' type='checkbox'  ><label for='section-261b963c-2849-4a97-a8b0-229e827c581f' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-589a14c9-74a9-4c4d-9976-ae294fe9c5c9' class='xr-index-data-in' type='checkbox'/><label for='index-589a14c9-74a9-4c4d-9976-ae294fe9c5c9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-31686d0e-22e6-4daa-a1b0-df8c8c09be91' class='xr-index-data-in' type='checkbox'/><label for='index-31686d0e-22e6-4daa-a1b0-df8c8c09be91' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bd25388f-1a54-4331-aa23-29974054e37d' class='xr-index-data-in' type='checkbox'/><label for='index-bd25388f-1a54-4331-aa23-29974054e37d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2015-02-01&#x27;, &#x27;2015-03-01&#x27;, &#x27;2015-04-01&#x27;, &#x27;2015-05-01&#x27;,\n",
-       "               &#x27;2015-06-01&#x27;, &#x27;2015-07-01&#x27;, &#x27;2015-08-01&#x27;, &#x27;2015-09-01&#x27;,\n",
-       "               &#x27;2015-10-01&#x27;, &#x27;2015-11-01&#x27;, &#x27;2015-12-01&#x27;, &#x27;2016-01-01&#x27;,\n",
-       "               &#x27;2016-02-01&#x27;, &#x27;2016-03-01&#x27;, &#x27;2016-04-01&#x27;, &#x27;2016-05-01&#x27;,\n",
-       "               &#x27;2016-06-01&#x27;, &#x27;2016-07-01&#x27;, &#x27;2016-08-01&#x27;, &#x27;2016-09-01&#x27;,\n",
-       "               &#x27;2016-10-01&#x27;, &#x27;2016-11-01&#x27;, &#x27;2016-12-01&#x27;, &#x27;2017-01-01&#x27;,\n",
-       "               &#x27;2017-02-01&#x27;, &#x27;2017-03-01&#x27;, &#x27;2017-04-01&#x27;, &#x27;2017-05-01&#x27;,\n",
-       "               &#x27;2017-06-01&#x27;, &#x27;2017-07-01&#x27;, &#x27;2017-08-01&#x27;, &#x27;2017-09-01&#x27;,\n",
-       "               &#x27;2017-10-01&#x27;, &#x27;2017-11-01&#x27;, &#x27;2017-12-01&#x27;, &#x27;2018-01-01&#x27;,\n",
-       "               &#x27;2018-02-01&#x27;, &#x27;2018-03-01&#x27;, &#x27;2018-04-01&#x27;, &#x27;2018-05-01&#x27;,\n",
-       "               &#x27;2018-06-01&#x27;, &#x27;2018-07-01&#x27;, &#x27;2018-08-01&#x27;, &#x27;2018-09-01&#x27;,\n",
-       "               &#x27;2018-10-01&#x27;, &#x27;2018-11-01&#x27;, &#x27;2018-12-01&#x27;, &#x27;2019-01-01&#x27;,\n",
-       "               &#x27;2019-02-01&#x27;, &#x27;2019-03-01&#x27;, &#x27;2019-04-01&#x27;, &#x27;2019-05-01&#x27;,\n",
-       "               &#x27;2019-06-01&#x27;, &#x27;2019-07-01&#x27;, &#x27;2019-08-01&#x27;, &#x27;2019-09-01&#x27;,\n",
-       "               &#x27;2019-10-01&#x27;, &#x27;2019-11-01&#x27;, &#x27;2019-12-01&#x27;, &#x27;2020-01-01&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9c18235e-39dd-4391-b9d8-eef52baf19f3' class='xr-section-summary-in' type='checkbox'  ><label for='section-9c18235e-39dd-4391-b9d8-eef52baf19f3' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 020510</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
+      "id": "510bad61-a98c-495b-a9fa-365887b9f1b6",
+      "cell_type": "code",
+      "source": "ds_2020 = load_ICONXPP_data(year=2024)\nds_2020",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/ICON-XPP/slo1802_atm_2d_ml_20200101T000000Z.nc\n",
+          "output_type": "stream"
+        },
+        {
+          "execution_count": 4,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "<xarray.Dataset> Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB dask.array<chunksize=(81920,), meta=np.ndarray>\n    clon           (ncells) float64 655kB dask.array<chunksize=(81920,), meta=np.ndarray>\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2020-02-01 ... 2025-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albnirdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    clat_bnds      (ncells, vertices) float64 2MB dask.array<chunksize=(81920, 3), meta=np.ndarray>\n    clct           (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqi_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv            (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    umfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    vmfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n    clon           (ncells) float64 655kB dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2020-02-01 ... 2025-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albnirdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    clat_bnds      (ncells, vertices) float64 2MB dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n    clct           (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqi_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv            (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    umfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    vmfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c06e3e7c-80a6-4989-b4ea-75e008f836c9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c06e3e7c-80a6-4989-b4ea-75e008f836c9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-456e8874-7d08-4bce-bc37-967f02292cc6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-456e8874-7d08-4bce-bc37-967f02292cc6' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-9b252d2a-a9f7-4b1f-9e2a-ca4d2e7cb949' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b252d2a-a9f7-4b1f-9e2a-ca4d2e7cb949' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f84c5656-99e5-4887-8f61-cc7884831b0b' class='xr-var-data-in' type='checkbox'><label for='data-f84c5656-99e5-4887-8f61-cc7884831b0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640.00 kiB </td>\n                        <td> 640.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-a5c40df4-f15d-48a7-8a2e-bd9f856520f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5c40df4-f15d-48a7-8a2e-bd9f856520f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-450efcea-6d7f-4fbe-b72d-9cfe2ddd6bcc' class='xr-var-data-in' type='checkbox'><label for='data-450efcea-6d7f-4fbe-b72d-9cfe2ddd6bcc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640.00 kiB </td>\n                        <td> 640.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-b2b11ba5-1b60-4f63-a6e8-733db5879c18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2b11ba5-1b60-4f63-a6e8-733db5879c18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2189aa34-740a-48ba-b766-19ac838f3d5e' class='xr-var-data-in' type='checkbox'><label for='data-2189aa34-740a-48ba-b766-19ac838f3d5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-e4266a6f-e37a-4a64-a1ae-5058fde1bbc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4266a6f-e37a-4a64-a1ae-5058fde1bbc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a32e90f0-f81d-4588-8eb9-1558f66bc46f' class='xr-var-data-in' type='checkbox'><label for='data-a32e90f0-f81d-4588-8eb9-1558f66bc46f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-01 ... 2025-01-01</div><input id='attrs-7f0dd66d-72d0-4873-ad85-1182ffc50d59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f0dd66d-72d0-4873-ad85-1182ffc50d59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f6bce05-14c5-4297-9d4b-6432b8f24c8c' class='xr-var-data-in' type='checkbox'><label for='data-8f6bce05-14c5-4297-9d4b-6432b8f24c8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;, &#x27;2020-03-01T00:00:00.000000000&#x27;,\n       &#x27;2020-04-01T00:00:00.000000000&#x27;, &#x27;2020-05-01T00:00:00.000000000&#x27;,\n       &#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-07-01T00:00:00.000000000&#x27;,\n       &#x27;2020-08-01T00:00:00.000000000&#x27;, &#x27;2020-09-01T00:00:00.000000000&#x27;,\n       &#x27;2020-10-01T00:00:00.000000000&#x27;, &#x27;2020-11-01T00:00:00.000000000&#x27;,\n       &#x27;2020-12-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n       &#x27;2021-02-01T00:00:00.000000000&#x27;, &#x27;2021-03-01T00:00:00.000000000&#x27;,\n       &#x27;2021-04-01T00:00:00.000000000&#x27;, &#x27;2021-05-01T00:00:00.000000000&#x27;,\n       &#x27;2021-06-01T00:00:00.000000000&#x27;, &#x27;2021-07-01T00:00:00.000000000&#x27;,\n       &#x27;2021-08-01T00:00:00.000000000&#x27;, &#x27;2021-09-01T00:00:00.000000000&#x27;,\n       &#x27;2021-10-01T00:00:00.000000000&#x27;, &#x27;2021-11-01T00:00:00.000000000&#x27;,\n       &#x27;2021-12-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:00:00.000000000&#x27;,\n       &#x27;2022-02-01T00:00:00.000000000&#x27;, &#x27;2022-03-01T00:00:00.000000000&#x27;,\n       &#x27;2022-04-01T00:00:00.000000000&#x27;, &#x27;2022-05-01T00:00:00.000000000&#x27;,\n       &#x27;2022-06-01T00:00:00.000000000&#x27;, &#x27;2022-07-01T00:00:00.000000000&#x27;,\n       &#x27;2022-08-01T00:00:00.000000000&#x27;, &#x27;2022-09-01T00:00:00.000000000&#x27;,\n       &#x27;2022-10-01T00:00:00.000000000&#x27;, &#x27;2022-11-01T00:00:00.000000000&#x27;,\n       &#x27;2022-12-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n       &#x27;2023-02-01T00:00:00.000000000&#x27;, &#x27;2023-03-01T00:00:00.000000000&#x27;,\n       &#x27;2023-04-01T00:00:00.000000000&#x27;, &#x27;2023-05-01T00:00:00.000000000&#x27;,\n       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;,\n       &#x27;2023-08-01T00:00:00.000000000&#x27;, &#x27;2023-09-01T00:00:00.000000000&#x27;,\n       &#x27;2023-10-01T00:00:00.000000000&#x27;, &#x27;2023-11-01T00:00:00.000000000&#x27;,\n       &#x27;2023-12-01T00:00:00.000000000&#x27;, &#x27;2024-01-01T00:00:00.000000000&#x27;,\n       &#x27;2024-02-01T00:00:00.000000000&#x27;, &#x27;2024-03-01T00:00:00.000000000&#x27;,\n       &#x27;2024-04-01T00:00:00.000000000&#x27;, &#x27;2024-05-01T00:00:00.000000000&#x27;,\n       &#x27;2024-06-01T00:00:00.000000000&#x27;, &#x27;2024-07-01T00:00:00.000000000&#x27;,\n       &#x27;2024-08-01T00:00:00.000000000&#x27;, &#x27;2024-09-01T00:00:00.000000000&#x27;,\n       &#x27;2024-10-01T00:00:00.000000000&#x27;, &#x27;2024-11-01T00:00:00.000000000&#x27;,\n       &#x27;2024-12-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b608e2f0-ee60-4230-b438-ec04469ba971' class='xr-section-summary-in' type='checkbox'  ><label for='section-b608e2f0-ee60-4230-b438-ec04469ba971' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5a2acaf8-8790-476a-9342-6c478b4d77b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a2acaf8-8790-476a-9342-6c478b4d77b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f600963-4e7e-4c8b-9273-6eeb6b939731' class='xr-var-data-in' type='checkbox'><label for='data-8f600963-4e7e-4c8b-9273-6eeb6b939731' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  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text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a8cfca9e-eec4-4fe4-befd-318ac354a536' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8cfca9e-eec4-4fe4-befd-318ac354a536' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f878127-b84b-40fd-b378-c3f590246bf8' class='xr-var-data-in' type='checkbox'><label for='data-6f878127-b84b-40fd-b378-c3f590246bf8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-37d9cb64-d7d9-4de6-a995-7b6a3cbf5f8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37d9cb64-d7d9-4de6-a995-7b6a3cbf5f8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-741b0ccd-3736-4ec5-873c-7876efee87fe' class='xr-var-data-in' type='checkbox'><label for='data-741b0ccd-3736-4ec5-873c-7876efee87fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-9817fc7c-ea71-455f-892f-2cf94ace7ca9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9817fc7c-ea71-455f-892f-2cf94ace7ca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eeb2cdf2-e735-4d91-9563-4fdfdd876635' class='xr-var-data-in' type='checkbox'><label for='data-eeb2cdf2-e735-4d91-9563-4fdfdd876635' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  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text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-173fae23-f25d-4984-a4e2-c73cce78a8f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-173fae23-f25d-4984-a4e2-c73cce78a8f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-debb3889-b168-463e-9295-d0541c20f660' class='xr-var-data-in' type='checkbox'><label for='data-debb3889-b168-463e-9295-d0541c20f660' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d8aff872-80e5-46f8-9298-ad8f47bd12f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8aff872-80e5-46f8-9298-ad8f47bd12f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ae41f7e-7fbd-4507-ade1-ed3e2ee70299' class='xr-var-data-in' type='checkbox'><label for='data-4ae41f7e-7fbd-4507-ade1-ed3e2ee70299' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-f2a1b5ba-b5b6-4a7d-9f82-83d068a727b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2a1b5ba-b5b6-4a7d-9f82-83d068a727b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e0d11bc-c62a-4469-84ee-3137b5de1167' class='xr-var-data-in' type='checkbox'><label for='data-6e0d11bc-c62a-4469-84ee-3137b5de1167' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-002c2e95-40de-4838-99bf-e37810bf8789' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-002c2e95-40de-4838-99bf-e37810bf8789' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b7168fc-180b-4d68-884a-2635421de1b5' class='xr-var-data-in' type='checkbox'><label for='data-3b7168fc-180b-4d68-884a-2635421de1b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-3bd8bc90-1e65-4dbb-ab75-cb0cb263f052' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bd8bc90-1e65-4dbb-ab75-cb0cb263f052' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75766267-84b9-4b37-8287-93150393820a' class='xr-var-data-in' type='checkbox'><label for='data-75766267-84b9-4b37-8287-93150393820a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320.00 kiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4e05197a-6b9f-48d0-b1e7-7d1b4f5bed6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e05197a-6b9f-48d0-b1e7-7d1b4f5bed6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f2b106a-a017-49bf-b301-7a98197d96bc' class='xr-var-data-in' type='checkbox'><label for='data-6f2b106a-a017-49bf-b301-7a98197d96bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d68025d7-0398-464f-9365-bfa326c7dc26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d68025d7-0398-464f-9365-bfa326c7dc26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-346e23bb-0604-4db9-aa08-176ccebed694' class='xr-var-data-in' type='checkbox'><label for='data-346e23bb-0604-4db9-aa08-176ccebed694' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3578247a-dc81-42ea-b3f5-e966d576400a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3578247a-dc81-42ea-b3f5-e966d576400a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f8cb8a6-854c-41f0-ab0e-3dad6d804811' class='xr-var-data-in' type='checkbox'><label for='data-6f8cb8a6-854c-41f0-ab0e-3dad6d804811' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3a40ba6f-bb13-4cca-b7e8-3684dcc91be2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a40ba6f-bb13-4cca-b7e8-3684dcc91be2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9acde312-e22c-4889-868e-71098335f778' class='xr-var-data-in' type='checkbox'><label for='data-9acde312-e22c-4889-868e-71098335f778' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b96408d7-f35f-4f50-94db-c894aef80853' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b96408d7-f35f-4f50-94db-c894aef80853' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97145d88-9b1b-4a93-b033-6e3c451a4b88' class='xr-var-data-in' type='checkbox'><label for='data-97145d88-9b1b-4a93-b033-6e3c451a4b88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-0d93906a-1e9e-4502-8673-cda09c421aac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d93906a-1e9e-4502-8673-cda09c421aac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9f5a692-f0e6-419c-b7b6-f75cf4b60dcf' class='xr-var-data-in' type='checkbox'><label for='data-f9f5a692-f0e6-419c-b7b6-f75cf4b60dcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2baa6c4c-7e39-46bc-9470-78d36a69e853' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2baa6c4c-7e39-46bc-9470-78d36a69e853' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d9cd978-ce49-4ca9-a43d-f2d3e14a454a' class='xr-var-data-in' type='checkbox'><label for='data-2d9cd978-ce49-4ca9-a43d-f2d3e14a454a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-daf834ce-2742-499a-81d9-427a663ddb78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-daf834ce-2742-499a-81d9-427a663ddb78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c0e8fca-0261-4e1c-b50b-da351e9718c3' class='xr-var-data-in' type='checkbox'><label for='data-6c0e8fca-0261-4e1c-b50b-da351e9718c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1dcaecf9-b7c8-4dd2-958d-ff15c674ce6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dcaecf9-b7c8-4dd2-958d-ff15c674ce6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-294777d1-89ab-4975-b7c8-4af623e1dde9' class='xr-var-data-in' type='checkbox'><label for='data-294777d1-89ab-4975-b7c8-4af623e1dde9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-28405db9-438a-49f3-a38d-5687814dc5c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28405db9-438a-49f3-a38d-5687814dc5c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e861e825-da98-4a82-a3a3-1d59411c1b08' class='xr-var-data-in' type='checkbox'><label for='data-e861e825-da98-4a82-a3a3-1d59411c1b08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-fc8cbfdb-b305-402f-86d4-998a1805253b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc8cbfdb-b305-402f-86d4-998a1805253b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1971643c-0265-4bfc-80c3-8b75770b6423' class='xr-var-data-in' type='checkbox'><label for='data-1971643c-0265-4bfc-80c3-8b75770b6423' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-fda5433d-be5a-426d-9513-f890e4c23708' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fda5433d-be5a-426d-9513-f890e4c23708' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0119bdb3-f86b-4c9d-85e7-2515e06c0460' class='xr-var-data-in' type='checkbox'><label for='data-0119bdb3-f86b-4c9d-85e7-2515e06c0460' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f61edf90-3676-4bba-9c37-6805d0dc4a74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f61edf90-3676-4bba-9c37-6805d0dc4a74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6fa9556-6c24-4c7f-9b32-b48adc256736' class='xr-var-data-in' type='checkbox'><label for='data-a6fa9556-6c24-4c7f-9b32-b48adc256736' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-489e591b-de4f-4e3d-af30-ec1a1572f689' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-489e591b-de4f-4e3d-af30-ec1a1572f689' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e91ebefb-5bb4-4ea4-a399-2ce1e2bedbfe' class='xr-var-data-in' type='checkbox'><label for='data-e91ebefb-5bb4-4ea4-a399-2ce1e2bedbfe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-04b7141e-339e-4de6-bdfd-3cf431d80e2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04b7141e-339e-4de6-bdfd-3cf431d80e2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe5a70ba-b620-4154-90c3-1b897e54ee3a' class='xr-var-data-in' type='checkbox'><label for='data-fe5a70ba-b620-4154-90c3-1b897e54ee3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  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y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-403e0407-6db4-419d-b0b2-1d8dafc65424' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-403e0407-6db4-419d-b0b2-1d8dafc65424' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59f08ce6-bb42-49c4-8034-2efbba9724d1' class='xr-var-data-in' type='checkbox'><label for='data-59f08ce6-bb42-49c4-8034-2efbba9724d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-81cebf7f-4367-4111-a113-5d6836553a65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81cebf7f-4367-4111-a113-5d6836553a65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-036edb19-bb73-4fdc-9aeb-bb98b9148a88' class='xr-var-data-in' type='checkbox'><label for='data-036edb19-bb73-4fdc-9aeb-bb98b9148a88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp_10m</span></div><div class='xr-var-dims'>(time, height, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2001b65f-1cdc-4ee2-8cc5-bd7538475b5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2001b65f-1cdc-4ee2-8cc5-bd7538475b5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3446941-9355-49b6-b054-a42ef994fbcb' class='xr-var-data-in' type='checkbox'><label for='data-d3446941-9355-49b6-b054-a42ef994fbcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>wind speed in 10m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.0</dd><dt><span>standard_name :</span></dt><dd>sp_10m</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"26\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"26\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"27\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"28\" />\n  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"29\" />\n  <line x1=\"15\" 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Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d7d89ed2-7c3a-4c26-a02b-ba7edf54b30c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7d89ed2-7c3a-4c26-a02b-ba7edf54b30c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-827763a2-876c-407e-9685-d02c3d027074' class='xr-var-data-in' type='checkbox'><label for='data-827763a2-876c-407e-9685-d02c3d027074' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- 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-->\n  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"24\" y1=\"40\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,40.36121446433689 24.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c58b545f-10da-4fa4-8238-d3170f2728e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c58b545f-10da-4fa4-8238-d3170f2728e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f756bd4-4a92-4221-990f-2fec9283cfb2' class='xr-var-data-in' type='checkbox'><label for='data-8f756bd4-4a92-4221-990f-2fec9283cfb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" 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0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-43441f6e-0c3c-4c03-b003-c4b6021e0834' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43441f6e-0c3c-4c03-b003-c4b6021e0834' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33455098-4d5a-496b-9b27-a7c825575eea' class='xr-var-data-in' type='checkbox'><label for='data-33455098-4d5a-496b-9b27-a7c825575eea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-585c41a1-3cfd-4fe2-acbd-9b82ad58f6b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-585c41a1-3cfd-4fe2-acbd-9b82ad58f6b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80285bb2-523d-42de-bc31-0b30012ec750' class='xr-var-data-in' type='checkbox'><label for='data-80285bb2-523d-42de-bc31-0b30012ec750' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-9b6f68ed-5d74-440d-a869-1f947b001ce6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b6f68ed-5d74-440d-a869-1f947b001ce6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b77ab42d-3137-4298-b901-dac64536a020' class='xr-var-data-in' type='checkbox'><label for='data-b77ab42d-3137-4298-b901-dac64536a020' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4da36a73-9eda-4df6-9eed-1104607c66c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4da36a73-9eda-4df6-9eed-1104607c66c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7508e94-54b6-4876-b963-074d369dbd10' class='xr-var-data-in' type='checkbox'><label for='data-b7508e94-54b6-4876-b963-074d369dbd10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b0832eba-fd1a-4e82-a460-eb5e74d4ca06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0832eba-fd1a-4e82-a460-eb5e74d4ca06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24cabfd5-1f6c-4819-b6f1-a15b048a5ccd' class='xr-var-data-in' type='checkbox'><label for='data-24cabfd5-1f6c-4819-b6f1-a15b048a5ccd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-434de76e-1340-4e5c-9b5a-f5131c016003' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-434de76e-1340-4e5c-9b5a-f5131c016003' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50f3092c-a66e-4e78-a103-d4e8ad28c0df' class='xr-var-data-in' type='checkbox'><label for='data-50f3092c-a66e-4e78-a103-d4e8ad28c0df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-7ca9b9e8-276f-4ae4-a0de-2040c4582edf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ca9b9e8-276f-4ae4-a0de-2040c4582edf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6ab685f-e292-40df-8e4c-07f4748c98a4' class='xr-var-data-in' type='checkbox'><label for='data-c6ab685f-e292-40df-8e4c-07f4748c98a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a49ef13b-1cf9-4eff-8bea-904cb0b52c4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a49ef13b-1cf9-4eff-8bea-904cb0b52c4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-795839d7-9b23-4339-ad04-b8c9c84202d0' class='xr-var-data-in' type='checkbox'><label for='data-795839d7-9b23-4339-ad04-b8c9c84202d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-79e1da75-6750-422e-9986-12d12a75ddb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79e1da75-6750-422e-9986-12d12a75ddb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbe7804a-92ae-4c4f-b258-6963d37741ca' class='xr-var-data-in' type='checkbox'><label for='data-dbe7804a-92ae-4c4f-b258-6963d37741ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-33f744af-f084-40de-967d-9fad812a3ada' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33f744af-f084-40de-967d-9fad812a3ada' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dddf0bd-bce4-4f5b-b934-0e5c8bb3a143' class='xr-var-data-in' type='checkbox'><label for='data-6dddf0bd-bce4-4f5b-b934-0e5c8bb3a143' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f9f46bdf-8a35-479f-8827-0fe2651babb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9f46bdf-8a35-479f-8827-0fe2651babb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea0a33c7-d18e-43ce-81cc-61bd13b75008' class='xr-var-data-in' type='checkbox'><label for='data-ea0a33c7-d18e-43ce-81cc-61bd13b75008' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1798ef50-bc30-4d1f-ae8e-5ddf4688beae' class='xr-section-summary-in' type='checkbox'  ><label for='section-1798ef50-bc30-4d1f-ae8e-5ddf4688beae' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b680d75c-8636-4ea6-b5f0-ce74b8d5a00f' class='xr-index-data-in' type='checkbox'/><label for='index-b680d75c-8636-4ea6-b5f0-ce74b8d5a00f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aba72c83-fe2f-45d2-b5fa-2c9ea8e62a36' class='xr-index-data-in' type='checkbox'/><label for='index-aba72c83-fe2f-45d2-b5fa-2c9ea8e62a36' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ff5b5410-8315-4625-ac3c-248cd1061d03' class='xr-index-data-in' type='checkbox'/><label for='index-ff5b5410-8315-4625-ac3c-248cd1061d03' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-02-01&#x27;, &#x27;2020-03-01&#x27;, &#x27;2020-04-01&#x27;, &#x27;2020-05-01&#x27;,\n               &#x27;2020-06-01&#x27;, &#x27;2020-07-01&#x27;, &#x27;2020-08-01&#x27;, &#x27;2020-09-01&#x27;,\n               &#x27;2020-10-01&#x27;, &#x27;2020-11-01&#x27;, &#x27;2020-12-01&#x27;, &#x27;2021-01-01&#x27;,\n               &#x27;2021-02-01&#x27;, &#x27;2021-03-01&#x27;, &#x27;2021-04-01&#x27;, &#x27;2021-05-01&#x27;,\n               &#x27;2021-06-01&#x27;, &#x27;2021-07-01&#x27;, &#x27;2021-08-01&#x27;, &#x27;2021-09-01&#x27;,\n               &#x27;2021-10-01&#x27;, &#x27;2021-11-01&#x27;, &#x27;2021-12-01&#x27;, &#x27;2022-01-01&#x27;,\n               &#x27;2022-02-01&#x27;, &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;,\n               &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;,\n               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;, &#x27;2023-05-01&#x27;,\n               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;, &#x27;2023-08-01&#x27;, &#x27;2023-09-01&#x27;,\n               &#x27;2023-10-01&#x27;, &#x27;2023-11-01&#x27;, &#x27;2023-12-01&#x27;, &#x27;2024-01-01&#x27;,\n               &#x27;2024-02-01&#x27;, &#x27;2024-03-01&#x27;, &#x27;2024-04-01&#x27;, &#x27;2024-05-01&#x27;,\n               &#x27;2024-06-01&#x27;, &#x27;2024-07-01&#x27;, &#x27;2024-08-01&#x27;, &#x27;2024-09-01&#x27;,\n               &#x27;2024-10-01&#x27;, &#x27;2024-11-01&#x27;, &#x27;2024-12-01&#x27;, &#x27;2025-01-01&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3774f532-06f0-41d9-b094-816ec578d172' class='xr-section-summary-in' type='checkbox'  ><label for='section-3774f532-06f0-41d9-b094-816ec578d172' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 044638</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\n",
-       "Coordinates:\n",
-       "    clat           (ncells) float64 dask.array<chunksize=(81920,), meta=np.ndarray>\n",
-       "    clon           (ncells) float64 dask.array<chunksize=(81920,), meta=np.ndarray>\n",
-       "  * height         (height) float64 10.0\n",
-       "  * height_2       (height_2) float64 2.0\n",
-       "  * time           (time) datetime64[ns] 2015-02-01 2015-03-01 ... 2020-01-01\n",
-       "Dimensions without coordinates: ncells, vertices\n",
-       "Data variables: (12/40)\n",
-       "    albnirdif      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    albnirdir      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    albvisdif      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    albvisdir      (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    clat_bnds      (ncells, vertices) float64 dask.array<chunksize=(81920, 3), meta=np.ndarray>\n",
-       "    clct           (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    ...             ...\n",
-       "    tqc_dia        (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    tqi_dia        (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    tqv            (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    tqv_dia        (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    umfl_s         (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "    vmfl_s         (time, ncells) float32 dask.array<chunksize=(1, 81920), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n",
-       "    Conventions:          CF-1.6\n",
-       "    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n",
-       "    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n",
-       "    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n",
-       "    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n",
-       "    number_of_grid_used:  30\n",
-       "    references:           see MPIM/DWD publications\n",
-       "    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n",
-       "    title:                ICON simulation\n",
-       "    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_2015 = load_ICONXPP_data(year=2020, month=1)\n",
-    "ds_2015"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8110a212-0814-4b9c-8a32-9d6379c6a6c0",
-   "metadata": {},
-   "source": [
-    "The data can be visualized as demonstrated below. For example, the total precipitation rate on **2020-01-01 at 00:00:00 UTC** is plotted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "6750630a-8b1b-4a35-8cb7-8323a55dbf1b",
-   "metadata": {},
-   "outputs": [
+      "execution_count": 4
+    },
     {
-     "data": {
-      "text/plain": [
-       "<earthkit.plots.components.maps.Map at 0x320140250>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
+      "id": "f4521020-47fd-47e9-8254-f83321c920c4",
+      "cell_type": "markdown",
+      "source": "The `earthkit` package is used for plotting the data.",
+      "metadata": {}
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 700x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "id": "962a9a11-d103-4588-ade3-4373c847540c",
+      "cell_type": "code",
+      "source": "import earthkit.plots\nimport earthkit.plots.quickmap",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Loading Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n[pyodide]: Loaded Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stderr",
+          "text": "/lib/python3.12/site-packages/earthkit/regrid/utils/caching.py:27: UserWarning: only soft file lock is available\n  from filelock import FileLock\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 309.4MiB (from 124.2MiB) for this notebook\n",
+          "output_type": "stream"
+        }
+      ],
+      "execution_count": 5
+    },
+    {
+      "id": "99449502-9fb2-4097-89b0-d63634a0e250",
+      "cell_type": "markdown",
+      "source": "The data can be visualized as demonstrated below. For example, the 2 metre temperature on **2024-12-01 at 00:00:00 UTC** is plotted.\n",
+      "metadata": {}
+    },
+    {
+      "id": "d792634e-3a22-4e7d-92f8-f81f66eabbde",
+      "cell_type": "code",
+      "source": "import numpy as np\n\nchart = earthkit.plots.quickmap.plot(\n    ds_2020[\"t_2m\"].sel(time=\"2024-12-01 00:00:00\"), \n    x=\"clon\", y=\"clat\"\n)\nchart.coastlines()\nchart.show()\n",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "text": "/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip\n  warnings.warn(f'Downloading: {url}', DownloadWarning)\n",
+          "output_type": "stream"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 700x600 with 2 Axes>",
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 534.6MiB (from 309.4MiB) for this notebook\n",
+          "output_type": "stream"
+        }
+      ],
+      "execution_count": 6
+    },
+    {
+      "id": "9ee0a8b1-6ecf-4306-99b0-93f8e65b164b",
+      "cell_type": "markdown",
+      "source": "If interested in data on **2020-01-01 at 00:00:00 UTC** the previous NetCDF file needs to be loaded. This will automatically be done by specifying the month as well.",
+      "metadata": {}
+    },
+    {
+      "id": "30d45387-9af5-4a4b-b4bd-d026f04e644d",
+      "cell_type": "code",
+      "source": "ds_2015 = load_ICONXPP_data(year=2020, month=1)\nds_2015",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/ICON-XPP/slo1802_atm_2d_ml_20150101T000000Z.nc\n",
+          "output_type": "stream"
+        },
+        {
+          "execution_count": 7,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "<xarray.Dataset> Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB dask.array<chunksize=(81920,), meta=np.ndarray>\n    clon           (ncells) float64 655kB dask.array<chunksize=(81920,), meta=np.ndarray>\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2015-02-01 ... 2020-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albnirdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    clat_bnds      (ncells, vertices) float64 2MB dask.array<chunksize=(81920, 3), meta=np.ndarray>\n    clct           (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqi_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv            (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    umfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    vmfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n    clon           (ncells) float64 655kB dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2015-02-01 ... 2020-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albnirdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    clat_bnds      (ncells, vertices) float64 2MB dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n    clct           (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqi_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv            (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    umfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    vmfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-679c9595-4861-42ce-9015-75cb6a819728' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-679c9595-4861-42ce-9015-75cb6a819728' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7e4ed73c-630a-4201-aaab-bc5a25700fc5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e4ed73c-630a-4201-aaab-bc5a25700fc5' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-ea4d2bd2-9f2b-433e-9337-5ac8651863f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea4d2bd2-9f2b-433e-9337-5ac8651863f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2de778e3-083d-47f8-b17b-6f22d2c4961d' class='xr-var-data-in' type='checkbox'><label for='data-2de778e3-083d-47f8-b17b-6f22d2c4961d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640.00 kiB </td>\n                        <td> 640.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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:</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640.00 kiB </td>\n                        <td> 640.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-dd7f4a8c-03e7-42a9-a3f5-32d3a3755a20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd7f4a8c-03e7-42a9-a3f5-32d3a3755a20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2e14e23-b0a0-44c9-be08-bd9c3ccfee2d' class='xr-var-data-in' type='checkbox'><label for='data-c2e14e23-b0a0-44c9-be08-bd9c3ccfee2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-99992352-bf20-43ca-a0fd-dc7aacbadbfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99992352-bf20-43ca-a0fd-dc7aacbadbfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4533736-8ee3-4472-a44d-ffb4410d6d80' class='xr-var-data-in' type='checkbox'><label for='data-f4533736-8ee3-4472-a44d-ffb4410d6d80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-02-01 ... 2020-01-01</div><input id='attrs-a3070bd4-bbe3-40bd-9f57-8b9a2bf2b3ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3070bd4-bbe3-40bd-9f57-8b9a2bf2b3ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe84d01e-8b33-4621-91c9-25187d0107ef' class='xr-var-data-in' type='checkbox'><label for='data-fe84d01e-8b33-4621-91c9-25187d0107ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n       &#x27;2016-12-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n       &#x27;2017-02-01T00:00:00.000000000&#x27;, &#x27;2017-03-01T00:00:00.000000000&#x27;,\n       &#x27;2017-04-01T00:00:00.000000000&#x27;, &#x27;2017-05-01T00:00:00.000000000&#x27;,\n       &#x27;2017-06-01T00:00:00.000000000&#x27;, &#x27;2017-07-01T00:00:00.000000000&#x27;,\n       &#x27;2017-08-01T00:00:00.000000000&#x27;, &#x27;2017-09-01T00:00:00.000000000&#x27;,\n       &#x27;2017-10-01T00:00:00.000000000&#x27;, &#x27;2017-11-01T00:00:00.000000000&#x27;,\n       &#x27;2017-12-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n       &#x27;2018-02-01T00:00:00.000000000&#x27;, &#x27;2018-03-01T00:00:00.000000000&#x27;,\n       &#x27;2018-04-01T00:00:00.000000000&#x27;, &#x27;2018-05-01T00:00:00.000000000&#x27;,\n       &#x27;2018-06-01T00:00:00.000000000&#x27;, &#x27;2018-07-01T00:00:00.000000000&#x27;,\n       &#x27;2018-08-01T00:00:00.000000000&#x27;, &#x27;2018-09-01T00:00:00.000000000&#x27;,\n       &#x27;2018-10-01T00:00:00.000000000&#x27;, &#x27;2018-11-01T00:00:00.000000000&#x27;,\n       &#x27;2018-12-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;,\n       &#x27;2019-04-01T00:00:00.000000000&#x27;, &#x27;2019-05-01T00:00:00.000000000&#x27;,\n       &#x27;2019-06-01T00:00:00.000000000&#x27;, &#x27;2019-07-01T00:00:00.000000000&#x27;,\n       &#x27;2019-08-01T00:00:00.000000000&#x27;, &#x27;2019-09-01T00:00:00.000000000&#x27;,\n       &#x27;2019-10-01T00:00:00.000000000&#x27;, &#x27;2019-11-01T00:00:00.000000000&#x27;,\n       &#x27;2019-12-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8538e7b3-e9db-40d0-b39b-79e1b0605362' class='xr-section-summary-in' type='checkbox'  ><label for='section-8538e7b3-e9db-40d0-b39b-79e1b0605362' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-7fd026b9-eba4-4a47-a9de-fc76a52050bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fd026b9-eba4-4a47-a9de-fc76a52050bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef342d59-d00c-4d16-a0a3-d16a39acfba9' class='xr-var-data-in' type='checkbox'><label for='data-ef342d59-d00c-4d16-a0a3-d16a39acfba9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f14d2f6b-8591-4695-b98e-949464ac715d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f14d2f6b-8591-4695-b98e-949464ac715d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56243f9c-a378-4761-8b4e-dd8a05a67a27' class='xr-var-data-in' type='checkbox'><label for='data-56243f9c-a378-4761-8b4e-dd8a05a67a27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-92c1e5f3-133b-4348-b087-b10f8e9a22d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92c1e5f3-133b-4348-b087-b10f8e9a22d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5a7ebc6-d12b-4fd0-b1e1-2926f378fbd6' class='xr-var-data-in' type='checkbox'><label for='data-b5a7ebc6-d12b-4fd0-b1e1-2926f378fbd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-75069a33-5025-490a-a945-40a46b02d3ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75069a33-5025-490a-a945-40a46b02d3ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ebfa691-c1cf-4be4-a91c-aefae09f21bf' class='xr-var-data-in' type='checkbox'><label for='data-6ebfa691-c1cf-4be4-a91c-aefae09f21bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  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text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-e7b2890c-9ce1-4b7b-81c7-1de02a9de80d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7b2890c-9ce1-4b7b-81c7-1de02a9de80d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d594c22d-3e17-4428-b0d2-f6773df1dd18' class='xr-var-data-in' type='checkbox'><label for='data-d594c22d-3e17-4428-b0d2-f6773df1dd18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-075b7602-4ddd-426b-8bcd-0fcfb9dfc39e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-075b7602-4ddd-426b-8bcd-0fcfb9dfc39e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb2cbd5d-02bb-45b0-a94f-2e762684cf56' class='xr-var-data-in' type='checkbox'><label for='data-cb2cbd5d-02bb-45b0-a94f-2e762684cf56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-ae3ab328-43bb-43b5-b156-370fbbda4606' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae3ab328-43bb-43b5-b156-370fbbda4606' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44e39e56-ff6a-43e0-bde9-7d8b40a119ca' class='xr-var-data-in' type='checkbox'><label for='data-44e39e56-ff6a-43e0-bde9-7d8b40a119ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-bd73c5f2-6b5e-43e5-bf83-43f040f8ce00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd73c5f2-6b5e-43e5-bf83-43f040f8ce00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07fb58f9-ff94-4151-8ef3-40b350a2cace' class='xr-var-data-in' type='checkbox'><label for='data-07fb58f9-ff94-4151-8ef3-40b350a2cace' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-f41dd1b2-f15a-48df-b769-16924ade4170' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f41dd1b2-f15a-48df-b769-16924ade4170' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9da1d08-0bd1-4d75-9a14-54d3b49be522' class='xr-var-data-in' type='checkbox'><label for='data-b9da1d08-0bd1-4d75-9a14-54d3b49be522' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320.00 kiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-e534fe6b-7e1e-4bae-b32b-65b74214350b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e534fe6b-7e1e-4bae-b32b-65b74214350b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-274e1843-9486-48c6-b540-233d77a752d3' class='xr-var-data-in' type='checkbox'><label for='data-274e1843-9486-48c6-b540-233d77a752d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-49d726e4-e830-4250-be10-fa57388b187d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49d726e4-e830-4250-be10-fa57388b187d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6bd15da-eeb4-41ec-844f-416bdcf88f10' class='xr-var-data-in' type='checkbox'><label for='data-e6bd15da-eeb4-41ec-844f-416bdcf88f10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-99ada3c9-6e11-4dcf-9a24-4a4702764c68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99ada3c9-6e11-4dcf-9a24-4a4702764c68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e679593-5786-46a1-9021-1ef006d8e58a' class='xr-var-data-in' type='checkbox'><label for='data-3e679593-5786-46a1-9021-1ef006d8e58a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-67877f71-34b4-4a40-b825-0f8a0b8ed945' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67877f71-34b4-4a40-b825-0f8a0b8ed945' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30393594-fb76-40a8-9519-8f6b705e958c' class='xr-var-data-in' type='checkbox'><label for='data-30393594-fb76-40a8-9519-8f6b705e958c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1be844bd-c228-48db-b3c4-861163f38ec8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1be844bd-c228-48db-b3c4-861163f38ec8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15c1bd1b-82d7-4ebc-9398-1fc14b12023d' class='xr-var-data-in' type='checkbox'><label for='data-15c1bd1b-82d7-4ebc-9398-1fc14b12023d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-60568aea-d7cc-43c9-a819-ca7d11074963' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60568aea-d7cc-43c9-a819-ca7d11074963' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65105234-cc0b-42f7-9a51-416267dcd2d5' class='xr-var-data-in' type='checkbox'><label for='data-65105234-cc0b-42f7-9a51-416267dcd2d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-11a3e582-c003-47a6-bad4-11ad87fcefbd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11a3e582-c003-47a6-bad4-11ad87fcefbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-091b55dd-0cec-4b02-8dbb-098261a4c310' class='xr-var-data-in' type='checkbox'><label for='data-091b55dd-0cec-4b02-8dbb-098261a4c310' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3e19b9d1-27ce-41b4-9514-318c9a50cd3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e19b9d1-27ce-41b4-9514-318c9a50cd3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-171cc027-c4aa-47e3-b694-9d7219058847' class='xr-var-data-in' type='checkbox'><label for='data-171cc027-c4aa-47e3-b694-9d7219058847' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4f834b88-470b-4abe-939a-314f0020a4fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f834b88-470b-4abe-939a-314f0020a4fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4de75d2-a7fd-4183-b031-c9fa8fa4b90e' class='xr-var-data-in' type='checkbox'><label for='data-e4de75d2-a7fd-4183-b031-c9fa8fa4b90e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-713dd58f-cb25-4b89-94a8-636e5701c9d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-713dd58f-cb25-4b89-94a8-636e5701c9d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20b7e3ec-b99c-49ec-b6f7-6345e55cf00d' class='xr-var-data-in' type='checkbox'><label for='data-20b7e3ec-b99c-49ec-b6f7-6345e55cf00d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4dceb78f-51af-4df6-a68f-378b4ba7b300' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4dceb78f-51af-4df6-a68f-378b4ba7b300' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9c93f3a-8b84-444c-9b45-663fbfeaa133' class='xr-var-data-in' type='checkbox'><label for='data-c9c93f3a-8b84-444c-9b45-663fbfeaa133' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-171ea39d-c40a-4092-9597-2e2a4988227a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-171ea39d-c40a-4092-9597-2e2a4988227a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b907299c-d6f2-45a1-9c54-c0829e248e67' class='xr-var-data-in' type='checkbox'><label for='data-b907299c-d6f2-45a1-9c54-c0829e248e67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-0e51ceb7-b6b0-44c1-b1f5-806b7bec6555' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e51ceb7-b6b0-44c1-b1f5-806b7bec6555' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8e08c9e-fd02-45be-83c0-89e30d2542f9' class='xr-var-data-in' type='checkbox'><label for='data-d8e08c9e-fd02-45be-83c0-89e30d2542f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f87e36ff-e9ae-4069-8003-b9e5266eef9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f87e36ff-e9ae-4069-8003-b9e5266eef9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06d1f5ff-87a0-468f-80e4-4bb175984411' class='xr-var-data-in' type='checkbox'><label for='data-06d1f5ff-87a0-468f-80e4-4bb175984411' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-aeb3faf6-d113-4136-b3be-38f1ed2d1582' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aeb3faf6-d113-4136-b3be-38f1ed2d1582' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37fe2b12-0504-4525-ac9e-32e4efb3f272' class='xr-var-data-in' type='checkbox'><label for='data-37fe2b12-0504-4525-ac9e-32e4efb3f272' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-35b78fe0-a582-4850-8eca-847673a581f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35b78fe0-a582-4850-8eca-847673a581f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10cbe3b4-816d-451d-aff6-0e2d6d181778' class='xr-var-data-in' type='checkbox'><label for='data-10cbe3b4-816d-451d-aff6-0e2d6d181778' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d3f6f33e-5bff-4482-9d34-1d9c909dfada' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3f6f33e-5bff-4482-9d34-1d9c909dfada' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1997374e-2d72-4d53-a62e-4915d4ff4ee6' class='xr-var-data-in' type='checkbox'><label for='data-1997374e-2d72-4d53-a62e-4915d4ff4ee6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp_10m</span></div><div class='xr-var-dims'>(time, height, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-97189bce-9832-428f-b052-4db0bbed5a4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97189bce-9832-428f-b052-4db0bbed5a4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bc930bc-2e6f-4550-84ca-e0e86dcfaa6b' class='xr-var-data-in' type='checkbox'><label for='data-4bc930bc-2e6f-4550-84ca-e0e86dcfaa6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>wind speed in 10m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.0</dd><dt><span>standard_name :</span></dt><dd>sp_10m</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"26\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"26\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"27\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"28\" />\n  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"29\" />\n  <line x1=\"15\" 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Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-e6c15ce9-b683-4d2c-ad63-ad0101674d2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6c15ce9-b683-4d2c-ad63-ad0101674d2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7397972-f5ff-41ae-a656-de537902b02e' class='xr-var-data-in' type='checkbox'><label for='data-d7397972-f5ff-41ae-a656-de537902b02e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- 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transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-eb2b086f-8933-4568-ba17-68b9404d4336' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb2b086f-8933-4568-ba17-68b9404d4336' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bf657d3-f2a7-4975-b8bd-751b79eea1d5' class='xr-var-data-in' type='checkbox'><label for='data-5bf657d3-f2a7-4975-b8bd-751b79eea1d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8454062a-4322-4e57-9e5c-232de764cb73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8454062a-4322-4e57-9e5c-232de764cb73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59fd8488-00aa-48d7-854d-552b3b263e97' class='xr-var-data-in' type='checkbox'><label for='data-59fd8488-00aa-48d7-854d-552b3b263e97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-144c2592-a50e-4f15-aa62-9637ab9df24a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-144c2592-a50e-4f15-aa62-9637ab9df24a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-239f309f-74cf-47ed-b0a7-4816fc1951fa' class='xr-var-data-in' type='checkbox'><label for='data-239f309f-74cf-47ed-b0a7-4816fc1951fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8cb62ffc-3cf9-4c83-8877-01ff5db3da80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cb62ffc-3cf9-4c83-8877-01ff5db3da80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5308e92-afa1-4ff3-84f4-d92e97bc0bec' class='xr-var-data-in' type='checkbox'><label for='data-a5308e92-afa1-4ff3-84f4-d92e97bc0bec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-84759989-70d2-48db-adc9-abd0e9036524' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84759989-70d2-48db-adc9-abd0e9036524' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c3d7102-7987-4339-a632-99ba48b3a33c' class='xr-var-data-in' type='checkbox'><label for='data-6c3d7102-7987-4339-a632-99ba48b3a33c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-dca0c49e-23c4-4cd1-9a22-902c0088165d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dca0c49e-23c4-4cd1-9a22-902c0088165d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a818b089-1c3c-4bbb-a8de-df9962559bba' class='xr-var-data-in' type='checkbox'><label for='data-a818b089-1c3c-4bbb-a8de-df9962559bba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-96e374e0-f77a-4895-9a3d-fc727a6956ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96e374e0-f77a-4895-9a3d-fc727a6956ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e065fe19-42a0-41f9-83cb-55ad6eaf59cf' class='xr-var-data-in' type='checkbox'><label for='data-e065fe19-42a0-41f9-83cb-55ad6eaf59cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b075c909-d412-4f72-be86-af5ac9246210' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b075c909-d412-4f72-be86-af5ac9246210' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf38ceb6-7ccd-4b91-b461-45699bf5b1a2' class='xr-var-data-in' type='checkbox'><label for='data-cf38ceb6-7ccd-4b91-b461-45699bf5b1a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ef554989-8a02-473f-956d-5f21efed87cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef554989-8a02-473f-956d-5f21efed87cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c80842a-0b00-4de9-b1ac-68adf0cbe3e4' class='xr-var-data-in' type='checkbox'><label for='data-3c80842a-0b00-4de9-b1ac-68adf0cbe3e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-868bede7-360f-4db2-8b60-0610dfe93218' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-868bede7-360f-4db2-8b60-0610dfe93218' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00cb3f3d-0f1c-4a69-9e4f-82760f4a5422' class='xr-var-data-in' type='checkbox'><label for='data-00cb3f3d-0f1c-4a69-9e4f-82760f4a5422' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-08ed2599-88a3-42c0-88e8-9a92b947a029' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08ed2599-88a3-42c0-88e8-9a92b947a029' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d129506e-ed3c-4e4e-a725-0bdfb2c2e911' class='xr-var-data-in' type='checkbox'><label for='data-d129506e-ed3c-4e4e-a725-0bdfb2c2e911' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-735d9124-10f2-45fe-a50d-74fb12c66a0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-735d9124-10f2-45fe-a50d-74fb12c66a0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18922918-aa1d-4efc-bddd-36ee2e032c49' class='xr-var-data-in' type='checkbox'><label for='data-18922918-aa1d-4efc-bddd-36ee2e032c49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-cece28ba-6dda-4a12-ba86-4c286520790f' class='xr-section-summary-in' type='checkbox'  ><label for='section-cece28ba-6dda-4a12-ba86-4c286520790f' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3d27dc8a-8913-4d4c-8f46-47ff1c29abe4' class='xr-index-data-in' type='checkbox'/><label for='index-3d27dc8a-8913-4d4c-8f46-47ff1c29abe4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bbebbd37-b0d3-4cf9-a252-f136c96e71e6' class='xr-index-data-in' type='checkbox'/><label for='index-bbebbd37-b0d3-4cf9-a252-f136c96e71e6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-74d23bcb-a33e-425a-8dbc-179862f37aee' class='xr-index-data-in' type='checkbox'/><label for='index-74d23bcb-a33e-425a-8dbc-179862f37aee' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2015-02-01&#x27;, &#x27;2015-03-01&#x27;, &#x27;2015-04-01&#x27;, &#x27;2015-05-01&#x27;,\n               &#x27;2015-06-01&#x27;, &#x27;2015-07-01&#x27;, &#x27;2015-08-01&#x27;, &#x27;2015-09-01&#x27;,\n               &#x27;2015-10-01&#x27;, &#x27;2015-11-01&#x27;, &#x27;2015-12-01&#x27;, &#x27;2016-01-01&#x27;,\n               &#x27;2016-02-01&#x27;, &#x27;2016-03-01&#x27;, &#x27;2016-04-01&#x27;, &#x27;2016-05-01&#x27;,\n               &#x27;2016-06-01&#x27;, &#x27;2016-07-01&#x27;, &#x27;2016-08-01&#x27;, &#x27;2016-09-01&#x27;,\n               &#x27;2016-10-01&#x27;, &#x27;2016-11-01&#x27;, &#x27;2016-12-01&#x27;, &#x27;2017-01-01&#x27;,\n               &#x27;2017-02-01&#x27;, &#x27;2017-03-01&#x27;, &#x27;2017-04-01&#x27;, &#x27;2017-05-01&#x27;,\n               &#x27;2017-06-01&#x27;, &#x27;2017-07-01&#x27;, &#x27;2017-08-01&#x27;, &#x27;2017-09-01&#x27;,\n               &#x27;2017-10-01&#x27;, &#x27;2017-11-01&#x27;, &#x27;2017-12-01&#x27;, &#x27;2018-01-01&#x27;,\n               &#x27;2018-02-01&#x27;, &#x27;2018-03-01&#x27;, &#x27;2018-04-01&#x27;, &#x27;2018-05-01&#x27;,\n               &#x27;2018-06-01&#x27;, &#x27;2018-07-01&#x27;, &#x27;2018-08-01&#x27;, &#x27;2018-09-01&#x27;,\n               &#x27;2018-10-01&#x27;, &#x27;2018-11-01&#x27;, &#x27;2018-12-01&#x27;, &#x27;2019-01-01&#x27;,\n               &#x27;2019-02-01&#x27;, &#x27;2019-03-01&#x27;, &#x27;2019-04-01&#x27;, &#x27;2019-05-01&#x27;,\n               &#x27;2019-06-01&#x27;, &#x27;2019-07-01&#x27;, &#x27;2019-08-01&#x27;, &#x27;2019-09-01&#x27;,\n               &#x27;2019-10-01&#x27;, &#x27;2019-11-01&#x27;, &#x27;2019-12-01&#x27;, &#x27;2020-01-01&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3899a397-2fc1-488d-9dc8-db794dbedcc8' class='xr-section-summary-in' type='checkbox'  ><label for='section-3899a397-2fc1-488d-9dc8-db794dbedcc8' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 020510</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 7
+    },
+    {
+      "id": "8110a212-0814-4b9c-8a32-9d6379c6a6c0",
+      "cell_type": "markdown",
+      "source": "The data can be visualized as demonstrated below. For example, the total precipitation rate on **2020-01-01 at 00:00:00 UTC** is plotted.",
+      "metadata": {}
+    },
+    {
+      "id": "6750630a-8b1b-4a35-8cb7-8323a55dbf1b",
+      "cell_type": "code",
+      "source": "earthkit.plots.quickmap.plot(\n    ds_2015[\"tot_prec_rate\"].sel(time=\"2020-01-01 00:00:00\"), \n    x=\"clon\", y=\"clat\"\n)\n",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "execution_count": 8,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "<earthkit.plots.components.maps.Map at 0x16f278c0>"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 700x600 with 2 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 8
+    },
+    {
+      "id": "e3153d77-e5e3-49b0-94ba-9048a71889e8",
+      "cell_type": "markdown",
+      "source": "### Please note: due to an earthkit-plots bug related to np.datetime64 it is currently only possible to plot data ranging from 1678 to 2262.",
+      "metadata": {}
+    },
+    {
+      "id": "9f820694-e442-487d-981a-4fbd238a806e",
+      "cell_type": "code",
+      "source": "",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
     }
-   ],
-   "source": [
-    "earthkit.plots.quickmap.plot(\n",
-    "    ds_2015[\"tot_prec_rate\"].sel(time=np.datetime64(\"2020-01-01 00:00:00\"))[::10], \n",
-    "    x=\"clon\", y=\"clat\"\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3153d77-e5e3-49b0-94ba-9048a71889e8",
-   "metadata": {},
-   "source": [
-    "### Please note: due to an earthkit-plots bug related to np.datetime64 it is currently only possible to plot data ranging from 1678 to 2262."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f820694-e442-487d-981a-4fbd238a806e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  ]
+}
\ No newline at end of file

From 33ab732dbcb88c73769974282149a6e056d6f0be Mon Sep 17 00:00:00 2001
From: Sara Faghih-Naini <sara.faghihnaini@ecmwf.int>
Date: Mon, 9 Dec 2024 15:04:47 +0100
Subject: [PATCH 5/8] Rescale lon/lat in ICON-XPP notebook

---
 04-example-datasets/04-ICONXPP.ipynb | 32 +++++++++++++++++-----------
 1 file changed, 20 insertions(+), 12 deletions(-)

diff --git a/04-example-datasets/04-ICONXPP.ipynb b/04-example-datasets/04-ICONXPP.ipynb
index 7af1996..ae3ae9f 100644
--- a/04-example-datasets/04-ICONXPP.ipynb
+++ b/04-example-datasets/04-ICONXPP.ipynb
@@ -86,7 +86,7 @@
     {
       "id": "0b393415-87ca-4dc7-8c72-4cdd101d6621",
       "cell_type": "code",
-      "source": "import json\n\nimport requests\nimport xarray as xr\n\n\ndef load_ICONXPP_data(year=None, month=None):\n    \n    if not isinstance(year, int) or year < 1300 or year > 2300:\n        raise ValueError(\"Please specify a valid year as an integer between 1300 and 2300.\")\n\n    if year == 2300:\n        if not month:\n            print(f\"Warning: Only data for January 1st is available for 2300.\")\n        if month and month != 1:\n            raise ValueError(f\"Only data for January 1st is available for 2300.\")\n            \n    if month:\n        if not isinstance(month, int) or month < 1 or month > 12:\n            raise ValueError(f\"Please specify a valid month as an integer between 1 and 12.\")\n\n    file_year = (year // 10) * 10\n    if not year % 10 <= 4: \n        file_year += 5\n\n\n    if not month and year % 5 == 0:\n        if year == 1300:\n            print(f\"Warning: Data for January 1st of 1300 is not available as is is the simulation starting time.\")\n        else:\n            print(f\"Warning: Data for January 1st of '{year}' is stored in the previous file. Please specify the month to load the correct dataset.\")\n    elif month == 1 and year % 5 == 0:\n        if year == 1300:\n            raise ValueError(f\"Data for January 1st of 1300 is not available as this is the simulation starting time.\")\n        file_year -= 5\n        \n    if year == 2300:\n        file_year = 2295\n\n    url = f\"{BASE_URL}/ICON-XPP/slo1802_atm_2d_ml_{file_year}0101T000000Z.nc\"\n        \n    ref = requests.get(f\"{url}.ref\").json()\n\n    print(f\"Loading dataset {url}\")\n\n    return xr.open_dataset(\n        \"reference://\", \n        engine=\"zarr\", \n        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n        consolidated=False, \n        chunks=dict(),\n    )",
+      "source": "import json\n\nimport numpy as np\nimport requests\nimport xarray as xr\n\n\ndef load_ICONXPP_data(year=None, month=None):\n    \n    if not isinstance(year, int) or year < 1300 or year > 2300:\n        raise ValueError(\"Please specify a valid year as an integer between 1300 and 2300.\")\n\n    if year == 2300:\n        if not month:\n            print(f\"Warning: Only data for January 1st is available for 2300.\")\n        if month and month != 1:\n            raise ValueError(f\"Only data for January 1st is available for 2300.\")\n            \n    if month:\n        if not isinstance(month, int) or month < 1 or month > 12:\n            raise ValueError(f\"Please specify a valid month as an integer between 1 and 12.\")\n\n    file_year = (year // 10) * 10\n    if not year % 10 <= 4: \n        file_year += 5\n\n\n    if not month and year % 5 == 0:\n        if year == 1300:\n            print(f\"Warning: Data for January 1st of 1300 is not available as is is the simulation starting time.\")\n        else:\n            print(f\"Warning: Data for January 1st of '{year}' is stored in the previous file. Please specify the month to load the correct dataset.\")\n    elif month == 1 and year % 5 == 0:\n        if year == 1300:\n            raise ValueError(f\"Data for January 1st of 1300 is not available as this is the simulation starting time.\")\n        file_year -= 5\n        \n    if year == 2300:\n        file_year = 2295\n\n    url = f\"{BASE_URL}/ICON-XPP/slo1802_atm_2d_ml_{file_year}0101T000000Z.nc\"\n        \n    ref = requests.get(f\"{url}.ref\").json()\n\n    print(f\"Loading dataset {url}\")\n\n    ds = xr.open_dataset(\n        \"reference://\", \n        engine=\"zarr\", \n        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n        consolidated=False, \n        chunks=dict(),\n    )\n    ds.clon.values=np.degrees(ds.clon.values)\n    ds.clat.values=np.degrees(ds.clat.values)\n    \n    return ds\n    ",
       "metadata": {
         "trusted": true
       },
@@ -122,8 +122,8 @@
           "execution_count": 4,
           "output_type": "execute_result",
           "data": {
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n    clon           (ncells) float64 655kB dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2020-02-01 ... 2025-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albnirdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    clat_bnds      (ncells, vertices) float64 2MB dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n    clct           (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqi_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv            (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    umfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    vmfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c06e3e7c-80a6-4989-b4ea-75e008f836c9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c06e3e7c-80a6-4989-b4ea-75e008f836c9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-456e8874-7d08-4bce-bc37-967f02292cc6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-456e8874-7d08-4bce-bc37-967f02292cc6' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-9b252d2a-a9f7-4b1f-9e2a-ca4d2e7cb949' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b252d2a-a9f7-4b1f-9e2a-ca4d2e7cb949' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f84c5656-99e5-4887-8f61-cc7884831b0b' class='xr-var-data-in' type='checkbox'><label for='data-f84c5656-99e5-4887-8f61-cc7884831b0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640.00 kiB </td>\n                        <td> 640.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-a5c40df4-f15d-48a7-8a2e-bd9f856520f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5c40df4-f15d-48a7-8a2e-bd9f856520f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-450efcea-6d7f-4fbe-b72d-9cfe2ddd6bcc' class='xr-var-data-in' type='checkbox'><label for='data-450efcea-6d7f-4fbe-b72d-9cfe2ddd6bcc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640.00 kiB </td>\n                        <td> 640.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-b2b11ba5-1b60-4f63-a6e8-733db5879c18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2b11ba5-1b60-4f63-a6e8-733db5879c18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2189aa34-740a-48ba-b766-19ac838f3d5e' class='xr-var-data-in' type='checkbox'><label for='data-2189aa34-740a-48ba-b766-19ac838f3d5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-e4266a6f-e37a-4a64-a1ae-5058fde1bbc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4266a6f-e37a-4a64-a1ae-5058fde1bbc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a32e90f0-f81d-4588-8eb9-1558f66bc46f' class='xr-var-data-in' type='checkbox'><label for='data-a32e90f0-f81d-4588-8eb9-1558f66bc46f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-01 ... 2025-01-01</div><input id='attrs-7f0dd66d-72d0-4873-ad85-1182ffc50d59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f0dd66d-72d0-4873-ad85-1182ffc50d59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f6bce05-14c5-4297-9d4b-6432b8f24c8c' class='xr-var-data-in' type='checkbox'><label for='data-8f6bce05-14c5-4297-9d4b-6432b8f24c8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;, &#x27;2020-03-01T00:00:00.000000000&#x27;,\n       &#x27;2020-04-01T00:00:00.000000000&#x27;, &#x27;2020-05-01T00:00:00.000000000&#x27;,\n       &#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-07-01T00:00:00.000000000&#x27;,\n       &#x27;2020-08-01T00:00:00.000000000&#x27;, &#x27;2020-09-01T00:00:00.000000000&#x27;,\n       &#x27;2020-10-01T00:00:00.000000000&#x27;, &#x27;2020-11-01T00:00:00.000000000&#x27;,\n       &#x27;2020-12-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n       &#x27;2021-02-01T00:00:00.000000000&#x27;, &#x27;2021-03-01T00:00:00.000000000&#x27;,\n       &#x27;2021-04-01T00:00:00.000000000&#x27;, &#x27;2021-05-01T00:00:00.000000000&#x27;,\n       &#x27;2021-06-01T00:00:00.000000000&#x27;, &#x27;2021-07-01T00:00:00.000000000&#x27;,\n       &#x27;2021-08-01T00:00:00.000000000&#x27;, &#x27;2021-09-01T00:00:00.000000000&#x27;,\n       &#x27;2021-10-01T00:00:00.000000000&#x27;, &#x27;2021-11-01T00:00:00.000000000&#x27;,\n       &#x27;2021-12-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:00:00.000000000&#x27;,\n       &#x27;2022-02-01T00:00:00.000000000&#x27;, &#x27;2022-03-01T00:00:00.000000000&#x27;,\n       &#x27;2022-04-01T00:00:00.000000000&#x27;, &#x27;2022-05-01T00:00:00.000000000&#x27;,\n       &#x27;2022-06-01T00:00:00.000000000&#x27;, &#x27;2022-07-01T00:00:00.000000000&#x27;,\n       &#x27;2022-08-01T00:00:00.000000000&#x27;, &#x27;2022-09-01T00:00:00.000000000&#x27;,\n       &#x27;2022-10-01T00:00:00.000000000&#x27;, &#x27;2022-11-01T00:00:00.000000000&#x27;,\n       &#x27;2022-12-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n       &#x27;2023-02-01T00:00:00.000000000&#x27;, &#x27;2023-03-01T00:00:00.000000000&#x27;,\n       &#x27;2023-04-01T00:00:00.000000000&#x27;, &#x27;2023-05-01T00:00:00.000000000&#x27;,\n       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;,\n       &#x27;2023-08-01T00:00:00.000000000&#x27;, &#x27;2023-09-01T00:00:00.000000000&#x27;,\n       &#x27;2023-10-01T00:00:00.000000000&#x27;, &#x27;2023-11-01T00:00:00.000000000&#x27;,\n       &#x27;2023-12-01T00:00:00.000000000&#x27;, &#x27;2024-01-01T00:00:00.000000000&#x27;,\n       &#x27;2024-02-01T00:00:00.000000000&#x27;, &#x27;2024-03-01T00:00:00.000000000&#x27;,\n       &#x27;2024-04-01T00:00:00.000000000&#x27;, &#x27;2024-05-01T00:00:00.000000000&#x27;,\n       &#x27;2024-06-01T00:00:00.000000000&#x27;, &#x27;2024-07-01T00:00:00.000000000&#x27;,\n       &#x27;2024-08-01T00:00:00.000000000&#x27;, &#x27;2024-09-01T00:00:00.000000000&#x27;,\n       &#x27;2024-10-01T00:00:00.000000000&#x27;, &#x27;2024-11-01T00:00:00.000000000&#x27;,\n       &#x27;2024-12-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b608e2f0-ee60-4230-b438-ec04469ba971' class='xr-section-summary-in' type='checkbox'  ><label for='section-b608e2f0-ee60-4230-b438-ec04469ba971' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5a2acaf8-8790-476a-9342-6c478b4d77b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a2acaf8-8790-476a-9342-6c478b4d77b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f600963-4e7e-4c8b-9273-6eeb6b939731' class='xr-var-data-in' type='checkbox'><label for='data-8f600963-4e7e-4c8b-9273-6eeb6b939731' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a8cfca9e-eec4-4fe4-befd-318ac354a536' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8cfca9e-eec4-4fe4-befd-318ac354a536' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f878127-b84b-40fd-b378-c3f590246bf8' class='xr-var-data-in' type='checkbox'><label for='data-6f878127-b84b-40fd-b378-c3f590246bf8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-37d9cb64-d7d9-4de6-a995-7b6a3cbf5f8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37d9cb64-d7d9-4de6-a995-7b6a3cbf5f8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-741b0ccd-3736-4ec5-873c-7876efee87fe' class='xr-var-data-in' type='checkbox'><label for='data-741b0ccd-3736-4ec5-873c-7876efee87fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-9817fc7c-ea71-455f-892f-2cf94ace7ca9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9817fc7c-ea71-455f-892f-2cf94ace7ca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eeb2cdf2-e735-4d91-9563-4fdfdd876635' class='xr-var-data-in' type='checkbox'><label for='data-eeb2cdf2-e735-4d91-9563-4fdfdd876635' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  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text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-173fae23-f25d-4984-a4e2-c73cce78a8f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-173fae23-f25d-4984-a4e2-c73cce78a8f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-debb3889-b168-463e-9295-d0541c20f660' class='xr-var-data-in' type='checkbox'><label for='data-debb3889-b168-463e-9295-d0541c20f660' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d8aff872-80e5-46f8-9298-ad8f47bd12f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8aff872-80e5-46f8-9298-ad8f47bd12f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ae41f7e-7fbd-4507-ade1-ed3e2ee70299' class='xr-var-data-in' type='checkbox'><label for='data-4ae41f7e-7fbd-4507-ade1-ed3e2ee70299' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-f2a1b5ba-b5b6-4a7d-9f82-83d068a727b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2a1b5ba-b5b6-4a7d-9f82-83d068a727b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e0d11bc-c62a-4469-84ee-3137b5de1167' class='xr-var-data-in' type='checkbox'><label for='data-6e0d11bc-c62a-4469-84ee-3137b5de1167' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-002c2e95-40de-4838-99bf-e37810bf8789' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-002c2e95-40de-4838-99bf-e37810bf8789' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b7168fc-180b-4d68-884a-2635421de1b5' class='xr-var-data-in' type='checkbox'><label for='data-3b7168fc-180b-4d68-884a-2635421de1b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-3bd8bc90-1e65-4dbb-ab75-cb0cb263f052' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bd8bc90-1e65-4dbb-ab75-cb0cb263f052' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75766267-84b9-4b37-8287-93150393820a' class='xr-var-data-in' type='checkbox'><label for='data-75766267-84b9-4b37-8287-93150393820a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320.00 kiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4e05197a-6b9f-48d0-b1e7-7d1b4f5bed6d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e05197a-6b9f-48d0-b1e7-7d1b4f5bed6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f2b106a-a017-49bf-b301-7a98197d96bc' class='xr-var-data-in' type='checkbox'><label for='data-6f2b106a-a017-49bf-b301-7a98197d96bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d68025d7-0398-464f-9365-bfa326c7dc26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d68025d7-0398-464f-9365-bfa326c7dc26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-346e23bb-0604-4db9-aa08-176ccebed694' class='xr-var-data-in' type='checkbox'><label for='data-346e23bb-0604-4db9-aa08-176ccebed694' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3578247a-dc81-42ea-b3f5-e966d576400a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3578247a-dc81-42ea-b3f5-e966d576400a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f8cb8a6-854c-41f0-ab0e-3dad6d804811' class='xr-var-data-in' type='checkbox'><label for='data-6f8cb8a6-854c-41f0-ab0e-3dad6d804811' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3a40ba6f-bb13-4cca-b7e8-3684dcc91be2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a40ba6f-bb13-4cca-b7e8-3684dcc91be2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9acde312-e22c-4889-868e-71098335f778' class='xr-var-data-in' type='checkbox'><label for='data-9acde312-e22c-4889-868e-71098335f778' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b96408d7-f35f-4f50-94db-c894aef80853' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b96408d7-f35f-4f50-94db-c894aef80853' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97145d88-9b1b-4a93-b033-6e3c451a4b88' class='xr-var-data-in' type='checkbox'><label for='data-97145d88-9b1b-4a93-b033-6e3c451a4b88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-0d93906a-1e9e-4502-8673-cda09c421aac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d93906a-1e9e-4502-8673-cda09c421aac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9f5a692-f0e6-419c-b7b6-f75cf4b60dcf' class='xr-var-data-in' type='checkbox'><label for='data-f9f5a692-f0e6-419c-b7b6-f75cf4b60dcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2baa6c4c-7e39-46bc-9470-78d36a69e853' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2baa6c4c-7e39-46bc-9470-78d36a69e853' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d9cd978-ce49-4ca9-a43d-f2d3e14a454a' class='xr-var-data-in' type='checkbox'><label for='data-2d9cd978-ce49-4ca9-a43d-f2d3e14a454a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-daf834ce-2742-499a-81d9-427a663ddb78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-daf834ce-2742-499a-81d9-427a663ddb78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c0e8fca-0261-4e1c-b50b-da351e9718c3' class='xr-var-data-in' type='checkbox'><label for='data-6c0e8fca-0261-4e1c-b50b-da351e9718c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1dcaecf9-b7c8-4dd2-958d-ff15c674ce6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dcaecf9-b7c8-4dd2-958d-ff15c674ce6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-294777d1-89ab-4975-b7c8-4af623e1dde9' class='xr-var-data-in' type='checkbox'><label for='data-294777d1-89ab-4975-b7c8-4af623e1dde9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-28405db9-438a-49f3-a38d-5687814dc5c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28405db9-438a-49f3-a38d-5687814dc5c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e861e825-da98-4a82-a3a3-1d59411c1b08' class='xr-var-data-in' type='checkbox'><label for='data-e861e825-da98-4a82-a3a3-1d59411c1b08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-fc8cbfdb-b305-402f-86d4-998a1805253b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc8cbfdb-b305-402f-86d4-998a1805253b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1971643c-0265-4bfc-80c3-8b75770b6423' class='xr-var-data-in' type='checkbox'><label for='data-1971643c-0265-4bfc-80c3-8b75770b6423' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-fda5433d-be5a-426d-9513-f890e4c23708' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fda5433d-be5a-426d-9513-f890e4c23708' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0119bdb3-f86b-4c9d-85e7-2515e06c0460' class='xr-var-data-in' type='checkbox'><label for='data-0119bdb3-f86b-4c9d-85e7-2515e06c0460' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f61edf90-3676-4bba-9c37-6805d0dc4a74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f61edf90-3676-4bba-9c37-6805d0dc4a74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6fa9556-6c24-4c7f-9b32-b48adc256736' class='xr-var-data-in' type='checkbox'><label for='data-a6fa9556-6c24-4c7f-9b32-b48adc256736' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-489e591b-de4f-4e3d-af30-ec1a1572f689' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-489e591b-de4f-4e3d-af30-ec1a1572f689' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e91ebefb-5bb4-4ea4-a399-2ce1e2bedbfe' class='xr-var-data-in' type='checkbox'><label for='data-e91ebefb-5bb4-4ea4-a399-2ce1e2bedbfe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-04b7141e-339e-4de6-bdfd-3cf431d80e2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04b7141e-339e-4de6-bdfd-3cf431d80e2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe5a70ba-b620-4154-90c3-1b897e54ee3a' class='xr-var-data-in' type='checkbox'><label for='data-fe5a70ba-b620-4154-90c3-1b897e54ee3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-403e0407-6db4-419d-b0b2-1d8dafc65424' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-403e0407-6db4-419d-b0b2-1d8dafc65424' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59f08ce6-bb42-49c4-8034-2efbba9724d1' class='xr-var-data-in' type='checkbox'><label for='data-59f08ce6-bb42-49c4-8034-2efbba9724d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-81cebf7f-4367-4111-a113-5d6836553a65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81cebf7f-4367-4111-a113-5d6836553a65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-036edb19-bb73-4fdc-9aeb-bb98b9148a88' class='xr-var-data-in' type='checkbox'><label for='data-036edb19-bb73-4fdc-9aeb-bb98b9148a88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp_10m</span></div><div class='xr-var-dims'>(time, height, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2001b65f-1cdc-4ee2-8cc5-bd7538475b5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2001b65f-1cdc-4ee2-8cc5-bd7538475b5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3446941-9355-49b6-b054-a42ef994fbcb' class='xr-var-data-in' type='checkbox'><label for='data-d3446941-9355-49b6-b054-a42ef994fbcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>wind speed in 10m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.0</dd><dt><span>standard_name :</span></dt><dd>sp_10m</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"26\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"26\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"27\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"28\" />\n  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"29\" />\n  <line x1=\"15\" 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Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d7d89ed2-7c3a-4c26-a02b-ba7edf54b30c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7d89ed2-7c3a-4c26-a02b-ba7edf54b30c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-827763a2-876c-407e-9685-d02c3d027074' class='xr-var-data-in' type='checkbox'><label for='data-827763a2-876c-407e-9685-d02c3d027074' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- 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-->\n  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"24\" y1=\"40\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,40.36121446433689 24.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c58b545f-10da-4fa4-8238-d3170f2728e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c58b545f-10da-4fa4-8238-d3170f2728e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f756bd4-4a92-4221-990f-2fec9283cfb2' class='xr-var-data-in' type='checkbox'><label for='data-8f756bd4-4a92-4221-990f-2fec9283cfb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-43441f6e-0c3c-4c03-b003-c4b6021e0834' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43441f6e-0c3c-4c03-b003-c4b6021e0834' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33455098-4d5a-496b-9b27-a7c825575eea' class='xr-var-data-in' type='checkbox'><label for='data-33455098-4d5a-496b-9b27-a7c825575eea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-585c41a1-3cfd-4fe2-acbd-9b82ad58f6b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-585c41a1-3cfd-4fe2-acbd-9b82ad58f6b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80285bb2-523d-42de-bc31-0b30012ec750' class='xr-var-data-in' type='checkbox'><label for='data-80285bb2-523d-42de-bc31-0b30012ec750' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-9b6f68ed-5d74-440d-a869-1f947b001ce6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b6f68ed-5d74-440d-a869-1f947b001ce6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b77ab42d-3137-4298-b901-dac64536a020' class='xr-var-data-in' type='checkbox'><label for='data-b77ab42d-3137-4298-b901-dac64536a020' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4da36a73-9eda-4df6-9eed-1104607c66c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4da36a73-9eda-4df6-9eed-1104607c66c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7508e94-54b6-4876-b963-074d369dbd10' class='xr-var-data-in' type='checkbox'><label for='data-b7508e94-54b6-4876-b963-074d369dbd10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b0832eba-fd1a-4e82-a460-eb5e74d4ca06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0832eba-fd1a-4e82-a460-eb5e74d4ca06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24cabfd5-1f6c-4819-b6f1-a15b048a5ccd' class='xr-var-data-in' type='checkbox'><label for='data-24cabfd5-1f6c-4819-b6f1-a15b048a5ccd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-434de76e-1340-4e5c-9b5a-f5131c016003' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-434de76e-1340-4e5c-9b5a-f5131c016003' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50f3092c-a66e-4e78-a103-d4e8ad28c0df' class='xr-var-data-in' type='checkbox'><label for='data-50f3092c-a66e-4e78-a103-d4e8ad28c0df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-7ca9b9e8-276f-4ae4-a0de-2040c4582edf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ca9b9e8-276f-4ae4-a0de-2040c4582edf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6ab685f-e292-40df-8e4c-07f4748c98a4' class='xr-var-data-in' type='checkbox'><label for='data-c6ab685f-e292-40df-8e4c-07f4748c98a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a49ef13b-1cf9-4eff-8bea-904cb0b52c4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a49ef13b-1cf9-4eff-8bea-904cb0b52c4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-795839d7-9b23-4339-ad04-b8c9c84202d0' class='xr-var-data-in' type='checkbox'><label for='data-795839d7-9b23-4339-ad04-b8c9c84202d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-79e1da75-6750-422e-9986-12d12a75ddb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79e1da75-6750-422e-9986-12d12a75ddb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbe7804a-92ae-4c4f-b258-6963d37741ca' class='xr-var-data-in' type='checkbox'><label for='data-dbe7804a-92ae-4c4f-b258-6963d37741ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-33f744af-f084-40de-967d-9fad812a3ada' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33f744af-f084-40de-967d-9fad812a3ada' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dddf0bd-bce4-4f5b-b934-0e5c8bb3a143' class='xr-var-data-in' type='checkbox'><label for='data-6dddf0bd-bce4-4f5b-b934-0e5c8bb3a143' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f9f46bdf-8a35-479f-8827-0fe2651babb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9f46bdf-8a35-479f-8827-0fe2651babb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea0a33c7-d18e-43ce-81cc-61bd13b75008' class='xr-var-data-in' type='checkbox'><label for='data-ea0a33c7-d18e-43ce-81cc-61bd13b75008' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1798ef50-bc30-4d1f-ae8e-5ddf4688beae' class='xr-section-summary-in' type='checkbox'  ><label for='section-1798ef50-bc30-4d1f-ae8e-5ddf4688beae' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b680d75c-8636-4ea6-b5f0-ce74b8d5a00f' class='xr-index-data-in' type='checkbox'/><label for='index-b680d75c-8636-4ea6-b5f0-ce74b8d5a00f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aba72c83-fe2f-45d2-b5fa-2c9ea8e62a36' class='xr-index-data-in' type='checkbox'/><label for='index-aba72c83-fe2f-45d2-b5fa-2c9ea8e62a36' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ff5b5410-8315-4625-ac3c-248cd1061d03' class='xr-index-data-in' type='checkbox'/><label for='index-ff5b5410-8315-4625-ac3c-248cd1061d03' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-02-01&#x27;, &#x27;2020-03-01&#x27;, &#x27;2020-04-01&#x27;, &#x27;2020-05-01&#x27;,\n               &#x27;2020-06-01&#x27;, &#x27;2020-07-01&#x27;, &#x27;2020-08-01&#x27;, &#x27;2020-09-01&#x27;,\n               &#x27;2020-10-01&#x27;, &#x27;2020-11-01&#x27;, &#x27;2020-12-01&#x27;, &#x27;2021-01-01&#x27;,\n               &#x27;2021-02-01&#x27;, &#x27;2021-03-01&#x27;, &#x27;2021-04-01&#x27;, &#x27;2021-05-01&#x27;,\n               &#x27;2021-06-01&#x27;, &#x27;2021-07-01&#x27;, &#x27;2021-08-01&#x27;, &#x27;2021-09-01&#x27;,\n               &#x27;2021-10-01&#x27;, &#x27;2021-11-01&#x27;, &#x27;2021-12-01&#x27;, &#x27;2022-01-01&#x27;,\n               &#x27;2022-02-01&#x27;, &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;,\n               &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;,\n               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;, &#x27;2023-05-01&#x27;,\n               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;, &#x27;2023-08-01&#x27;, &#x27;2023-09-01&#x27;,\n               &#x27;2023-10-01&#x27;, &#x27;2023-11-01&#x27;, &#x27;2023-12-01&#x27;, &#x27;2024-01-01&#x27;,\n               &#x27;2024-02-01&#x27;, &#x27;2024-03-01&#x27;, &#x27;2024-04-01&#x27;, &#x27;2024-05-01&#x27;,\n               &#x27;2024-06-01&#x27;, &#x27;2024-07-01&#x27;, &#x27;2024-08-01&#x27;, &#x27;2024-09-01&#x27;,\n               &#x27;2024-10-01&#x27;, &#x27;2024-11-01&#x27;, &#x27;2024-12-01&#x27;, &#x27;2025-01-01&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3774f532-06f0-41d9-b094-816ec578d172' class='xr-section-summary-in' type='checkbox'  ><label for='section-3774f532-06f0-41d9-b094-816ec578d172' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 044638</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
+            "text/plain": "<xarray.Dataset> Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB 89.46 88.64 89.06 ... -27.32 -27.0\n    clon           (ncells) float64 655kB 72.0 50.25 72.0 ... 1.296 0.6204 0.357\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2020-02-01 ... 2025-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albnirdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    clat_bnds      (ncells, vertices) float64 2MB dask.array<chunksize=(81920, 3), meta=np.ndarray>\n    clct           (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqi_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv            (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    umfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    vmfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB 89.46 88.64 89.06 ... -27.32 -27.0\n    clon           (ncells) float64 655kB 72.0 50.25 72.0 ... 1.296 0.6204 0.357\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2020-02-01 ... 2025-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albnirdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    clat_bnds      (ncells, vertices) float64 2MB dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n    clct           (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqi_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv            (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    umfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    vmfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5b05dfb3-1690-4527-8e33-67d094ca8eb2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5b05dfb3-1690-4527-8e33-67d094ca8eb2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1d3be0d0-2c83-4ff1-a4c2-2ac282409846' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d3be0d0-2c83-4ff1-a4c2-2ac282409846' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.46 88.64 89.06 ... -27.32 -27.0</div><input id='attrs-dfd16620-2964-476a-9fcb-60d5fe07c4ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfd16620-2964-476a-9fcb-60d5fe07c4ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-895575a9-3e03-464c-8aa7-eac91e0c5e9a' class='xr-var-data-in' type='checkbox'><label for='data-895575a9-3e03-464c-8aa7-eac91e0c5e9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><pre>array([ 89.45889176,  88.63772156,  89.06223506, ..., -27.28616813,\n       -27.32237258, -27.0023716 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>72.0 50.25 72.0 ... 0.6204 0.357</div><input id='attrs-0548608a-cbe2-4add-9a9f-e1bb60db2337' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0548608a-cbe2-4add-9a9f-e1bb60db2337' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d2ce33f-e389-44e6-a162-af4bdc2fd892' class='xr-var-data-in' type='checkbox'><label for='data-9d2ce33f-e389-44e6-a162-af4bdc2fd892' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><pre>array([72.        , 50.25313351, 72.        , ...,  1.29630984,\n        0.62040403,  0.3569665 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-aea92e3e-ef9d-4176-82ba-5d68ebe16439' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aea92e3e-ef9d-4176-82ba-5d68ebe16439' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-372aed22-0d68-4a9a-a89c-778b27d421a3' class='xr-var-data-in' type='checkbox'><label for='data-372aed22-0d68-4a9a-a89c-778b27d421a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-b441b54b-7292-4409-be88-4f5af6c4f61a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b441b54b-7292-4409-be88-4f5af6c4f61a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24f2c299-1d8e-42ab-b39a-f2b21b0920d3' class='xr-var-data-in' type='checkbox'><label for='data-24f2c299-1d8e-42ab-b39a-f2b21b0920d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-01 ... 2025-01-01</div><input id='attrs-32bf4cbf-3a6f-414f-ab82-8628d0389e9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32bf4cbf-3a6f-414f-ab82-8628d0389e9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10f8de4b-89bc-4e31-801a-16eaa5b32002' class='xr-var-data-in' type='checkbox'><label for='data-10f8de4b-89bc-4e31-801a-16eaa5b32002' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;, &#x27;2020-03-01T00:00:00.000000000&#x27;,\n       &#x27;2020-04-01T00:00:00.000000000&#x27;, &#x27;2020-05-01T00:00:00.000000000&#x27;,\n       &#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-07-01T00:00:00.000000000&#x27;,\n       &#x27;2020-08-01T00:00:00.000000000&#x27;, &#x27;2020-09-01T00:00:00.000000000&#x27;,\n       &#x27;2020-10-01T00:00:00.000000000&#x27;, &#x27;2020-11-01T00:00:00.000000000&#x27;,\n       &#x27;2020-12-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n       &#x27;2021-02-01T00:00:00.000000000&#x27;, &#x27;2021-03-01T00:00:00.000000000&#x27;,\n       &#x27;2021-04-01T00:00:00.000000000&#x27;, &#x27;2021-05-01T00:00:00.000000000&#x27;,\n       &#x27;2021-06-01T00:00:00.000000000&#x27;, &#x27;2021-07-01T00:00:00.000000000&#x27;,\n       &#x27;2021-08-01T00:00:00.000000000&#x27;, &#x27;2021-09-01T00:00:00.000000000&#x27;,\n       &#x27;2021-10-01T00:00:00.000000000&#x27;, &#x27;2021-11-01T00:00:00.000000000&#x27;,\n       &#x27;2021-12-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:00:00.000000000&#x27;,\n       &#x27;2022-02-01T00:00:00.000000000&#x27;, &#x27;2022-03-01T00:00:00.000000000&#x27;,\n       &#x27;2022-04-01T00:00:00.000000000&#x27;, &#x27;2022-05-01T00:00:00.000000000&#x27;,\n       &#x27;2022-06-01T00:00:00.000000000&#x27;, &#x27;2022-07-01T00:00:00.000000000&#x27;,\n       &#x27;2022-08-01T00:00:00.000000000&#x27;, &#x27;2022-09-01T00:00:00.000000000&#x27;,\n       &#x27;2022-10-01T00:00:00.000000000&#x27;, &#x27;2022-11-01T00:00:00.000000000&#x27;,\n       &#x27;2022-12-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n       &#x27;2023-02-01T00:00:00.000000000&#x27;, &#x27;2023-03-01T00:00:00.000000000&#x27;,\n       &#x27;2023-04-01T00:00:00.000000000&#x27;, &#x27;2023-05-01T00:00:00.000000000&#x27;,\n       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;,\n       &#x27;2023-08-01T00:00:00.000000000&#x27;, &#x27;2023-09-01T00:00:00.000000000&#x27;,\n       &#x27;2023-10-01T00:00:00.000000000&#x27;, &#x27;2023-11-01T00:00:00.000000000&#x27;,\n       &#x27;2023-12-01T00:00:00.000000000&#x27;, &#x27;2024-01-01T00:00:00.000000000&#x27;,\n       &#x27;2024-02-01T00:00:00.000000000&#x27;, &#x27;2024-03-01T00:00:00.000000000&#x27;,\n       &#x27;2024-04-01T00:00:00.000000000&#x27;, &#x27;2024-05-01T00:00:00.000000000&#x27;,\n       &#x27;2024-06-01T00:00:00.000000000&#x27;, &#x27;2024-07-01T00:00:00.000000000&#x27;,\n       &#x27;2024-08-01T00:00:00.000000000&#x27;, &#x27;2024-09-01T00:00:00.000000000&#x27;,\n       &#x27;2024-10-01T00:00:00.000000000&#x27;, &#x27;2024-11-01T00:00:00.000000000&#x27;,\n       &#x27;2024-12-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e60a0b25-0c93-49b9-a4ac-08956a71eafb' class='xr-section-summary-in' type='checkbox'  ><label for='section-e60a0b25-0c93-49b9-a4ac-08956a71eafb' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-49e012d5-64b3-4b17-80aa-be5960b10426' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49e012d5-64b3-4b17-80aa-be5960b10426' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70b5393d-4b85-4e6e-b267-c4571df61697' class='xr-var-data-in' type='checkbox'><label for='data-70b5393d-4b85-4e6e-b267-c4571df61697' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1ea2d5ea-2eb1-4fbd-831c-310af230bb71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ea2d5ea-2eb1-4fbd-831c-310af230bb71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83d39e33-2c24-4a79-b4e3-1567ae916e5c' class='xr-var-data-in' type='checkbox'><label for='data-83d39e33-2c24-4a79-b4e3-1567ae916e5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-abc69030-7b3f-4f80-ade0-1a3bc9e2c20f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abc69030-7b3f-4f80-ade0-1a3bc9e2c20f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c800255-ca04-4796-8494-8185a597aa4a' class='xr-var-data-in' type='checkbox'><label for='data-7c800255-ca04-4796-8494-8185a597aa4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-cdabf300-cb48-4fe4-a0ad-028fe4e6f25f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdabf300-cb48-4fe4-a0ad-028fe4e6f25f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71ead9d8-155d-478e-932f-cb45f80bacc9' class='xr-var-data-in' type='checkbox'><label for='data-71ead9d8-155d-478e-932f-cb45f80bacc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  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class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-ad02fa19-59fd-48ee-b3a5-233979f365dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad02fa19-59fd-48ee-b3a5-233979f365dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-645f9b51-e9b9-453d-80a3-3f81d88835c9' class='xr-var-data-in' type='checkbox'><label for='data-645f9b51-e9b9-453d-80a3-3f81d88835c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a5f80d0f-6b14-457b-b1db-45f829324638' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5f80d0f-6b14-457b-b1db-45f829324638' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ad02202-feab-4d9d-9175-bdefaedc0c14' class='xr-var-data-in' type='checkbox'><label for='data-5ad02202-feab-4d9d-9175-bdefaedc0c14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-26d47575-2bec-4d2d-89e4-d196934dc254' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26d47575-2bec-4d2d-89e4-d196934dc254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb29a67f-cba9-4526-bafa-77d6d0aa974c' class='xr-var-data-in' type='checkbox'><label for='data-eb29a67f-cba9-4526-bafa-77d6d0aa974c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ebc7faf2-73b9-43cb-9c21-d99f5ed15a65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebc7faf2-73b9-43cb-9c21-d99f5ed15a65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-702b06f3-986a-464e-9bcf-c4bb605163d6' class='xr-var-data-in' type='checkbox'><label for='data-702b06f3-986a-464e-9bcf-c4bb605163d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-aad67f80-9688-41fe-989d-161a8d3761e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aad67f80-9688-41fe-989d-161a8d3761e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0ff0673-2305-48e5-b705-bde16903a30d' class='xr-var-data-in' type='checkbox'><label for='data-f0ff0673-2305-48e5-b705-bde16903a30d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320.00 kiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-545117ba-7d5c-46b6-b223-ac559185139b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-545117ba-7d5c-46b6-b223-ac559185139b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6efbad31-38f3-4650-ae84-faa98f332067' class='xr-var-data-in' type='checkbox'><label for='data-6efbad31-38f3-4650-ae84-faa98f332067' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-999035e0-ee7c-4b38-83a3-1cc0d47b9c3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-999035e0-ee7c-4b38-83a3-1cc0d47b9c3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93b1f0d0-31c5-474f-9f23-bac672a61171' class='xr-var-data-in' type='checkbox'><label for='data-93b1f0d0-31c5-474f-9f23-bac672a61171' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-7a111dcf-0b8c-46d4-bedd-077085722474' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a111dcf-0b8c-46d4-bedd-077085722474' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57ffdbfd-19b3-4200-a247-1cb80b4fd3f5' class='xr-var-data-in' type='checkbox'><label for='data-57ffdbfd-19b3-4200-a247-1cb80b4fd3f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-db220078-c98b-4f87-912c-cef180abf63d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db220078-c98b-4f87-912c-cef180abf63d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-324857a5-8264-4bc5-b9b6-34a43a121240' class='xr-var-data-in' type='checkbox'><label for='data-324857a5-8264-4bc5-b9b6-34a43a121240' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1952e0ac-e226-45f5-9399-c1431cc278a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1952e0ac-e226-45f5-9399-c1431cc278a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d7747d9-24a6-435b-9838-7e0da3a3ceb1' class='xr-var-data-in' type='checkbox'><label for='data-9d7747d9-24a6-435b-9838-7e0da3a3ceb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8f8ac19d-b82f-4cb8-9558-bfd9594d9ceb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f8ac19d-b82f-4cb8-9558-bfd9594d9ceb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6225e564-a3e3-4037-bfa8-e96aca0d5374' class='xr-var-data-in' type='checkbox'><label for='data-6225e564-a3e3-4037-bfa8-e96aca0d5374' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ec311409-a637-4206-b9b7-0acf6f336f86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec311409-a637-4206-b9b7-0acf6f336f86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d794d907-9af8-4728-b543-eed59f2d0fd2' class='xr-var-data-in' type='checkbox'><label for='data-d794d907-9af8-4728-b543-eed59f2d0fd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-16f8868f-ff91-4bbb-80cd-b7c1b9268bad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16f8868f-ff91-4bbb-80cd-b7c1b9268bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70e924fb-c3ad-4903-b0b3-ffa48658ccb8' class='xr-var-data-in' type='checkbox'><label for='data-70e924fb-c3ad-4903-b0b3-ffa48658ccb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f62ecdb7-da56-4d20-ae20-fdc257f8cf43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f62ecdb7-da56-4d20-ae20-fdc257f8cf43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cebdbbc8-7084-4bad-a1e8-6d7d05d70bcc' class='xr-var-data-in' type='checkbox'><label for='data-cebdbbc8-7084-4bad-a1e8-6d7d05d70bcc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-35b3e04c-d2ac-473c-8ee9-685e1d768a76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35b3e04c-d2ac-473c-8ee9-685e1d768a76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd26d3ad-0ea8-4cb7-b177-3c57ddf9bc93' class='xr-var-data-in' type='checkbox'><label for='data-bd26d3ad-0ea8-4cb7-b177-3c57ddf9bc93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-20912c7d-b1bf-46ac-8069-0cd8b758a6c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20912c7d-b1bf-46ac-8069-0cd8b758a6c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e74b62b-efcf-4217-bfe2-282c9f3702f1' class='xr-var-data-in' type='checkbox'><label for='data-0e74b62b-efcf-4217-bfe2-282c9f3702f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a7fa2688-48c6-467f-b48a-fb13c635a7e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7fa2688-48c6-467f-b48a-fb13c635a7e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a9eae5a-fe5c-49e2-a138-2d136b5ee258' class='xr-var-data-in' type='checkbox'><label for='data-1a9eae5a-fe5c-49e2-a138-2d136b5ee258' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-467af929-a783-41d3-a30b-c7f8ec1fe6f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-467af929-a783-41d3-a30b-c7f8ec1fe6f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa15a1c9-ea01-4bed-9d63-986106ba544e' class='xr-var-data-in' type='checkbox'><label for='data-aa15a1c9-ea01-4bed-9d63-986106ba544e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ebd8578f-350b-42d6-b4b2-dcd32f1f36ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebd8578f-350b-42d6-b4b2-dcd32f1f36ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35e70693-4462-4ab6-a015-98e05b2adde5' class='xr-var-data-in' type='checkbox'><label for='data-35e70693-4462-4ab6-a015-98e05b2adde5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-7bd05a47-c5fd-4ea1-ad50-dc7f9c9a2546' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bd05a47-c5fd-4ea1-ad50-dc7f9c9a2546' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be2516b2-7f41-48c4-9c96-83b6c1c289e6' class='xr-var-data-in' type='checkbox'><label for='data-be2516b2-7f41-48c4-9c96-83b6c1c289e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-db5eeb27-3252-407a-83dd-b67b55ed2050' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db5eeb27-3252-407a-83dd-b67b55ed2050' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-823df9ee-abde-433f-8c18-019275228dee' class='xr-var-data-in' type='checkbox'><label for='data-823df9ee-abde-433f-8c18-019275228dee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-605ef7cf-191f-4a88-961a-9a5a3150dd50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-605ef7cf-191f-4a88-961a-9a5a3150dd50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-822d5616-57e9-4e3d-a968-c4481aec52d3' class='xr-var-data-in' type='checkbox'><label for='data-822d5616-57e9-4e3d-a968-c4481aec52d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp_10m</span></div><div class='xr-var-dims'>(time, height, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-6595553f-5cde-42b4-b655-66d9d5b99154' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6595553f-5cde-42b4-b655-66d9d5b99154' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d73fffd-280a-466d-beea-adfb24f1f7d0' class='xr-var-data-in' type='checkbox'><label for='data-5d73fffd-280a-466d-beea-adfb24f1f7d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>wind speed in 10m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.0</dd><dt><span>standard_name :</span></dt><dd>sp_10m</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"26\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"26\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"27\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"28\" />\n  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"29\" />\n  <line x1=\"15\" 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Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-30dcf2ad-44c9-4666-98a8-a6bf2309f997' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30dcf2ad-44c9-4666-98a8-a6bf2309f997' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87f68f5c-c917-4f62-b24c-98ab009228f0' class='xr-var-data-in' type='checkbox'><label for='data-87f68f5c-c917-4f62-b24c-98ab009228f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- 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-->\n  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"24\" y1=\"40\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,40.36121446433689 24.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3261370a-7345-4ac1-aa51-9cdb54d5987b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3261370a-7345-4ac1-aa51-9cdb54d5987b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1750de10-2ddf-406b-b94c-ac0e785dd61e' class='xr-var-data-in' type='checkbox'><label for='data-1750de10-2ddf-406b-b94c-ac0e785dd61e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-9b84e28f-450b-407a-ae52-65a2a9689e78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b84e28f-450b-407a-ae52-65a2a9689e78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-829acbd2-0244-4319-b7c7-6b2d0ec454a9' class='xr-var-data-in' type='checkbox'><label for='data-829acbd2-0244-4319-b7c7-6b2d0ec454a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-768a206f-780e-4f49-8ac8-d73e6a348c49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-768a206f-780e-4f49-8ac8-d73e6a348c49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-512b6e89-0efe-461c-912e-34da60682bda' class='xr-var-data-in' type='checkbox'><label for='data-512b6e89-0efe-461c-912e-34da60682bda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c57de098-e0d9-4f24-918f-f29930cfcd10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c57de098-e0d9-4f24-918f-f29930cfcd10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e4903b1-5e77-422d-ac35-2f9a4ee1d29a' class='xr-var-data-in' type='checkbox'><label for='data-8e4903b1-5e77-422d-ac35-2f9a4ee1d29a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-9bed908b-c91a-4784-86d8-5d99efc524ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bed908b-c91a-4784-86d8-5d99efc524ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1d0fb81-af4f-4313-b0e6-d85dd6c34e21' class='xr-var-data-in' type='checkbox'><label for='data-c1d0fb81-af4f-4313-b0e6-d85dd6c34e21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-45b59d1e-dce1-4d2a-9ab1-fac4575c90c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45b59d1e-dce1-4d2a-9ab1-fac4575c90c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e80afda-bcf2-4c64-b4ce-248302697a7a' class='xr-var-data-in' type='checkbox'><label for='data-5e80afda-bcf2-4c64-b4ce-248302697a7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1d3af677-2a59-4971-b8b5-e8103723d1dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d3af677-2a59-4971-b8b5-e8103723d1dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7553f34-2869-4713-9640-b84e266065c5' class='xr-var-data-in' type='checkbox'><label for='data-b7553f34-2869-4713-9640-b84e266065c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d0f9bd13-d254-4f7c-9a69-91fe6e0b4b4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0f9bd13-d254-4f7c-9a69-91fe6e0b4b4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebfae586-7720-45ad-893e-a74af422df1f' class='xr-var-data-in' type='checkbox'><label for='data-ebfae586-7720-45ad-893e-a74af422df1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-75650f34-c2c0-4552-b1a8-759bea7f72ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75650f34-c2c0-4552-b1a8-759bea7f72ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9276272-3fb8-42d4-9464-8af212fa7a12' class='xr-var-data-in' type='checkbox'><label for='data-a9276272-3fb8-42d4-9464-8af212fa7a12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-711b3661-5e8b-4c61-b2dd-23235b790008' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-711b3661-5e8b-4c61-b2dd-23235b790008' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25f361f1-d996-4916-8792-173f61592e09' class='xr-var-data-in' type='checkbox'><label for='data-25f361f1-d996-4916-8792-173f61592e09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1b62f034-c43e-4ff8-8089-9c7abf27935b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b62f034-c43e-4ff8-8089-9c7abf27935b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67129103-b98f-4f1c-a4f1-151014dd327b' class='xr-var-data-in' type='checkbox'><label for='data-67129103-b98f-4f1c-a4f1-151014dd327b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f8c3cb0d-b6d0-4942-abce-c35de869c094' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8c3cb0d-b6d0-4942-abce-c35de869c094' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5602a71-b17f-4dff-bae1-9da7cb989ad6' class='xr-var-data-in' type='checkbox'><label for='data-e5602a71-b17f-4dff-bae1-9da7cb989ad6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-749041ed-3f8a-4056-8875-1a5ee45132a0' class='xr-section-summary-in' type='checkbox'  ><label for='section-749041ed-3f8a-4056-8875-1a5ee45132a0' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3db74a5e-d7db-46ab-b82b-d96de1a35d10' class='xr-index-data-in' type='checkbox'/><label for='index-3db74a5e-d7db-46ab-b82b-d96de1a35d10' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c754965d-9969-4e04-8036-002e3b15d833' class='xr-index-data-in' type='checkbox'/><label for='index-c754965d-9969-4e04-8036-002e3b15d833' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d6cc486d-92c4-493b-b048-aeeebef74fce' class='xr-index-data-in' type='checkbox'/><label for='index-d6cc486d-92c4-493b-b048-aeeebef74fce' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-02-01&#x27;, &#x27;2020-03-01&#x27;, &#x27;2020-04-01&#x27;, &#x27;2020-05-01&#x27;,\n               &#x27;2020-06-01&#x27;, &#x27;2020-07-01&#x27;, &#x27;2020-08-01&#x27;, &#x27;2020-09-01&#x27;,\n               &#x27;2020-10-01&#x27;, &#x27;2020-11-01&#x27;, &#x27;2020-12-01&#x27;, &#x27;2021-01-01&#x27;,\n               &#x27;2021-02-01&#x27;, &#x27;2021-03-01&#x27;, &#x27;2021-04-01&#x27;, &#x27;2021-05-01&#x27;,\n               &#x27;2021-06-01&#x27;, &#x27;2021-07-01&#x27;, &#x27;2021-08-01&#x27;, &#x27;2021-09-01&#x27;,\n               &#x27;2021-10-01&#x27;, &#x27;2021-11-01&#x27;, &#x27;2021-12-01&#x27;, &#x27;2022-01-01&#x27;,\n               &#x27;2022-02-01&#x27;, &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;,\n               &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;,\n               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;, &#x27;2023-05-01&#x27;,\n               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;, &#x27;2023-08-01&#x27;, &#x27;2023-09-01&#x27;,\n               &#x27;2023-10-01&#x27;, &#x27;2023-11-01&#x27;, &#x27;2023-12-01&#x27;, &#x27;2024-01-01&#x27;,\n               &#x27;2024-02-01&#x27;, &#x27;2024-03-01&#x27;, &#x27;2024-04-01&#x27;, &#x27;2024-05-01&#x27;,\n               &#x27;2024-06-01&#x27;, &#x27;2024-07-01&#x27;, &#x27;2024-08-01&#x27;, &#x27;2024-09-01&#x27;,\n               &#x27;2024-10-01&#x27;, &#x27;2024-11-01&#x27;, &#x27;2024-12-01&#x27;, &#x27;2025-01-01&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8a6db70e-a7fb-4c2d-b4bb-db9f8fc9f1bb' class='xr-section-summary-in' type='checkbox'  ><label for='section-8a6db70e-a7fb-4c2d-b4bb-db9f8fc9f1bb' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 044638</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
           },
           "metadata": {}
         }
@@ -171,7 +171,7 @@
     {
       "id": "d792634e-3a22-4e7d-92f8-f81f66eabbde",
       "cell_type": "code",
-      "source": "import numpy as np\n\nchart = earthkit.plots.quickmap.plot(\n    ds_2020[\"t_2m\"].sel(time=\"2024-12-01 00:00:00\"), \n    x=\"clon\", y=\"clat\"\n)\nchart.coastlines()\nchart.show()\n",
+      "source": "earthkit.plots.quickmap.plot(\n    ds_2020[\"t_2m\"].sel(time=\"2024-12-01 00:00:00\"), \n    x=\"clon\", y=\"clat\"\n)\n",
       "metadata": {
         "trusted": true
       },
@@ -182,17 +182,25 @@
           "output_type": "stream"
         },
         {
-          "output_type": "display_data",
+          "execution_count": 6,
+          "output_type": "execute_result",
           "data": {
-            "text/plain": "<Figure size 700x600 with 2 Axes>",
-            "image/png": 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"
+            "text/plain": "<earthkit.plots.components.maps.Map at 0x10848dd8>"
           },
           "metadata": {}
         },
         {
           "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 534.6MiB (from 309.4MiB) for this notebook\n",
+          "text": "[pyodide]: Memory usage has grown to 637.2MiB (from 309.4MiB) for this notebook\n",
           "output_type": "stream"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 700x600 with 2 Axes>",
+            "image/png": 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+          },
+          "metadata": {}
         }
       ],
       "execution_count": 6
@@ -220,8 +228,8 @@
           "execution_count": 7,
           "output_type": "execute_result",
           "data": {
-            "text/plain": "<xarray.Dataset> Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB dask.array<chunksize=(81920,), meta=np.ndarray>\n    clon           (ncells) float64 655kB dask.array<chunksize=(81920,), meta=np.ndarray>\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2015-02-01 ... 2020-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albnirdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    clat_bnds      (ncells, vertices) float64 2MB dask.array<chunksize=(81920, 3), meta=np.ndarray>\n    clct           (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqi_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv            (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    umfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    vmfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n    clon           (ncells) float64 655kB dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2015-02-01 ... 2020-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albnirdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    clat_bnds      (ncells, vertices) float64 2MB dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n    clct           (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqi_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv            (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    umfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    vmfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-679c9595-4861-42ce-9015-75cb6a819728' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-679c9595-4861-42ce-9015-75cb6a819728' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7e4ed73c-630a-4201-aaab-bc5a25700fc5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e4ed73c-630a-4201-aaab-bc5a25700fc5' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-ea4d2bd2-9f2b-433e-9337-5ac8651863f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea4d2bd2-9f2b-433e-9337-5ac8651863f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2de778e3-083d-47f8-b17b-6f22d2c4961d' class='xr-var-data-in' type='checkbox'><label for='data-2de778e3-083d-47f8-b17b-6f22d2c4961d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640.00 kiB </td>\n                        <td> 640.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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:</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640.00 kiB </td>\n                        <td> 640.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-dd7f4a8c-03e7-42a9-a3f5-32d3a3755a20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd7f4a8c-03e7-42a9-a3f5-32d3a3755a20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2e14e23-b0a0-44c9-be08-bd9c3ccfee2d' class='xr-var-data-in' type='checkbox'><label for='data-c2e14e23-b0a0-44c9-be08-bd9c3ccfee2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-99992352-bf20-43ca-a0fd-dc7aacbadbfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99992352-bf20-43ca-a0fd-dc7aacbadbfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4533736-8ee3-4472-a44d-ffb4410d6d80' class='xr-var-data-in' type='checkbox'><label for='data-f4533736-8ee3-4472-a44d-ffb4410d6d80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-02-01 ... 2020-01-01</div><input id='attrs-a3070bd4-bbe3-40bd-9f57-8b9a2bf2b3ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3070bd4-bbe3-40bd-9f57-8b9a2bf2b3ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe84d01e-8b33-4621-91c9-25187d0107ef' class='xr-var-data-in' type='checkbox'><label for='data-fe84d01e-8b33-4621-91c9-25187d0107ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n       &#x27;2016-12-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n       &#x27;2017-02-01T00:00:00.000000000&#x27;, &#x27;2017-03-01T00:00:00.000000000&#x27;,\n       &#x27;2017-04-01T00:00:00.000000000&#x27;, &#x27;2017-05-01T00:00:00.000000000&#x27;,\n       &#x27;2017-06-01T00:00:00.000000000&#x27;, &#x27;2017-07-01T00:00:00.000000000&#x27;,\n       &#x27;2017-08-01T00:00:00.000000000&#x27;, &#x27;2017-09-01T00:00:00.000000000&#x27;,\n       &#x27;2017-10-01T00:00:00.000000000&#x27;, &#x27;2017-11-01T00:00:00.000000000&#x27;,\n       &#x27;2017-12-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n       &#x27;2018-02-01T00:00:00.000000000&#x27;, &#x27;2018-03-01T00:00:00.000000000&#x27;,\n       &#x27;2018-04-01T00:00:00.000000000&#x27;, &#x27;2018-05-01T00:00:00.000000000&#x27;,\n       &#x27;2018-06-01T00:00:00.000000000&#x27;, &#x27;2018-07-01T00:00:00.000000000&#x27;,\n       &#x27;2018-08-01T00:00:00.000000000&#x27;, &#x27;2018-09-01T00:00:00.000000000&#x27;,\n       &#x27;2018-10-01T00:00:00.000000000&#x27;, &#x27;2018-11-01T00:00:00.000000000&#x27;,\n       &#x27;2018-12-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;,\n       &#x27;2019-04-01T00:00:00.000000000&#x27;, &#x27;2019-05-01T00:00:00.000000000&#x27;,\n       &#x27;2019-06-01T00:00:00.000000000&#x27;, &#x27;2019-07-01T00:00:00.000000000&#x27;,\n       &#x27;2019-08-01T00:00:00.000000000&#x27;, &#x27;2019-09-01T00:00:00.000000000&#x27;,\n       &#x27;2019-10-01T00:00:00.000000000&#x27;, &#x27;2019-11-01T00:00:00.000000000&#x27;,\n       &#x27;2019-12-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8538e7b3-e9db-40d0-b39b-79e1b0605362' class='xr-section-summary-in' type='checkbox'  ><label for='section-8538e7b3-e9db-40d0-b39b-79e1b0605362' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-7fd026b9-eba4-4a47-a9de-fc76a52050bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fd026b9-eba4-4a47-a9de-fc76a52050bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef342d59-d00c-4d16-a0a3-d16a39acfba9' class='xr-var-data-in' type='checkbox'><label for='data-ef342d59-d00c-4d16-a0a3-d16a39acfba9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f14d2f6b-8591-4695-b98e-949464ac715d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f14d2f6b-8591-4695-b98e-949464ac715d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56243f9c-a378-4761-8b4e-dd8a05a67a27' class='xr-var-data-in' type='checkbox'><label for='data-56243f9c-a378-4761-8b4e-dd8a05a67a27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-92c1e5f3-133b-4348-b087-b10f8e9a22d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92c1e5f3-133b-4348-b087-b10f8e9a22d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5a7ebc6-d12b-4fd0-b1e1-2926f378fbd6' class='xr-var-data-in' type='checkbox'><label for='data-b5a7ebc6-d12b-4fd0-b1e1-2926f378fbd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-75069a33-5025-490a-a945-40a46b02d3ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75069a33-5025-490a-a945-40a46b02d3ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ebfa691-c1cf-4be4-a91c-aefae09f21bf' class='xr-var-data-in' type='checkbox'><label for='data-6ebfa691-c1cf-4be4-a91c-aefae09f21bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  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text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-e7b2890c-9ce1-4b7b-81c7-1de02a9de80d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7b2890c-9ce1-4b7b-81c7-1de02a9de80d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d594c22d-3e17-4428-b0d2-f6773df1dd18' class='xr-var-data-in' type='checkbox'><label for='data-d594c22d-3e17-4428-b0d2-f6773df1dd18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-075b7602-4ddd-426b-8bcd-0fcfb9dfc39e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-075b7602-4ddd-426b-8bcd-0fcfb9dfc39e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb2cbd5d-02bb-45b0-a94f-2e762684cf56' class='xr-var-data-in' type='checkbox'><label for='data-cb2cbd5d-02bb-45b0-a94f-2e762684cf56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-ae3ab328-43bb-43b5-b156-370fbbda4606' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae3ab328-43bb-43b5-b156-370fbbda4606' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44e39e56-ff6a-43e0-bde9-7d8b40a119ca' class='xr-var-data-in' type='checkbox'><label for='data-44e39e56-ff6a-43e0-bde9-7d8b40a119ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-bd73c5f2-6b5e-43e5-bf83-43f040f8ce00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd73c5f2-6b5e-43e5-bf83-43f040f8ce00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07fb58f9-ff94-4151-8ef3-40b350a2cace' class='xr-var-data-in' type='checkbox'><label for='data-07fb58f9-ff94-4151-8ef3-40b350a2cace' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-f41dd1b2-f15a-48df-b769-16924ade4170' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f41dd1b2-f15a-48df-b769-16924ade4170' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9da1d08-0bd1-4d75-9a14-54d3b49be522' class='xr-var-data-in' type='checkbox'><label for='data-b9da1d08-0bd1-4d75-9a14-54d3b49be522' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320.00 kiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-e534fe6b-7e1e-4bae-b32b-65b74214350b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e534fe6b-7e1e-4bae-b32b-65b74214350b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-274e1843-9486-48c6-b540-233d77a752d3' class='xr-var-data-in' type='checkbox'><label for='data-274e1843-9486-48c6-b540-233d77a752d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-49d726e4-e830-4250-be10-fa57388b187d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49d726e4-e830-4250-be10-fa57388b187d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6bd15da-eeb4-41ec-844f-416bdcf88f10' class='xr-var-data-in' type='checkbox'><label for='data-e6bd15da-eeb4-41ec-844f-416bdcf88f10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-99ada3c9-6e11-4dcf-9a24-4a4702764c68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99ada3c9-6e11-4dcf-9a24-4a4702764c68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e679593-5786-46a1-9021-1ef006d8e58a' class='xr-var-data-in' type='checkbox'><label for='data-3e679593-5786-46a1-9021-1ef006d8e58a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-67877f71-34b4-4a40-b825-0f8a0b8ed945' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67877f71-34b4-4a40-b825-0f8a0b8ed945' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30393594-fb76-40a8-9519-8f6b705e958c' class='xr-var-data-in' type='checkbox'><label for='data-30393594-fb76-40a8-9519-8f6b705e958c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1be844bd-c228-48db-b3c4-861163f38ec8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1be844bd-c228-48db-b3c4-861163f38ec8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15c1bd1b-82d7-4ebc-9398-1fc14b12023d' class='xr-var-data-in' type='checkbox'><label for='data-15c1bd1b-82d7-4ebc-9398-1fc14b12023d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-60568aea-d7cc-43c9-a819-ca7d11074963' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60568aea-d7cc-43c9-a819-ca7d11074963' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65105234-cc0b-42f7-9a51-416267dcd2d5' class='xr-var-data-in' type='checkbox'><label for='data-65105234-cc0b-42f7-9a51-416267dcd2d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-11a3e582-c003-47a6-bad4-11ad87fcefbd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11a3e582-c003-47a6-bad4-11ad87fcefbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-091b55dd-0cec-4b02-8dbb-098261a4c310' class='xr-var-data-in' type='checkbox'><label for='data-091b55dd-0cec-4b02-8dbb-098261a4c310' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3e19b9d1-27ce-41b4-9514-318c9a50cd3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e19b9d1-27ce-41b4-9514-318c9a50cd3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-171cc027-c4aa-47e3-b694-9d7219058847' class='xr-var-data-in' type='checkbox'><label for='data-171cc027-c4aa-47e3-b694-9d7219058847' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4f834b88-470b-4abe-939a-314f0020a4fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f834b88-470b-4abe-939a-314f0020a4fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4de75d2-a7fd-4183-b031-c9fa8fa4b90e' class='xr-var-data-in' type='checkbox'><label for='data-e4de75d2-a7fd-4183-b031-c9fa8fa4b90e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-713dd58f-cb25-4b89-94a8-636e5701c9d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-713dd58f-cb25-4b89-94a8-636e5701c9d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20b7e3ec-b99c-49ec-b6f7-6345e55cf00d' class='xr-var-data-in' type='checkbox'><label for='data-20b7e3ec-b99c-49ec-b6f7-6345e55cf00d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4dceb78f-51af-4df6-a68f-378b4ba7b300' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4dceb78f-51af-4df6-a68f-378b4ba7b300' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9c93f3a-8b84-444c-9b45-663fbfeaa133' class='xr-var-data-in' type='checkbox'><label for='data-c9c93f3a-8b84-444c-9b45-663fbfeaa133' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-171ea39d-c40a-4092-9597-2e2a4988227a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-171ea39d-c40a-4092-9597-2e2a4988227a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b907299c-d6f2-45a1-9c54-c0829e248e67' class='xr-var-data-in' type='checkbox'><label for='data-b907299c-d6f2-45a1-9c54-c0829e248e67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-0e51ceb7-b6b0-44c1-b1f5-806b7bec6555' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e51ceb7-b6b0-44c1-b1f5-806b7bec6555' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8e08c9e-fd02-45be-83c0-89e30d2542f9' class='xr-var-data-in' type='checkbox'><label for='data-d8e08c9e-fd02-45be-83c0-89e30d2542f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f87e36ff-e9ae-4069-8003-b9e5266eef9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f87e36ff-e9ae-4069-8003-b9e5266eef9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06d1f5ff-87a0-468f-80e4-4bb175984411' class='xr-var-data-in' type='checkbox'><label for='data-06d1f5ff-87a0-468f-80e4-4bb175984411' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-aeb3faf6-d113-4136-b3be-38f1ed2d1582' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aeb3faf6-d113-4136-b3be-38f1ed2d1582' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37fe2b12-0504-4525-ac9e-32e4efb3f272' class='xr-var-data-in' type='checkbox'><label for='data-37fe2b12-0504-4525-ac9e-32e4efb3f272' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-35b78fe0-a582-4850-8eca-847673a581f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35b78fe0-a582-4850-8eca-847673a581f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10cbe3b4-816d-451d-aff6-0e2d6d181778' class='xr-var-data-in' type='checkbox'><label for='data-10cbe3b4-816d-451d-aff6-0e2d6d181778' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d3f6f33e-5bff-4482-9d34-1d9c909dfada' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3f6f33e-5bff-4482-9d34-1d9c909dfada' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1997374e-2d72-4d53-a62e-4915d4ff4ee6' class='xr-var-data-in' type='checkbox'><label for='data-1997374e-2d72-4d53-a62e-4915d4ff4ee6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp_10m</span></div><div class='xr-var-dims'>(time, height, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-97189bce-9832-428f-b052-4db0bbed5a4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97189bce-9832-428f-b052-4db0bbed5a4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bc930bc-2e6f-4550-84ca-e0e86dcfaa6b' class='xr-var-data-in' type='checkbox'><label for='data-4bc930bc-2e6f-4550-84ca-e0e86dcfaa6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>wind speed in 10m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.0</dd><dt><span>standard_name :</span></dt><dd>sp_10m</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"26\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"26\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"27\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"28\" />\n  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"29\" />\n  <line x1=\"15\" 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Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-e6c15ce9-b683-4d2c-ad63-ad0101674d2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6c15ce9-b683-4d2c-ad63-ad0101674d2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7397972-f5ff-41ae-a656-de537902b02e' class='xr-var-data-in' type='checkbox'><label for='data-d7397972-f5ff-41ae-a656-de537902b02e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- 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transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-eb2b086f-8933-4568-ba17-68b9404d4336' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb2b086f-8933-4568-ba17-68b9404d4336' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bf657d3-f2a7-4975-b8bd-751b79eea1d5' class='xr-var-data-in' type='checkbox'><label for='data-5bf657d3-f2a7-4975-b8bd-751b79eea1d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8454062a-4322-4e57-9e5c-232de764cb73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8454062a-4322-4e57-9e5c-232de764cb73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59fd8488-00aa-48d7-854d-552b3b263e97' class='xr-var-data-in' type='checkbox'><label for='data-59fd8488-00aa-48d7-854d-552b3b263e97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-144c2592-a50e-4f15-aa62-9637ab9df24a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-144c2592-a50e-4f15-aa62-9637ab9df24a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-239f309f-74cf-47ed-b0a7-4816fc1951fa' class='xr-var-data-in' type='checkbox'><label for='data-239f309f-74cf-47ed-b0a7-4816fc1951fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8cb62ffc-3cf9-4c83-8877-01ff5db3da80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cb62ffc-3cf9-4c83-8877-01ff5db3da80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5308e92-afa1-4ff3-84f4-d92e97bc0bec' class='xr-var-data-in' type='checkbox'><label for='data-a5308e92-afa1-4ff3-84f4-d92e97bc0bec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-84759989-70d2-48db-adc9-abd0e9036524' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84759989-70d2-48db-adc9-abd0e9036524' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c3d7102-7987-4339-a632-99ba48b3a33c' class='xr-var-data-in' type='checkbox'><label for='data-6c3d7102-7987-4339-a632-99ba48b3a33c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-dca0c49e-23c4-4cd1-9a22-902c0088165d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dca0c49e-23c4-4cd1-9a22-902c0088165d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a818b089-1c3c-4bbb-a8de-df9962559bba' class='xr-var-data-in' type='checkbox'><label for='data-a818b089-1c3c-4bbb-a8de-df9962559bba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-96e374e0-f77a-4895-9a3d-fc727a6956ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96e374e0-f77a-4895-9a3d-fc727a6956ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e065fe19-42a0-41f9-83cb-55ad6eaf59cf' class='xr-var-data-in' type='checkbox'><label for='data-e065fe19-42a0-41f9-83cb-55ad6eaf59cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b075c909-d412-4f72-be86-af5ac9246210' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b075c909-d412-4f72-be86-af5ac9246210' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf38ceb6-7ccd-4b91-b461-45699bf5b1a2' class='xr-var-data-in' type='checkbox'><label for='data-cf38ceb6-7ccd-4b91-b461-45699bf5b1a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ef554989-8a02-473f-956d-5f21efed87cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef554989-8a02-473f-956d-5f21efed87cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c80842a-0b00-4de9-b1ac-68adf0cbe3e4' class='xr-var-data-in' type='checkbox'><label for='data-3c80842a-0b00-4de9-b1ac-68adf0cbe3e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-868bede7-360f-4db2-8b60-0610dfe93218' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-868bede7-360f-4db2-8b60-0610dfe93218' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00cb3f3d-0f1c-4a69-9e4f-82760f4a5422' class='xr-var-data-in' type='checkbox'><label for='data-00cb3f3d-0f1c-4a69-9e4f-82760f4a5422' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-08ed2599-88a3-42c0-88e8-9a92b947a029' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08ed2599-88a3-42c0-88e8-9a92b947a029' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d129506e-ed3c-4e4e-a725-0bdfb2c2e911' class='xr-var-data-in' type='checkbox'><label for='data-d129506e-ed3c-4e4e-a725-0bdfb2c2e911' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-735d9124-10f2-45fe-a50d-74fb12c66a0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-735d9124-10f2-45fe-a50d-74fb12c66a0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18922918-aa1d-4efc-bddd-36ee2e032c49' class='xr-var-data-in' type='checkbox'><label for='data-18922918-aa1d-4efc-bddd-36ee2e032c49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-cece28ba-6dda-4a12-ba86-4c286520790f' class='xr-section-summary-in' type='checkbox'  ><label for='section-cece28ba-6dda-4a12-ba86-4c286520790f' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3d27dc8a-8913-4d4c-8f46-47ff1c29abe4' class='xr-index-data-in' type='checkbox'/><label for='index-3d27dc8a-8913-4d4c-8f46-47ff1c29abe4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bbebbd37-b0d3-4cf9-a252-f136c96e71e6' class='xr-index-data-in' type='checkbox'/><label for='index-bbebbd37-b0d3-4cf9-a252-f136c96e71e6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-74d23bcb-a33e-425a-8dbc-179862f37aee' class='xr-index-data-in' type='checkbox'/><label for='index-74d23bcb-a33e-425a-8dbc-179862f37aee' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2015-02-01&#x27;, &#x27;2015-03-01&#x27;, &#x27;2015-04-01&#x27;, &#x27;2015-05-01&#x27;,\n               &#x27;2015-06-01&#x27;, &#x27;2015-07-01&#x27;, &#x27;2015-08-01&#x27;, &#x27;2015-09-01&#x27;,\n               &#x27;2015-10-01&#x27;, &#x27;2015-11-01&#x27;, &#x27;2015-12-01&#x27;, &#x27;2016-01-01&#x27;,\n               &#x27;2016-02-01&#x27;, &#x27;2016-03-01&#x27;, &#x27;2016-04-01&#x27;, &#x27;2016-05-01&#x27;,\n               &#x27;2016-06-01&#x27;, &#x27;2016-07-01&#x27;, &#x27;2016-08-01&#x27;, &#x27;2016-09-01&#x27;,\n               &#x27;2016-10-01&#x27;, &#x27;2016-11-01&#x27;, &#x27;2016-12-01&#x27;, &#x27;2017-01-01&#x27;,\n               &#x27;2017-02-01&#x27;, &#x27;2017-03-01&#x27;, &#x27;2017-04-01&#x27;, &#x27;2017-05-01&#x27;,\n               &#x27;2017-06-01&#x27;, &#x27;2017-07-01&#x27;, &#x27;2017-08-01&#x27;, &#x27;2017-09-01&#x27;,\n               &#x27;2017-10-01&#x27;, &#x27;2017-11-01&#x27;, &#x27;2017-12-01&#x27;, &#x27;2018-01-01&#x27;,\n               &#x27;2018-02-01&#x27;, &#x27;2018-03-01&#x27;, &#x27;2018-04-01&#x27;, &#x27;2018-05-01&#x27;,\n               &#x27;2018-06-01&#x27;, &#x27;2018-07-01&#x27;, &#x27;2018-08-01&#x27;, &#x27;2018-09-01&#x27;,\n               &#x27;2018-10-01&#x27;, &#x27;2018-11-01&#x27;, &#x27;2018-12-01&#x27;, &#x27;2019-01-01&#x27;,\n               &#x27;2019-02-01&#x27;, &#x27;2019-03-01&#x27;, &#x27;2019-04-01&#x27;, &#x27;2019-05-01&#x27;,\n               &#x27;2019-06-01&#x27;, &#x27;2019-07-01&#x27;, &#x27;2019-08-01&#x27;, &#x27;2019-09-01&#x27;,\n               &#x27;2019-10-01&#x27;, &#x27;2019-11-01&#x27;, &#x27;2019-12-01&#x27;, &#x27;2020-01-01&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3899a397-2fc1-488d-9dc8-db794dbedcc8' class='xr-section-summary-in' type='checkbox'  ><label for='section-3899a397-2fc1-488d-9dc8-db794dbedcc8' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 020510</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
+            "text/plain": "<xarray.Dataset> Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB 89.46 88.64 89.06 ... -27.32 -27.0\n    clon           (ncells) float64 655kB 72.0 50.25 72.0 ... 1.296 0.6204 0.357\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2015-02-01 ... 2020-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albnirdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    clat_bnds      (ncells, vertices) float64 2MB dask.array<chunksize=(81920, 3), meta=np.ndarray>\n    clct           (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqi_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv            (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    umfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    vmfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB 89.46 88.64 89.06 ... -27.32 -27.0\n    clon           (ncells) float64 655kB 72.0 50.25 72.0 ... 1.296 0.6204 0.357\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2015-02-01 ... 2020-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albnirdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    clat_bnds      (ncells, vertices) float64 2MB dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n    clct           (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqi_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv            (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    umfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    vmfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-476f6d22-190e-4fc2-9f75-ceba4096c720' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-476f6d22-190e-4fc2-9f75-ceba4096c720' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-97dbb2c6-2800-4879-a309-cc055ab70525' class='xr-section-summary-in' type='checkbox'  checked><label for='section-97dbb2c6-2800-4879-a309-cc055ab70525' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.46 88.64 89.06 ... -27.32 -27.0</div><input id='attrs-dd5923da-5376-406d-869b-a6f4eadc379e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd5923da-5376-406d-869b-a6f4eadc379e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-508b0108-b4bc-4dae-9bd0-2e649d041491' class='xr-var-data-in' type='checkbox'><label for='data-508b0108-b4bc-4dae-9bd0-2e649d041491' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><pre>array([ 89.45889176,  88.63772156,  89.06223506, ..., -27.28616813,\n       -27.32237258, -27.0023716 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>72.0 50.25 72.0 ... 0.6204 0.357</div><input id='attrs-c36467d6-7eeb-415c-8d7d-8b4245500d6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c36467d6-7eeb-415c-8d7d-8b4245500d6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d5cd5a7-a1a6-4f9b-bc4c-385f1ddf3b35' class='xr-var-data-in' type='checkbox'><label for='data-5d5cd5a7-a1a6-4f9b-bc4c-385f1ddf3b35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><pre>array([72.        , 50.25313351, 72.        , ...,  1.29630984,\n        0.62040403,  0.3569665 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-55a77492-b035-49a8-aa6b-bcf763e0a7ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55a77492-b035-49a8-aa6b-bcf763e0a7ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f12469b6-8b5b-4978-822d-aa99ad34edad' class='xr-var-data-in' type='checkbox'><label for='data-f12469b6-8b5b-4978-822d-aa99ad34edad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-2cb517dc-102a-4ba7-8791-578e78ff3dff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cb517dc-102a-4ba7-8791-578e78ff3dff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-891e8949-c6ce-48ed-b54a-eef0c6d6918d' class='xr-var-data-in' type='checkbox'><label for='data-891e8949-c6ce-48ed-b54a-eef0c6d6918d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-02-01 ... 2020-01-01</div><input id='attrs-9f11dafa-8b4b-4627-9e38-e114157f6fb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f11dafa-8b4b-4627-9e38-e114157f6fb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8d6f664-f355-4304-871f-6c9d50d56021' class='xr-var-data-in' type='checkbox'><label for='data-a8d6f664-f355-4304-871f-6c9d50d56021' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n       &#x27;2016-12-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n       &#x27;2017-02-01T00:00:00.000000000&#x27;, &#x27;2017-03-01T00:00:00.000000000&#x27;,\n       &#x27;2017-04-01T00:00:00.000000000&#x27;, &#x27;2017-05-01T00:00:00.000000000&#x27;,\n       &#x27;2017-06-01T00:00:00.000000000&#x27;, &#x27;2017-07-01T00:00:00.000000000&#x27;,\n       &#x27;2017-08-01T00:00:00.000000000&#x27;, &#x27;2017-09-01T00:00:00.000000000&#x27;,\n       &#x27;2017-10-01T00:00:00.000000000&#x27;, &#x27;2017-11-01T00:00:00.000000000&#x27;,\n       &#x27;2017-12-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n       &#x27;2018-02-01T00:00:00.000000000&#x27;, &#x27;2018-03-01T00:00:00.000000000&#x27;,\n       &#x27;2018-04-01T00:00:00.000000000&#x27;, &#x27;2018-05-01T00:00:00.000000000&#x27;,\n       &#x27;2018-06-01T00:00:00.000000000&#x27;, &#x27;2018-07-01T00:00:00.000000000&#x27;,\n       &#x27;2018-08-01T00:00:00.000000000&#x27;, &#x27;2018-09-01T00:00:00.000000000&#x27;,\n       &#x27;2018-10-01T00:00:00.000000000&#x27;, &#x27;2018-11-01T00:00:00.000000000&#x27;,\n       &#x27;2018-12-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;,\n       &#x27;2019-04-01T00:00:00.000000000&#x27;, &#x27;2019-05-01T00:00:00.000000000&#x27;,\n       &#x27;2019-06-01T00:00:00.000000000&#x27;, &#x27;2019-07-01T00:00:00.000000000&#x27;,\n       &#x27;2019-08-01T00:00:00.000000000&#x27;, &#x27;2019-09-01T00:00:00.000000000&#x27;,\n       &#x27;2019-10-01T00:00:00.000000000&#x27;, &#x27;2019-11-01T00:00:00.000000000&#x27;,\n       &#x27;2019-12-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da81ea68-f16f-4eb2-9d78-d2386e0c2a34' class='xr-section-summary-in' type='checkbox'  ><label for='section-da81ea68-f16f-4eb2-9d78-d2386e0c2a34' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-75125f1d-2600-4caf-bed5-5d2cd12e63e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75125f1d-2600-4caf-bed5-5d2cd12e63e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d0fc992-c79d-493b-85d5-e4be2a32d6c3' class='xr-var-data-in' type='checkbox'><label for='data-9d0fc992-c79d-493b-85d5-e4be2a32d6c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-17b5b174-30b9-4888-89a9-e42a1ee6e974' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17b5b174-30b9-4888-89a9-e42a1ee6e974' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4139139d-9f8a-4f17-8c45-a11a9926d972' class='xr-var-data-in' type='checkbox'><label for='data-4139139d-9f8a-4f17-8c45-a11a9926d972' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f20282e6-6d95-4e7a-b817-154e32077b86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f20282e6-6d95-4e7a-b817-154e32077b86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51258272-b0f4-4199-aad1-5df8c78e27ac' class='xr-var-data-in' type='checkbox'><label for='data-51258272-b0f4-4199-aad1-5df8c78e27ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-50f570cd-c028-4a24-93cf-af45aeb34643' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50f570cd-c028-4a24-93cf-af45aeb34643' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7e5fab8-5daa-45b7-aae0-4a2e53780ebf' class='xr-var-data-in' type='checkbox'><label for='data-f7e5fab8-5daa-45b7-aae0-4a2e53780ebf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  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class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-b2f3f785-3fe8-42db-9113-773ddca8f00e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2f3f785-3fe8-42db-9113-773ddca8f00e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8be29722-3a70-4818-a176-df2da7b29a2e' class='xr-var-data-in' type='checkbox'><label for='data-8be29722-3a70-4818-a176-df2da7b29a2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-69f1e0ff-73d5-45e1-917f-c42d0247f3a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69f1e0ff-73d5-45e1-917f-c42d0247f3a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b231c91c-4157-4c82-95b7-242464f5fc94' class='xr-var-data-in' type='checkbox'><label for='data-b231c91c-4157-4c82-95b7-242464f5fc94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-57ce5fdb-22ef-4a50-aa91-6c3fec4af5ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57ce5fdb-22ef-4a50-aa91-6c3fec4af5ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-923a9870-00ee-4cd6-b5e3-e1ba380befd6' class='xr-var-data-in' type='checkbox'><label for='data-923a9870-00ee-4cd6-b5e3-e1ba380befd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-215bc499-4976-4446-a002-74f06564e3b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-215bc499-4976-4446-a002-74f06564e3b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4daea53a-f789-440c-b691-f1d5808c4bfb' class='xr-var-data-in' type='checkbox'><label for='data-4daea53a-f789-440c-b691-f1d5808c4bfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-913d51b7-5159-43cd-b515-4b152db503cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-913d51b7-5159-43cd-b515-4b152db503cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79f1af18-8170-4a17-8539-2e672e1da3d3' class='xr-var-data-in' type='checkbox'><label for='data-79f1af18-8170-4a17-8539-2e672e1da3d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320.00 kiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-94ce0900-063e-4552-aa2c-715c4ef00fcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94ce0900-063e-4552-aa2c-715c4ef00fcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-966c4203-3992-4143-a197-cfe431892b14' class='xr-var-data-in' type='checkbox'><label for='data-966c4203-3992-4143-a197-cfe431892b14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c4b9badb-5998-4d74-bfdf-bd921e4e04a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4b9badb-5998-4d74-bfdf-bd921e4e04a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ddc770a-c2f9-41a7-84f0-dcae3afce69d' class='xr-var-data-in' type='checkbox'><label for='data-2ddc770a-c2f9-41a7-84f0-dcae3afce69d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-0af72539-fd50-47ce-b405-5cfa49775d20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0af72539-fd50-47ce-b405-5cfa49775d20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87427130-444a-4210-9e3a-0ac275557788' class='xr-var-data-in' type='checkbox'><label for='data-87427130-444a-4210-9e3a-0ac275557788' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-99067a5d-7bf0-4aee-8ec2-3b68f57b85f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99067a5d-7bf0-4aee-8ec2-3b68f57b85f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25cbc13f-3901-418e-a7b7-a2d39fb43946' class='xr-var-data-in' type='checkbox'><label for='data-25cbc13f-3901-418e-a7b7-a2d39fb43946' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2e4f1507-dda4-41dc-a07b-d4faf6558cbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e4f1507-dda4-41dc-a07b-d4faf6558cbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bdb4829-12af-472c-b74c-d34a93377e18' class='xr-var-data-in' type='checkbox'><label for='data-8bdb4829-12af-472c-b74c-d34a93377e18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c315f50b-9ece-461d-8c3d-610fd9af342f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c315f50b-9ece-461d-8c3d-610fd9af342f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b33dead-506f-4a67-8297-8e45085546c1' class='xr-var-data-in' type='checkbox'><label for='data-2b33dead-506f-4a67-8297-8e45085546c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-7c1fbce8-ffcd-48f2-ab7f-154359db29a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c1fbce8-ffcd-48f2-ab7f-154359db29a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50db397b-bcef-4732-886f-8397f2cbcf9d' class='xr-var-data-in' type='checkbox'><label for='data-50db397b-bcef-4732-886f-8397f2cbcf9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-6c66d9fb-9e41-4be5-b19c-9d01cd6f8f2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c66d9fb-9e41-4be5-b19c-9d01cd6f8f2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8671a2b0-5d12-4d5d-9b67-f746709a2184' class='xr-var-data-in' type='checkbox'><label for='data-8671a2b0-5d12-4d5d-9b67-f746709a2184' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-cbc3ebd6-182d-4288-87cb-3d55806c0254' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbc3ebd6-182d-4288-87cb-3d55806c0254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d4e0d92-d208-45f1-a9d5-aefd69b57d88' class='xr-var-data-in' type='checkbox'><label for='data-8d4e0d92-d208-45f1-a9d5-aefd69b57d88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-dc9665ec-dc56-4eb0-9cc0-b70a4ebe7819' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc9665ec-dc56-4eb0-9cc0-b70a4ebe7819' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d4590ed-9f0a-4bc0-889b-f032141412a2' class='xr-var-data-in' type='checkbox'><label for='data-3d4590ed-9f0a-4bc0-889b-f032141412a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-064700e0-cfd3-4650-9800-0dde8be333a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-064700e0-cfd3-4650-9800-0dde8be333a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5deb341-2e78-4deb-ab81-e54b57499743' class='xr-var-data-in' type='checkbox'><label for='data-e5deb341-2e78-4deb-ab81-e54b57499743' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ce03ac63-ef24-49ab-b978-7908d28e1e35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce03ac63-ef24-49ab-b978-7908d28e1e35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34b28deb-d7ba-433d-a360-2af4851581aa' class='xr-var-data-in' type='checkbox'><label for='data-34b28deb-d7ba-433d-a360-2af4851581aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8960b254-132e-4b24-9c8c-404bbc02f44c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8960b254-132e-4b24-9c8c-404bbc02f44c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47367123-3e25-4fe9-ba28-e85e0efb623b' class='xr-var-data-in' type='checkbox'><label for='data-47367123-3e25-4fe9-ba28-e85e0efb623b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b2427e54-3820-47af-afdc-8ae908ed1446' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2427e54-3820-47af-afdc-8ae908ed1446' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce266f07-56c2-4999-986f-f161f4912287' class='xr-var-data-in' type='checkbox'><label for='data-ce266f07-56c2-4999-986f-f161f4912287' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c0829ea2-33ac-42c1-b28b-6b3f47a46a62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0829ea2-33ac-42c1-b28b-6b3f47a46a62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1f827eb-c339-4fd8-9364-6a6cc413c71e' class='xr-var-data-in' type='checkbox'><label for='data-d1f827eb-c339-4fd8-9364-6a6cc413c71e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5d1e6660-f364-4a0e-b3dd-51812ed2731a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d1e6660-f364-4a0e-b3dd-51812ed2731a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79ed8637-09f1-448f-82c4-630026364b7c' class='xr-var-data-in' type='checkbox'><label for='data-79ed8637-09f1-448f-82c4-630026364b7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-e96758f6-d1ca-4f29-89ae-702b0f4d91c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e96758f6-d1ca-4f29-89ae-702b0f4d91c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-779004f0-8172-44a7-8dbb-2b50af520212' class='xr-var-data-in' type='checkbox'><label for='data-779004f0-8172-44a7-8dbb-2b50af520212' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp_10m</span></div><div class='xr-var-dims'>(time, height, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3a426bc4-c855-46e2-9049-1e71f96b1d73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a426bc4-c855-46e2-9049-1e71f96b1d73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9292982e-8180-4130-b46f-f3392027086f' class='xr-var-data-in' type='checkbox'><label for='data-9292982e-8180-4130-b46f-f3392027086f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>wind speed in 10m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.0</dd><dt><span>standard_name :</span></dt><dd>sp_10m</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"26\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"26\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"27\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"28\" />\n  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"29\" />\n  <line x1=\"15\" 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Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4b7ff6b5-26c2-4d45-bddd-0233076d240a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b7ff6b5-26c2-4d45-bddd-0233076d240a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cf96390-4e98-44cb-a3f1-fb8eb2f44878' class='xr-var-data-in' type='checkbox'><label for='data-4cf96390-4e98-44cb-a3f1-fb8eb2f44878' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- 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transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2f7d92cd-7bad-4384-9387-8ecfb82d13ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f7d92cd-7bad-4384-9387-8ecfb82d13ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4305817-4412-4080-b2da-769a7c5ba663' class='xr-var-data-in' type='checkbox'><label for='data-f4305817-4412-4080-b2da-769a7c5ba663' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-32405702-c6cb-4986-a334-1f609479fa92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32405702-c6cb-4986-a334-1f609479fa92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-058d07d6-c898-4a76-ac8c-0546fb94304c' class='xr-var-data-in' type='checkbox'><label for='data-058d07d6-c898-4a76-ac8c-0546fb94304c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-07305c90-5e9f-43f2-b6e0-74cd1d6ecb4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07305c90-5e9f-43f2-b6e0-74cd1d6ecb4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd87a60a-0049-46d4-92cf-487295b74472' class='xr-var-data-in' type='checkbox'><label for='data-bd87a60a-0049-46d4-92cf-487295b74472' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ddab5de8-a100-4cae-8c54-8ef12263a56c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddab5de8-a100-4cae-8c54-8ef12263a56c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6711a7b7-f640-4cae-b26f-b876f15573ed' class='xr-var-data-in' type='checkbox'><label for='data-6711a7b7-f640-4cae-b26f-b876f15573ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-901339db-bbf3-4ff8-bdad-1811bb252773' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-901339db-bbf3-4ff8-bdad-1811bb252773' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6b1baba-b9dd-47eb-ba48-dd9e78efe2fa' class='xr-var-data-in' type='checkbox'><label for='data-c6b1baba-b9dd-47eb-ba48-dd9e78efe2fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8e8f2e51-0d91-46fc-86d2-b3e275ff0184' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e8f2e51-0d91-46fc-86d2-b3e275ff0184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36ffad12-e338-417d-9974-3b15bb874d67' class='xr-var-data-in' type='checkbox'><label for='data-36ffad12-e338-417d-9974-3b15bb874d67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b0ba6877-dab3-4346-9e05-909b76c89f50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0ba6877-dab3-4346-9e05-909b76c89f50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff2bf06b-93a7-4638-a44c-238c3930c60a' class='xr-var-data-in' type='checkbox'><label for='data-ff2bf06b-93a7-4638-a44c-238c3930c60a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-cbd5bfe0-fe82-4599-a43b-e838ed59c2b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbd5bfe0-fe82-4599-a43b-e838ed59c2b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f900b986-f173-4439-b131-7853a137fe7e' class='xr-var-data-in' type='checkbox'><label for='data-f900b986-f173-4439-b131-7853a137fe7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c0cb0400-9727-453a-b701-330b83f7ce15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0cb0400-9727-453a-b701-330b83f7ce15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30225307-50ee-4034-a9dd-aad654ec200d' class='xr-var-data-in' type='checkbox'><label for='data-30225307-50ee-4034-a9dd-aad654ec200d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2d6c7f5b-a597-49df-b22d-7ccce40fbd5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d6c7f5b-a597-49df-b22d-7ccce40fbd5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e485daf7-8f19-4bea-ba4b-ade010ee6110' class='xr-var-data-in' type='checkbox'><label for='data-e485daf7-8f19-4bea-ba4b-ade010ee6110' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d03900fc-9fd3-4789-bdc0-3dde3214cc07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d03900fc-9fd3-4789-bdc0-3dde3214cc07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43c4f804-90a7-4ecd-bb1c-3f800927ce32' class='xr-var-data-in' type='checkbox'><label for='data-43c4f804-90a7-4ecd-bb1c-3f800927ce32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c55dfb5d-728f-46d8-9f24-55a3d57f1776' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c55dfb5d-728f-46d8-9f24-55a3d57f1776' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfa3c8d5-cf78-42fd-99c3-78cb2f31e6cf' class='xr-var-data-in' type='checkbox'><label for='data-bfa3c8d5-cf78-42fd-99c3-78cb2f31e6cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-970bd62b-a15d-474f-b884-66c6fd0c5372' class='xr-section-summary-in' type='checkbox'  ><label for='section-970bd62b-a15d-474f-b884-66c6fd0c5372' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6d6fc225-810e-4943-b93f-ab8ec5692611' class='xr-index-data-in' type='checkbox'/><label for='index-6d6fc225-810e-4943-b93f-ab8ec5692611' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8d042a6-65ed-4f17-85f7-21c05f52edd3' class='xr-index-data-in' type='checkbox'/><label for='index-b8d042a6-65ed-4f17-85f7-21c05f52edd3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d287326d-de80-4abb-bc92-06c13c94187b' class='xr-index-data-in' type='checkbox'/><label for='index-d287326d-de80-4abb-bc92-06c13c94187b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2015-02-01&#x27;, &#x27;2015-03-01&#x27;, &#x27;2015-04-01&#x27;, &#x27;2015-05-01&#x27;,\n               &#x27;2015-06-01&#x27;, &#x27;2015-07-01&#x27;, &#x27;2015-08-01&#x27;, &#x27;2015-09-01&#x27;,\n               &#x27;2015-10-01&#x27;, &#x27;2015-11-01&#x27;, &#x27;2015-12-01&#x27;, &#x27;2016-01-01&#x27;,\n               &#x27;2016-02-01&#x27;, &#x27;2016-03-01&#x27;, &#x27;2016-04-01&#x27;, &#x27;2016-05-01&#x27;,\n               &#x27;2016-06-01&#x27;, &#x27;2016-07-01&#x27;, &#x27;2016-08-01&#x27;, &#x27;2016-09-01&#x27;,\n               &#x27;2016-10-01&#x27;, &#x27;2016-11-01&#x27;, &#x27;2016-12-01&#x27;, &#x27;2017-01-01&#x27;,\n               &#x27;2017-02-01&#x27;, &#x27;2017-03-01&#x27;, &#x27;2017-04-01&#x27;, &#x27;2017-05-01&#x27;,\n               &#x27;2017-06-01&#x27;, &#x27;2017-07-01&#x27;, &#x27;2017-08-01&#x27;, &#x27;2017-09-01&#x27;,\n               &#x27;2017-10-01&#x27;, &#x27;2017-11-01&#x27;, &#x27;2017-12-01&#x27;, &#x27;2018-01-01&#x27;,\n               &#x27;2018-02-01&#x27;, &#x27;2018-03-01&#x27;, &#x27;2018-04-01&#x27;, &#x27;2018-05-01&#x27;,\n               &#x27;2018-06-01&#x27;, &#x27;2018-07-01&#x27;, &#x27;2018-08-01&#x27;, &#x27;2018-09-01&#x27;,\n               &#x27;2018-10-01&#x27;, &#x27;2018-11-01&#x27;, &#x27;2018-12-01&#x27;, &#x27;2019-01-01&#x27;,\n               &#x27;2019-02-01&#x27;, &#x27;2019-03-01&#x27;, &#x27;2019-04-01&#x27;, &#x27;2019-05-01&#x27;,\n               &#x27;2019-06-01&#x27;, &#x27;2019-07-01&#x27;, &#x27;2019-08-01&#x27;, &#x27;2019-09-01&#x27;,\n               &#x27;2019-10-01&#x27;, &#x27;2019-11-01&#x27;, &#x27;2019-12-01&#x27;, &#x27;2020-01-01&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-18ddb750-c1e0-4636-a618-7de2d8972256' class='xr-section-summary-in' type='checkbox'  ><label for='section-18ddb750-c1e0-4636-a618-7de2d8972256' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 020510</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
           },
           "metadata": {}
         }
@@ -246,7 +254,7 @@
           "execution_count": 8,
           "output_type": "execute_result",
           "data": {
-            "text/plain": "<earthkit.plots.components.maps.Map at 0x16f278c0>"
+            "text/plain": "<earthkit.plots.components.maps.Map at 0x10e42e48>"
           },
           "metadata": {}
         },
@@ -254,7 +262,7 @@
           "output_type": "display_data",
           "data": {
             "text/plain": "<Figure size 700x600 with 2 Axes>",
-            "image/png": 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"
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"
           },
           "metadata": {}
         }

From d1269883cc91cd93efa19a10075e43eedecc88fc Mon Sep 17 00:00:00 2001
From: Sara Faghih-Naini <sara.faghihnaini@ecmwf.int>
Date: Mon, 9 Dec 2024 17:51:28 +0100
Subject: [PATCH 6/8] Update READMEs

---
 04-example-datasets/README.md | 6 ++++--
 README.md                     | 7 ++++---
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/04-example-datasets/README.md b/04-example-datasets/README.md
index b54e56f..19ab839 100644
--- a/04-example-datasets/README.md
+++ b/04-example-datasets/README.md
@@ -5,8 +5,10 @@ The **Online Laboratory for Data Compression** is built so you can explore vario
 
 ## Overview of the provided notebooks
 
-- [`01-hplp.ipynb`](01-hplp.ipynb): open hplp-experiment datasets from the ECMWF S3 bucket
-- [`02-OpenIFS.ipynb`](02-OpenIFS.ipynb): open OpenIFS-experiment datasets from the ECMWF S3 bucket
+- [`01-hplp.ipynb`](01-hplp.ipynb): open hplp-experiment dataset from the ECMWF S3 bucket
+- [`02-OpenIFS.ipynb`](02-OpenIFS.ipynb): open OpenIFS-experiment dataset from the ECMWF S3 bucket
+- [`03-NextGEMS.ipynb`](03-NextGEMS.ipynb): open NextGEMS-experiment dataset from the ECMWF S3 bucket
+- [`04-ICONXPP.ipynb`](04-ICONXPP.ipynb): open ICON-XPP-experiment dataset from the ECMWF S3 bucket
 
 
 ## License
diff --git a/README.md b/README.md
index a315237..ed3e82b 100644
--- a/README.md
+++ b/README.md
@@ -49,9 +49,10 @@ The following is an overview of all notebooks:
 - [`03-examples/`](03-examples/README.md): Longer walkthrough examples that apply and evaluate data compression on different variables
   - [`01-compressors.ipynb`](03-examples/01-compressors.ipynb): comparison of different compressors on a small temperature and specific humidity dataset
 - [`04-example-datasets/`](04-example-datasets/README.md): Example datasets and access via an S3 bucket
-  - [`01-hplp.ipynb`](04-example-datasets/01-hplp.ipynb): open hplp-experiment datasets from the ECMWF S3 bucket
-  - [`02-OpenIFS.ipynb`](04-example-datasets/02-OpenIFS.ipynb): open OpenIFS-experiment datasets from the ECMWF S3 bucket 
-
+  - [`01-hplp.ipynb`](04-example-datasets/01-hplp.ipynb): open hplp-experiment dataset from the ECMWF S3 bucket
+  - [`02-OpenIFS.ipynb`](04-example-datasets/02-OpenIFS.ipynb): open OpenIFS-experiment dataset from the ECMWF S3 bucket
+  - [`03-NextGEMS.ipynb`](04-example-datasets/03-NextGEMS.ipynb): open NextGEMS-experiment dataset from the ECMWF S3 bucket
+  - [`04-ICONXPP.ipynb`](04-example-datasets/04-ICONXPP.ipynb): open ICON-XPP-experiment dataset from the ECMWF S3 bucket
 
 
 ## Getting Help and Contributing

From d3fe3497973956ff43ce04f0af219c80d09961ee Mon Sep 17 00:00:00 2001
From: Sara Faghih-Naini <sara.faghihnaini@ecmwf.int>
Date: Fri, 13 Dec 2024 15:18:57 +0100
Subject: [PATCH 7/8] Update notebooks

---
 04-example-datasets/01-hplp.ipynb     |   45 +-
 04-example-datasets/02-OpenIFS.ipynb  |   70 +-
 04-example-datasets/03-NextGEMS.ipynb | 1639 ++++---------------------
 04-example-datasets/04-ICONXPP.ipynb  |   30 +-
 4 files changed, 271 insertions(+), 1513 deletions(-)

diff --git a/04-example-datasets/01-hplp.ipynb b/04-example-datasets/01-hplp.ipynb
index e1e8abc..d95ffd7 100644
--- a/04-example-datasets/01-hplp.ipynb
+++ b/04-example-datasets/01-hplp.ipynb
@@ -36,7 +36,7 @@
     {
       "id": "463bf2c0-ac3f-4786-9687-362393802dce",
       "cell_type": "markdown",
-      "source": "This notebook is a comprehensive guide to accessing GRIB files for the [hplp experiment](https://apps.ecmwf.int/ifs-experiments/rd/hplp/). It includes details about the available parameters, levels, and steps, along with instructions for using the `load_hplp_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/). The GRIB files are not downloaded locally; instead, they are accessed remotely using `gribscan`, `fsspec`, and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `gribscan`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point single-time atmospheric and wave model (long window 4Dvar) outputs from the IFS.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco399), corresponding to a horizontal resolution of approximately 28 km, and with 137 vertical model levels. The 10-day weather forecast is provided in 12-hour intervals, beginning from 2020-07-21 at 00:00:00 UTC.\nThe total volume of the available dataset is about 187 GB.\n\nThe GRIB files are organized into different types of data fields based on the levels at which they are stored.\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in hPa):\n```python\n[1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000]\n```\n\nThe corresponding IDs and more information are given in the table below:",
+      "source": "This notebook is a comprehensive guide to accessing GRIB files for the [hplp experiment](https://apps.ecmwf.int/ifs-experiments/rd/hplp/). It includes details about the available parameters, levels, and steps, along with instructions for using the `load_hplp_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/). The GRIB files are not downloaded locally; instead, they are accessed remotely using `gribscan`, `fsspec`, and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `gribscan`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point single-time atmospheric and wave model (long window 4Dvar) outputs from the IFS.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco399), corresponding to a horizontal resolution of approximately 28 km, and with 137 vertical model levels. The 10-day weather forecast is provided in 12-hour intervals, beginning from 2020-07-21 at 00:00:00 UTC.\nThe total volume of the available dataset is about 187 GB.\n\nThe GRIB files are organized into different types of data fields based on the levels at which they are stored. This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/).\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in hPa):\n```python\n[1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000]\n```\n\nThe corresponding IDs and more information are given in the table below:",
       "metadata": {}
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@@ -84,7 +84,7 @@
     {
       "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
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-      "source": "## Available Steps \n\nThe following timesteps are available: \n\n```python \n[0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144, 156, 168, 180, 192, 204, 216, 228, 240]\n ```\nTimestep 0 is 2020-07-21T 00:00:00 UTC, increasing in 12-hour steps to 240  which is 2020-07-31T 00:00:00 UTC.\n\n## Using the `load_hplp_data` Function\n\nTo load the GRIB files, you can use the following function:\n\n```python\nload_hplp_data(leveltype=None, gridtype=None, step=None, remap=False)\n```\n\n### Parameters\n\n- **`leveltype`**: Defines the type of data to load. Choose one of the following:\n  - `\"pl\"`: Pressure level fields.\n  - `\"ml\"`: Model level fields.\n  - `\"sfc\"`: Surface level fields.\n  - `\"wave\"`: Wave model fields.\n- **`gridtype`**: Specifies the grid type for the data. Options include:\n  - `\"reduced_gg\"`: Reduced Gaussian grid (available for `pl`, `ml`, and `sfc`).\n  - `\"sh\"`: Spherical harmonic coefficients (available for `pl` and `ml`).\n  - `\"reduced_ll\"`: Reduced lat-long grid (available for `wave`).\n- **`step`**: The `ml` data on the `reduced_gg` grid is split into two files. Specify the timestep (`step`) to load the corresponding file. Valid steps are:\n  - `0-120`: First file (2020-07-21T00:00:00 to 2020-07-26T00:00:00).\n  - `132-240`: Second file (2020-07-26T12:00:00 to 2020-07-31T00:00:00).\n- **`remap`**: When using gridtype=`sh`, data cannot be directly plotted. To load remapped data on an O400 grid, set `remap=True`.",
+      "source": "## Available Steps \n\nThe following timesteps are available: \n\n```python \n[0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144, 156, 168, 180, 192, 204, 216, 228, 240]\n ```\nTimestep 0 is 2020-07-21T 00:00:00 UTC, increasing in 12-hour steps to 240  which is 2020-07-31T 00:00:00 UTC.\n\n## Using the `load_hplp_data` Function\n\nTo load the GRIB files, you can use the following function:\n\n```python\nload_hplp_data(leveltype=None, gridtype=None, step=None, remap=False)\n```\n\n### Parameters\n\n- **`leveltype`**: Defines the type of data to load. Choose one of the following:\n  - `\"pl\"`: Pressure level fields.\n  - `\"ml\"`: Model level fields.\n  - `\"sfc\"`: Surface level fields.\n  - `\"wave\"`: Wave model fields.\n- **`gridtype`**: Specifies the grid type for the data. Options include:\n  - `\"reduced_gg\"`: Reduced Gaussian grid (available for `pl`, `ml`, and `sfc`).\n  - `\"sh\"`: Spherical harmonic coefficients (available for `pl` and `ml`).\n  - `\"reduced_ll\"`: Reduced lat-long grid (available for `wave`).\n- **`step`**: The `ml` data on the `reduced_gg` grid are split into two files. Specify the timestep (`step`) to load the corresponding file. Valid steps are:\n  - `0-120`: First file (2020-07-21T00:00:00 to 2020-07-26T00:00:00).\n  - `132-240`: Second file (2020-07-26T12:00:00 to 2020-07-31T00:00:00).\n- **`remap`**: When using gridtype=`sh`, data cannot be directly plotted. To load remapped data on an O400 grid, set `remap=True`.",
       "metadata": {}
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     {
@@ -113,7 +113,7 @@
       "outputs": [
         {
           "name": "stdout",
-          "text": "[pyodide]: Loading aiohttp, aiosignal, asciitree, async-timeout, attrs, cffi, cfgrib, click, eccodes, findlibs, frozenlist, fsspec, gribscan, msgpack, multidict, numcodecs, numpy, openblas, pandas, propcache, pycparser, python-dateutil, pytz, scipy, tzdata, xarray, yarl, zarr\n[pyodide]: Loaded aiohttp, aiosignal, asciitree, async-timeout, attrs, cffi, cfgrib, click, eccodes, findlibs, frozenlist, fsspec, gribscan, msgpack, multidict, numcodecs, numpy, openblas, pandas, propcache, pycparser, python-dateutil, pytz, scipy, tzdata, xarray, yarl, zarr\n[pyodide]: Memory usage has grown to 202.8MiB (from 49.9MiB) for this notebook\n",
+          "text": "[pyodide]: Memory usage has grown to 206.8MiB (from 49.9MiB) for this notebook\n",
           "output_type": "stream"
         }
       ],
@@ -128,7 +128,7 @@
     {
       "id": "3a68e663-c6c8-49c2-9678-929d87f772b4",
       "cell_type": "code",
-      "source": "import json\n\nimport requests\nimport xarray as xr\n\ndef load_hplp_data(leveltype=None, gridtype=None, step=None, remap=False):\n    if leveltype not in {\"pl\", \"ml\", \"sfc\", \"wave\"}:\n        raise ValueError(f\"Invalid leveltype: '{leveltype}'. Available leveltypes: pl, ml, sfc, wave\")\n\n    if leveltype in {\"ml\", \"pl\"} and not gridtype:\n        raise ValueError(f\"Gridtype is required for leveltype '{leveltype}'. Available: reduced_gg, sh\")\n\n    if remap and gridtype != \"sh\":\n        raise ValueError(f\"Only 'sh' fields can be remapped.\")\n\n    if leveltype == \"wave\" and gridtype != \"reduced_ll\":\n        print(\"Warning: Wave model data is stored on a reduced_ll grid.\")\n\n    if leveltype == \"sfc\" and gridtype != \"reduced_gg\":\n        print(\"Warning: Surface level data is stored on a reduced_gg grid.\")\n\n    if step and not (leveltype == \"ml\" and gridtype == \"reduced_gg\"):\n        print(\"Warning: Specifying 'step' is unnecessary for this configuration and will be ignored.\")\n\n    if leveltype in {\"sfc\", \"wave\"}:\n        url = f\"{BASE_URL}/hplp/hplp_{leveltype}.grib\"\n    elif leveltype == \"ml\" and gridtype == \"reduced_gg\":\n        if step is None:\n            raise ValueError(\n                \"The ml reduced_gg data is split into two parts:\\n\"\n                \"  - Steps: 0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120 (2020-07-21T00:00:00 to 2020-07-26T00:00:00)\\n\"\n                \"  - Steps: 132, 144, 156, 168, 180, 192, 204, 216, 228, 240 (2020-07-26T12:00:00 to 2020-07-31T00:00:00)\\n\"\n                \"Specify a step smaller than 120 for accessing the first part, \\n\"\n                \"and a step greater or equal to 132 for accessing the second part.\"\n            )          \n        if step <= 120:\n            url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_0_120.grib\"\n        else:\n            url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_132_240.grib\"\n    else:\n        url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}\" + (\"_O400.grib\" if remap else \".grib\")\n    ref = requests.get(f\"{url}.ref\").json()\n\n    print(f\"Loading dataset {url}\")\n\n    return xr.open_dataset(\n        \"reference://\",\n        engine=\"zarr\",\n        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n        consolidated=False,\n    )",
+      "source": "import json\n\nimport requests\nimport xarray as xr\n\ndef load_hplp_data(leveltype=None, gridtype=None, step=None, remap=False):\n    if leveltype not in {\"pl\", \"ml\", \"sfc\", \"wave\"}:\n        raise ValueError(f\"Invalid leveltype: '{leveltype}'. Available leveltypes: pl, ml, sfc, wave\")\n\n    if leveltype in {\"ml\", \"pl\"} and not gridtype:\n        raise ValueError(f\"Gridtype is required for leveltype '{leveltype}'. Available: reduced_gg, sh\")\n\n    if remap and gridtype != \"sh\":\n        raise ValueError(f\"Only 'sh' fields can be remapped.\")\n\n    if leveltype == \"wave\" and gridtype != \"reduced_ll\":\n        print(\"Warning: Wave model data are stored on a reduced_ll grid.\")\n\n    if leveltype == \"sfc\" and gridtype != \"reduced_gg\":\n        print(\"Warning: Surface level data are stored on a reduced_gg grid.\")\n\n    if step and not (leveltype == \"ml\" and gridtype == \"reduced_gg\"):\n        print(\"Warning: Specifying 'step' is unnecessary for this configuration and will be ignored.\")\n\n    if leveltype in {\"sfc\", \"wave\"}:\n        url = f\"{BASE_URL}/hplp/hplp_{leveltype}.grib\"\n    elif leveltype == \"ml\" and gridtype == \"reduced_gg\":\n        if step is None:\n            raise ValueError(\n                \"The ml reduced_gg data are split into two parts:\\n\"\n                \"  - Steps: 0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120 (2020-07-21T00:00:00 to 2020-07-26T00:00:00)\\n\"\n                \"  - Steps: 132, 144, 156, 168, 180, 192, 204, 216, 228, 240 (2020-07-26T12:00:00 to 2020-07-31T00:00:00)\\n\"\n                \"Specify a step smaller than 120 for accessing the first part, \\n\"\n                \"and a step greater or equal to 132 for accessing the second part.\"\n            )          \n        if step <= 120:\n            url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_0_120.grib\"\n        else:\n            url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_132_240.grib\"\n    else:\n        url = f\"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}\" + (\"_O400.grib\" if remap else \".grib\")\n    ref = requests.get(f\"{url}.ref\").json()\n\n    print(f\"Loading dataset {url}\")\n\n    return xr.open_dataset(\n        \"reference://\",\n        engine=\"zarr\",\n        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n        consolidated=False,\n    )",
       "metadata": {
         "trusted": true
       },
@@ -159,7 +159,7 @@
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.xr-var-data,\n.xr-index-data-in:checked ~ .xr-index-data {\n  display: block;\n}\n\n.xr-var-data > table {\n  float: right;\n}\n\n.xr-var-name span,\n.xr-var-data,\n.xr-index-name div,\n.xr-index-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2,\n.xr-no-icon {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\nDimensions:  (value: 654400, level: 21, time: 21)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    q        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1fedd7af-88be-466e-ac2c-4cabc1fce06a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1fedd7af-88be-466e-ac2c-4cabc1fce06a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>value</span>: 654400</li><li><span class='xr-has-index'>level</span>: 21</li><li><span class='xr-has-index'>time</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d3c1afa5-1576-40cb-9f93-eda1bf842f26' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3c1afa5-1576-40cb-9f93-eda1bf842f26' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-52aeb07b-6085-4863-9344-144c109afac9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52aeb07b-6085-4863-9344-144c109afac9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-915330a2-7547-4b0e-bccc-6e367fb50b89' class='xr-var-data-in' type='checkbox'><label for='data-915330a2-7547-4b0e-bccc-6e367fb50b89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 500 700 850 925 1000</div><input id='attrs-d0518e50-5241-4b11-a7bf-9c28fda6ce2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0518e50-5241-4b11-a7bf-9c28fda6ce2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-694892fc-0273-4217-8583-c45811c0c374' class='xr-var-data-in' type='checkbox'><label for='data-694892fc-0273-4217-8583-c45811c0c374' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fab5b8d7-3519-4bd4-88fe-0d7e315432f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fab5b8d7-3519-4bd4-88fe-0d7e315432f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7484b4f4-1c27-48d5-af9a-b10e636c743b' class='xr-var-data-in' type='checkbox'><label for='data-7484b4f4-1c27-48d5-af9a-b10e636c743b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-ec771e90-65ea-4c1c-a9a5-3490d9a80f1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec771e90-65ea-4c1c-a9a5-3490d9a80f1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4f2bc91-8f46-45c7-b8c7-b24e55a8ffe2' class='xr-var-data-in' type='checkbox'><label for='data-b4f2bc91-8f46-45c7-b8c7-b24e55a8ffe2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a47b7d1-ab02-40c0-ac70-85a9e304f9e5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a47b7d1-ab02-40c0-ac70-85a9e304f9e5' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-487ee364-bbb4-46f0-8baf-5a6edc01fd83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-487ee364-bbb4-46f0-8baf-5a6edc01fd83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-203afd7d-c41f-4169-9f01-b153ab984bee' class='xr-var-data-in' type='checkbox'><label for='data-203afd7d-c41f-4169-9f01-b153ab984bee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>133</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>q</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>specific_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>q</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5edf564f-d246-4824-9cf8-a257bfb49e8a' class='xr-section-summary-in' type='checkbox'  ><label for='section-5edf564f-d246-4824-9cf8-a257bfb49e8a' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2aa76ac4-aa5c-44f9-849e-22dceda5ca41' class='xr-index-data-in' type='checkbox'/><label for='index-2aa76ac4-aa5c-44f9-849e-22dceda5ca41' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000],\n      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe07ca7a-bb99-4322-b232-407be8755434' class='xr-index-data-in' type='checkbox'/><label for='index-fe07ca7a-bb99-4322-b232-407be8755434' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16850edf-eb00-4290-98ff-053792f7de16' class='xr-section-summary-in' type='checkbox'  checked><label for='section-16850edf-eb00-4290-98ff-053792f7de16' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\nDimensions:  (value: 654400, level: 21, time: 21)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    q        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7bbe02b3-f641-4e14-b184-75f24fce627e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7bbe02b3-f641-4e14-b184-75f24fce627e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>value</span>: 654400</li><li><span class='xr-has-index'>level</span>: 21</li><li><span class='xr-has-index'>time</span>: 21</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1838017e-beb9-47fe-aac9-576fd53b73d2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1838017e-beb9-47fe-aac9-576fd53b73d2' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0feb064c-ab95-44e1-925e-6c3da5fb1107' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0feb064c-ab95-44e1-925e-6c3da5fb1107' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e109a216-d7ba-4494-8310-844cbd3b5dcf' class='xr-var-data-in' type='checkbox'><label for='data-e109a216-d7ba-4494-8310-844cbd3b5dcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 500 700 850 925 1000</div><input id='attrs-05a67575-c165-4adb-965e-f1d4e2daff49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05a67575-c165-4adb-965e-f1d4e2daff49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a73e3a32-8170-4c2e-9e86-be374231a87d' class='xr-var-data-in' type='checkbox'><label for='data-a73e3a32-8170-4c2e-9e86-be374231a87d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-74b12ce8-b6d6-4c1f-bff5-efee26f9685b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74b12ce8-b6d6-4c1f-bff5-efee26f9685b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7d71a7f-34c6-4b3d-98b1-9e728e9b7f51' class='xr-var-data-in' type='checkbox'><label for='data-f7d71a7f-34c6-4b3d-98b1-9e728e9b7f51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-a82fd799-8826-47f8-8298-dc046a1ee9a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a82fd799-8826-47f8-8298-dc046a1ee9a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-228972d3-1d0f-49c4-a77a-1cf372b1b06d' class='xr-var-data-in' type='checkbox'><label for='data-228972d3-1d0f-49c4-a77a-1cf372b1b06d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eb74cd1f-afc9-4cfd-838c-fb74b336ca45' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eb74cd1f-afc9-4cfd-838c-fb74b336ca45' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b987abbf-def8-4fc7-a214-70d3cb466bee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b987abbf-def8-4fc7-a214-70d3cb466bee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32dc5f75-deb7-4567-975b-a173c78fdd51' class='xr-var-data-in' type='checkbox'><label for='data-32dc5f75-deb7-4567-975b-a173c78fdd51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>133</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>q</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>specific_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>q</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-32d4c1a0-13d0-4165-894e-62e9e7502ec2' class='xr-section-summary-in' type='checkbox'  ><label for='section-32d4c1a0-13d0-4165-894e-62e9e7502ec2' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-bcb68afa-6cee-4470-9b74-490eedd6e783' class='xr-index-data-in' type='checkbox'/><label for='index-bcb68afa-6cee-4470-9b74-490eedd6e783' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000],\n      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9f5a8cd2-1d86-42be-835f-991a3f63f15f' class='xr-index-data-in' type='checkbox'/><label for='index-9f5a8cd2-1d86-42be-835f-991a3f63f15f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-262fabf5-06b0-4e08-98d3-be04681c1a11' class='xr-section-summary-in' type='checkbox'  checked><label for='section-262fabf5-06b0-4e08-98d3-be04681c1a11' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
           },
           "metadata": {}
         }
@@ -180,11 +180,6 @@
         "trusted": true
       },
       "outputs": [
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Loading Cartopy, Jinja2, MarkupSafe, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cftime, cligj, cloudpickle, cycler, dask, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, importlib_metadata, jsonschema, jsonschema_specifications, kiwisolver, locket, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, partd, pdbufr, pint, pkgconfig, pyparsing, pyproj, pyrsistent, pyshp, pyyaml, rasterio, referencing, rpds-py, setuptools, shapely, toolz, tqdm, typing-extensions, zipp\n[pyodide]: Loaded Cartopy, Jinja2, MarkupSafe, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cftime, cligj, cloudpickle, cycler, dask, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, importlib_metadata, jsonschema, jsonschema_specifications, kiwisolver, locket, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, partd, pdbufr, pint, pkgconfig, pyparsing, pyproj, pyrsistent, pyshp, pyyaml, rasterio, referencing, rpds-py, setuptools, shapely, toolz, tqdm, typing-extensions, zipp\n",
-          "output_type": "stream"
-        },
         {
           "name": "stderr",
           "text": "/lib/python3.12/site-packages/earthkit/regrid/utils/caching.py:27: UserWarning: only soft file lock is available\n  from filelock import FileLock\n",
@@ -192,7 +187,7 @@
         },
         {
           "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 292.0MiB (from 202.8MiB) for this notebook\n",
+          "text": "[pyodide]: Memory usage has grown to 297.8MiB (from 206.8MiB) for this notebook\n",
           "output_type": "stream"
         }
       ],
@@ -219,7 +214,7 @@
         },
         {
           "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 794.0MiB (from 292.0MiB) for this notebook\n",
+          "text": "[pyodide]: Memory usage has grown to 812.9MiB (from 297.8MiB) for this notebook\n",
           "output_type": "stream"
         },
         {
@@ -236,7 +231,7 @@
     {
       "id": "7731f334-8487-4256-ac41-8b693a504b35",
       "cell_type": "markdown",
-      "source": "The following example will load GRIB files for pressure level data stored as **spherical harmonic coefficients (sh)**. Since this data requires remapping to an **O400 grid** for visualization, the remapping step is included.\n",
+      "source": "The following example will load GRIB files for pressure level data stored as **spherical harmonic coefficients (sh)**. Since these data require remapping to an **O400 grid** for visualization, the remapping step is included.\n",
       "metadata": {}
     },
     {
@@ -257,7 +252,7 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": "<xarray.Dataset> Size: 18GB\nDimensions:  (time: 21, level: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    d        (time, level, value) float64 2GB ...\n    r        (time, level, value) float64 2GB ...\n    t        (time, level, value) float64 2GB ...\n    u        (time, level, value) float64 2GB ...\n    v        (time, level, value) float64 2GB ...\n    vo       (time, level, value) float64 2GB ...\n    w        (time, level, value) float64 2GB ...\n    z        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 18GB\nDimensions:  (time: 21, level: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    d        (time, level, value) float64 2GB ...\n    r        (time, level, value) float64 2GB ...\n    t        (time, level, value) float64 2GB ...\n    u        (time, level, value) float64 2GB ...\n    v        (time, level, value) float64 2GB ...\n    vo       (time, level, value) float64 2GB ...\n    w        (time, level, value) float64 2GB ...\n    z        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-db64db46-3c06-4308-84e5-6810970ad240' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-db64db46-3c06-4308-84e5-6810970ad240' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span class='xr-has-index'>level</span>: 21</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e8e951c3-2f01-4f19-9951-fbbd27dda11c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e8e951c3-2f01-4f19-9951-fbbd27dda11c' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fd9f22cf-4eac-47f8-b601-88e62dbee548' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd9f22cf-4eac-47f8-b601-88e62dbee548' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18628bc9-5d29-4f93-8920-e13f14d576b7' class='xr-var-data-in' type='checkbox'><label for='data-18628bc9-5d29-4f93-8920-e13f14d576b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 500 700 850 925 1000</div><input id='attrs-0316f366-c93e-4230-a52a-55e2a3270ae8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0316f366-c93e-4230-a52a-55e2a3270ae8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b40b191-91aa-488c-b89c-a78f796d6718' class='xr-var-data-in' type='checkbox'><label for='data-8b40b191-91aa-488c-b89c-a78f796d6718' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-15699af3-45b5-4526-8644-d08c1c9dadf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15699af3-45b5-4526-8644-d08c1c9dadf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a7040c8-6166-45ae-897e-6ef459653f1e' class='xr-var-data-in' type='checkbox'><label for='data-7a7040c8-6166-45ae-897e-6ef459653f1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-fe2366e6-0112-42ef-b2cc-82098fb40e6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe2366e6-0112-42ef-b2cc-82098fb40e6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80bab319-1839-4777-9fa7-d2cd86ccb017' class='xr-var-data-in' type='checkbox'><label for='data-80bab319-1839-4777-9fa7-d2cd86ccb017' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c7279364-832d-44d6-9756-86e7c551732b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c7279364-832d-44d6-9756-86e7c551732b' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>d</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf1695e6-65ee-4406-9b51-5f1e6d48e8fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf1695e6-65ee-4406-9b51-5f1e6d48e8fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c774e0c-e7c4-4a15-8180-53be570471fa' class='xr-var-data-in' type='checkbox'><label for='data-0c774e0c-e7c4-4a15-8180-53be570471fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>155</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>d</dd><dt><span>GRIB_units :</span></dt><dd>s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Divergence</dd><dt><span>GRIB_cfName :</span></dt><dd>divergence_of_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s**-1</dd><dt><span>long_name :</span></dt><dd>Divergence</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b126f268-44b7-4446-ab86-41a6ae4a4792' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b126f268-44b7-4446-ab86-41a6ae4a4792' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c331bbd-0428-4c45-9e68-2bce044a80f5' class='xr-var-data-in' type='checkbox'><label for='data-0c331bbd-0428-4c45-9e68-2bce044a80f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>157</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>r</dd><dt><span>GRIB_units :</span></dt><dd>%</dd><dt><span>GRIB_name :</span></dt><dd>Relative humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>relative_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>r</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Relative humidity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49230fda-44d9-4bf7-b0a0-3f64fef8784f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49230fda-44d9-4bf7-b0a0-3f64fef8784f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1172b8db-3efe-4b98-873e-a31785ef2b72' class='xr-var-data-in' type='checkbox'><label for='data-1172b8db-3efe-4b98-873e-a31785ef2b72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>130</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Temperature</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9d75d8d-b736-4ebb-9c02-a5d5cf473a8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9d75d8d-b736-4ebb-9c02-a5d5cf473a8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1babf3e4-691e-4246-a9ae-64144bc60925' class='xr-var-data-in' type='checkbox'><label for='data-1babf3e4-691e-4246-a9ae-64144bc60925' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>131</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>u</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>U component of wind</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>U component of wind</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-12cd3613-e3f1-41b3-898b-12383807300f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12cd3613-e3f1-41b3-898b-12383807300f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61a711da-4f80-4b28-9f4a-0ae72a2733b7' class='xr-var-data-in' type='checkbox'><label for='data-61a711da-4f80-4b28-9f4a-0ae72a2733b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>132</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>v</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>V component of wind</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>V component of wind</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vo</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dbfd4f75-ac41-43db-ab8e-d1a7e51361a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbfd4f75-ac41-43db-ab8e-d1a7e51361a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9804b2b3-41e3-4a69-9ef8-68fc1032344d' class='xr-var-data-in' type='checkbox'><label for='data-9804b2b3-41e3-4a69-9ef8-68fc1032344d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>138</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vo</dd><dt><span>GRIB_units :</span></dt><dd>s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Vorticity (relative)</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_relative_vorticity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vo</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s**-1</dd><dt><span>long_name :</span></dt><dd>Vorticity (relative)</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0717440-277b-4d6c-8f3a-9bb73334dda3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0717440-277b-4d6c-8f3a-9bb73334dda3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8894c3b9-3856-45d2-b5aa-544dfbb9ea2a' class='xr-var-data-in' type='checkbox'><label for='data-8894c3b9-3856-45d2-b5aa-544dfbb9ea2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>135</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>w</dd><dt><span>GRIB_units :</span></dt><dd>Pa s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Vertical velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd><dt><span>GRIB_cfVarName :</span></dt><dd>w</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Pa s**-1</dd><dt><span>long_name :</span></dt><dd>Vertical velocity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-06d5b0d3-9487-4415-8cac-a5297f2c6e50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06d5b0d3-9487-4415-8cac-a5297f2c6e50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f999bc5-6b9f-4fcc-90a9-e6b46361b65e' class='xr-var-data-in' type='checkbox'><label for='data-2f999bc5-6b9f-4fcc-90a9-e6b46361b65e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 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912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>long_name :</span></dt><dd>Geopotential</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b4f42500-6f4b-448b-a9ff-67424319e662' class='xr-section-summary-in' type='checkbox'  ><label for='section-b4f42500-6f4b-448b-a9ff-67424319e662' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a41a635d-5f15-4ed0-9f06-5cf9341add00' class='xr-index-data-in' type='checkbox'/><label for='index-a41a635d-5f15-4ed0-9f06-5cf9341add00' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000],\n      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-306f042a-5159-4a9b-ad3a-52a490bb44a9' class='xr-index-data-in' type='checkbox'/><label for='index-306f042a-5159-4a9b-ad3a-52a490bb44a9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b750efdb-0568-4af9-ba6e-e5a4eeed59eb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b750efdb-0568-4af9-ba6e-e5a4eeed59eb' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 18GB\nDimensions:  (time: 21, level: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 168B 1 2 3 5 7 10 20 ... 300 400 500 700 850 925 1000\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    d        (time, level, value) float64 2GB ...\n    r        (time, level, value) float64 2GB ...\n    t        (time, level, value) float64 2GB ...\n    u        (time, level, value) float64 2GB ...\n    v        (time, level, value) float64 2GB ...\n    vo       (time, level, value) float64 2GB ...\n    w        (time, level, value) float64 2GB ...\n    z        (time, level, value) float64 2GB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-79e2b0d4-98f7-41fc-908b-f2c5996b318d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79e2b0d4-98f7-41fc-908b-f2c5996b318d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span class='xr-has-index'>level</span>: 21</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4dc9f710-f0ea-42ec-8512-8f528a180e14' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4dc9f710-f0ea-42ec-8512-8f528a180e14' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-11f5f07a-91e4-49e1-9288-e2e281067af1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11f5f07a-91e4-49e1-9288-e2e281067af1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83f3a1ae-c35b-4689-9bab-d5381942b18c' class='xr-var-data-in' type='checkbox'><label for='data-83f3a1ae-c35b-4689-9bab-d5381942b18c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 5 7 ... 500 700 850 925 1000</div><input id='attrs-ab237b31-1a4d-47c2-a018-ca3ec7c6d8ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab237b31-1a4d-47c2-a018-ca3ec7c6d8ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf1ae643-d2cd-4273-b3ce-afe00c541e80' class='xr-var-data-in' type='checkbox'><label for='data-bf1ae643-d2cd-4273-b3ce-afe00c541e80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6afe7089-57d2-43d7-881e-693cb99c9f68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6afe7089-57d2-43d7-881e-693cb99c9f68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfa8a556-8699-417c-a1d7-4dbcc4aabd4b' class='xr-var-data-in' type='checkbox'><label for='data-dfa8a556-8699-417c-a1d7-4dbcc4aabd4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-8412966e-21a4-44b1-b7ca-627175795fd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8412966e-21a4-44b1-b7ca-627175795fd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d632cc25-15cc-45cb-ad91-4f1da39a1155' class='xr-var-data-in' type='checkbox'><label for='data-d632cc25-15cc-45cb-ad91-4f1da39a1155' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-58bf1e38-dc66-4a17-8e41-5a7d9f53f638' class='xr-section-summary-in' type='checkbox'  checked><label for='section-58bf1e38-dc66-4a17-8e41-5a7d9f53f638' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>d</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f1990dbb-ddae-4ad6-b19c-290fe599b05f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1990dbb-ddae-4ad6-b19c-290fe599b05f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e49b35c5-a9bd-4885-a0cf-fc75d308cfdb' class='xr-var-data-in' type='checkbox'><label for='data-e49b35c5-a9bd-4885-a0cf-fc75d308cfdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>155</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>d</dd><dt><span>GRIB_units :</span></dt><dd>s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Divergence</dd><dt><span>GRIB_cfName :</span></dt><dd>divergence_of_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s**-1</dd><dt><span>long_name :</span></dt><dd>Divergence</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6bd736b1-ca53-4456-94d9-c55623c5e44f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bd736b1-ca53-4456-94d9-c55623c5e44f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d819103f-005f-4fd2-bddc-ca71027fcacd' class='xr-var-data-in' type='checkbox'><label for='data-d819103f-005f-4fd2-bddc-ca71027fcacd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>157</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>r</dd><dt><span>GRIB_units :</span></dt><dd>%</dd><dt><span>GRIB_name :</span></dt><dd>Relative humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>relative_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>r</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Relative humidity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3fb17866-8e88-4083-a329-97399c1924a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fb17866-8e88-4083-a329-97399c1924a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ea00266-5d8e-4c21-9ba6-7ae39a5db114' class='xr-var-data-in' type='checkbox'><label for='data-1ea00266-5d8e-4c21-9ba6-7ae39a5db114' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>130</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Temperature</dd><dt><span>GRIB_cfName :</span></dt><dd>air_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bfbc7714-576c-4c15-9b69-b094b9a8a02d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfbc7714-576c-4c15-9b69-b094b9a8a02d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba6d0fcd-72df-40b5-b4ab-556937cc9e77' class='xr-var-data-in' type='checkbox'><label for='data-ba6d0fcd-72df-40b5-b4ab-556937cc9e77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>131</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>u</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>U component of wind</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>U component of wind</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fba35e9b-d811-47c9-9085-dca1e22e4500' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fba35e9b-d811-47c9-9085-dca1e22e4500' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8d00e25-a6d9-4abc-be02-6afb765c7f01' class='xr-var-data-in' type='checkbox'><label for='data-b8d00e25-a6d9-4abc-be02-6afb765c7f01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>132</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>v</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>V component of wind</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_wind</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>V component of wind</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vo</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4a7eb612-2596-442e-a7bf-45aaad1ac06a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a7eb612-2596-442e-a7bf-45aaad1ac06a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a475595-3f9a-45d9-846c-0631061e1ddb' class='xr-var-data-in' type='checkbox'><label for='data-0a475595-3f9a-45d9-846c-0631061e1ddb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>138</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vo</dd><dt><span>GRIB_units :</span></dt><dd>s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Vorticity (relative)</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_relative_vorticity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vo</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s**-1</dd><dt><span>long_name :</span></dt><dd>Vorticity (relative)</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a4d76cdc-7915-45f9-93c2-c3b8a4a3fda3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4d76cdc-7915-45f9-93c2-c3b8a4a3fda3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c87de14-fcb7-490b-a7b2-199d70222f20' class='xr-var-data-in' type='checkbox'><label for='data-3c87de14-fcb7-490b-a7b2-199d70222f20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>135</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>w</dd><dt><span>GRIB_units :</span></dt><dd>Pa s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Vertical velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd><dt><span>GRIB_cfVarName :</span></dt><dd>w</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 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384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Pa s**-1</dd><dt><span>long_name :</span></dt><dd>Vertical velocity</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f93efcf0-9192-4f21-8b60-98f745e20b97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f93efcf0-9192-4f21-8b60-98f745e20b97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a3b09be-4feb-42b9-a037-4eb08ce4e30b' class='xr-var-data-in' type='checkbox'><label for='data-2a3b09be-4feb-42b9-a037-4eb08ce4e30b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>isobaricInhPa</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 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112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>long_name :</span></dt><dd>Geopotential</dd></dl></div><div class='xr-var-data'><pre>[288590400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-12cb2b78-ba48-4c07-a139-45de2fcc5f07' class='xr-section-summary-in' type='checkbox'  ><label for='section-12cb2b78-ba48-4c07-a139-45de2fcc5f07' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-36eea009-7a1d-4583-9a92-84b7a51d0ce9' class='xr-index-data-in' type='checkbox'/><label for='index-36eea009-7a1d-4583-9a92-84b7a51d0ce9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  150,\n        200,  250,  300,  400,  500,  700,  850,  925, 1000],\n      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-848ab9db-f9cb-4813-aa15-81469c876f8f' class='xr-index-data-in' type='checkbox'/><label for='index-848ab9db-f9cb-4813-aa15-81469c876f8f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76086a99-8123-495f-8afe-d154d0f1da9e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-76086a99-8123-495f-8afe-d154d0f1da9e' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
           },
           "metadata": {}
         }
@@ -278,6 +273,11 @@
         "trusted": true
       },
       "outputs": [
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 927.7MiB (from 812.9MiB) for this notebook\n",
+          "output_type": "stream"
+        },
         {
           "output_type": "display_data",
           "data": {
@@ -292,7 +292,7 @@
     {
       "id": "92cc1ccd-bb9b-48f2-81ab-9641c75d84ec",
       "cell_type": "markdown",
-      "source": "The model level data stored on a **reduced_gg** grid is split into two files. Specifying a timestep of `240` ensures that the second part of the data is loaded, covering the time range from **2020-07-26 12:00:00 UTC** to **2020-07-31 00:00:00 UTC**.",
+      "source": "The model level data stored on a **reduced_gg** grid is split into two files. Specifying a timestep of `240` ensures that the second part of the data are loaded, covering the time range from **2020-07-26 12:00:00 UTC** to **2020-07-31 00:00:00 UTC**.",
       "metadata": {}
     },
     {
@@ -313,7 +313,7 @@
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 50GB\nDimensions:  (time: 10, level: 137, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 1kB 1 2 3 4 5 6 7 8 ... 131 132 133 134 135 136 137\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 80B 2020-07-26T12:00:00 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    cc       (time, level, value) float64 7GB ...\n    ciwc     (time, level, value) float64 7GB ...\n    clwc     (time, level, value) float64 7GB ...\n    crwc     (time, level, value) float64 7GB ...\n    cswc     (time, level, value) float64 7GB ...\n    o3       (time, level, value) float64 7GB ...\n    q        (time, level, value) float64 7GB ...\nAttributes:\n    GRIB_edition:            2\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8a3a7295-c7aa-4117-89c2-c40541edb524' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8a3a7295-c7aa-4117-89c2-c40541edb524' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10</li><li><span class='xr-has-index'>level</span>: 137</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-484a3f40-c9ec-4019-b8ec-929def15123f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-484a3f40-c9ec-4019-b8ec-929def15123f' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf49fd14-64eb-4a9e-8576-1c8d5eb3a288' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf49fd14-64eb-4a9e-8576-1c8d5eb3a288' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3112f5e8-aa9b-4a4e-9ce5-844d915539bf' class='xr-var-data-in' type='checkbox'><label for='data-3112f5e8-aa9b-4a4e-9ce5-844d915539bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 133 134 135 136 137</div><input id='attrs-3cccdf5a-9787-4708-aff5-7508d46f01b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3cccdf5a-9787-4708-aff5-7508d46f01b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbce50f4-03e1-4f26-88c7-c710559a4797' class='xr-var-data-in' type='checkbox'><label for='data-bbce50f4-03e1-4f26-88c7-c710559a4797' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,\n        29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,\n        43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,\n        57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,\n        71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,\n        85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,\n        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,\n       113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,\n       127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed488c65-3609-41a5-9ee4-60945620cc42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed488c65-3609-41a5-9ee4-60945620cc42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89a47b2f-b5cb-4857-8e70-aba07a0744fe' class='xr-var-data-in' type='checkbox'><label for='data-89a47b2f-b5cb-4857-8e70-aba07a0744fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-26T12:00:00 ... 2020-07-31</div><input id='attrs-6602d500-33cc-4d46-ad97-5535ccb76107' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6602d500-33cc-4d46-ad97-5535ccb76107' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d177e2b-b059-49b7-93dd-b1406060a4b1' class='xr-var-data-in' type='checkbox'><label for='data-5d177e2b-b059-49b7-93dd-b1406060a4b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-26T12:00:00.000000000&#x27;, &#x27;2020-07-27T00:00:00.000000000&#x27;,\n       &#x27;2020-07-27T12:00:00.000000000&#x27;, &#x27;2020-07-28T00:00:00.000000000&#x27;,\n       &#x27;2020-07-28T12:00:00.000000000&#x27;, &#x27;2020-07-29T00:00:00.000000000&#x27;,\n       &#x27;2020-07-29T12:00:00.000000000&#x27;, &#x27;2020-07-30T00:00:00.000000000&#x27;,\n       &#x27;2020-07-30T12:00:00.000000000&#x27;, &#x27;2020-07-31T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc637be7-e346-49d8-9df8-2438237ed460' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc637be7-e346-49d8-9df8-2438237ed460' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5ee603c-36e5-4a9b-aaf8-583f520a87f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5ee603c-36e5-4a9b-aaf8-583f520a87f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fe67be5-263c-431c-9d0d-9d371135171f' class='xr-var-data-in' type='checkbox'><label for='data-7fe67be5-263c-431c-9d0d-9d371135171f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>248</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Fraction of cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Fraction of cloud cover</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ciwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f72074a6-624b-49cc-b087-22fb23723176' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f72074a6-624b-49cc-b087-22fb23723176' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d63e1b72-20be-435f-b7d8-5700ba0f95d1' class='xr-var-data-in' type='checkbox'><label for='data-d63e1b72-20be-435f-b7d8-5700ba0f95d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>247</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ciwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific cloud ice water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ciwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific cloud ice water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d561f8a4-0109-47e1-babb-61ea8ab71ba9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d561f8a4-0109-47e1-babb-61ea8ab71ba9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-685d23d8-44c8-4699-8c05-0459cdcfcd61' class='xr-var-data-in' type='checkbox'><label for='data-685d23d8-44c8-4699-8c05-0459cdcfcd61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>246</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>clwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific cloud liquid water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>clwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific cloud liquid water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-109c0950-6b98-4802-be8c-398c2809e3e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-109c0950-6b98-4802-be8c-398c2809e3e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-206a2e2a-9624-4271-a438-c7f1f8a752d3' class='xr-var-data-in' type='checkbox'><label for='data-206a2e2a-9624-4271-a438-c7f1f8a752d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>75</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>crwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific rain water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>crwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific rain water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cswc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ea208c8f-fc75-4b07-ac70-85661659267f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea208c8f-fc75-4b07-ac70-85661659267f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37c7d949-e1c7-4ef2-b25f-a434751fcbad' class='xr-var-data-in' type='checkbox'><label for='data-37c7d949-e1c7-4ef2-b25f-a434751fcbad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>76</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cswc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific snow water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cswc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific snow water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-05232d2a-7bc8-4544-8e86-a9aa497c9a38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05232d2a-7bc8-4544-8e86-a9aa497c9a38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d12d5490-4941-4fb4-b43b-87ca3dd8d4cc' class='xr-var-data-in' type='checkbox'><label for='data-d12d5490-4941-4fb4-b43b-87ca3dd8d4cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>203</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>o3</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Ozone mass mixing ratio</dd><dt><span>GRIB_cfName :</span></dt><dd>mass_fraction_of_ozone_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>o3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Ozone mass mixing ratio</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-017c8ef2-2f45-4dfb-985f-361e88e911bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-017c8ef2-2f45-4dfb-985f-361e88e911bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fafad87-73db-4632-911e-dc22098f97ca' class='xr-var-data-in' type='checkbox'><label for='data-4fafad87-73db-4632-911e-dc22098f97ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>133</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>q</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>specific_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>q</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 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1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3e8569e-5240-4c81-8a99-06194a8681c9' class='xr-section-summary-in' type='checkbox'  ><label for='section-e3e8569e-5240-4c81-8a99-06194a8681c9' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0ff12cfc-6cbd-4de9-9f73-4f1c9f7d0cb3' class='xr-index-data-in' type='checkbox'/><label for='index-0ff12cfc-6cbd-4de9-9f73-4f1c9f7d0cb3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n       ...\n       128, 129, 130, 131, 132, 133, 134, 135, 136, 137],\n      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;, length=137))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-51b6774b-5689-44d5-9caf-c0678274d5fc' class='xr-index-data-in' type='checkbox'/><label for='index-51b6774b-5689-44d5-9caf-c0678274d5fc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-26 12:00:00&#x27;, &#x27;2020-07-27 00:00:00&#x27;,\n               &#x27;2020-07-27 12:00:00&#x27;, &#x27;2020-07-28 00:00:00&#x27;,\n               &#x27;2020-07-28 12:00:00&#x27;, &#x27;2020-07-29 00:00:00&#x27;,\n               &#x27;2020-07-29 12:00:00&#x27;, &#x27;2020-07-30 00:00:00&#x27;,\n               &#x27;2020-07-30 12:00:00&#x27;, &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d3d30fa-5bd8-4f54-8f22-3c70fefb7b0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6d3d30fa-5bd8-4f54-8f22-3c70fefb7b0c' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 50GB\nDimensions:  (time: 10, level: 137, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n  * level    (level) int64 1kB 1 2 3 4 5 6 7 8 ... 131 132 133 134 135 136 137\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 80B 2020-07-26T12:00:00 ... 2020-07-31\nDimensions without coordinates: value\nData variables:\n    cc       (time, level, value) float64 7GB ...\n    ciwc     (time, level, value) float64 7GB ...\n    clwc     (time, level, value) float64 7GB ...\n    crwc     (time, level, value) float64 7GB ...\n    cswc     (time, level, value) float64 7GB ...\n    o3       (time, level, value) float64 7GB ...\n    q        (time, level, value) float64 7GB ...\nAttributes:\n    GRIB_edition:            2\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-20ee11ed-077c-4ba0-abcf-1a13626fb795' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-20ee11ed-077c-4ba0-abcf-1a13626fb795' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10</li><li><span class='xr-has-index'>level</span>: 137</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1ac52728-6d62-4728-b9a8-7848c7dada2e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1ac52728-6d62-4728-b9a8-7848c7dada2e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-233ae07f-dbd6-422a-8134-ee57615c99b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-233ae07f-dbd6-422a-8134-ee57615c99b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01b5f8da-1a96-49bb-91b3-7fb340250a56' class='xr-var-data-in' type='checkbox'><label for='data-01b5f8da-1a96-49bb-91b3-7fb340250a56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 133 134 135 136 137</div><input id='attrs-dbe3af5c-cf18-4097-8838-996b33e0575c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dbe3af5c-cf18-4097-8838-996b33e0575c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62902961-a073-4d86-8c74-b239a7e42d7f' class='xr-var-data-in' type='checkbox'><label for='data-62902961-a073-4d86-8c74-b239a7e42d7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,  14,\n        15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,  28,\n        29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,  42,\n        43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,  56,\n        57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,  70,\n        71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,  84,\n        85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,  98,\n        99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,\n       113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,\n       127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-65161dc5-e04d-4fed-a448-a468d2c9f6fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65161dc5-e04d-4fed-a448-a468d2c9f6fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f63ad58-001c-46b7-819d-9ff1fcd41e9f' class='xr-var-data-in' type='checkbox'><label for='data-8f63ad58-001c-46b7-819d-9ff1fcd41e9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-26T12:00:00 ... 2020-07-31</div><input id='attrs-9d41c56b-c238-4f4a-ab4c-be704c72846b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d41c56b-c238-4f4a-ab4c-be704c72846b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0875b3e-7077-4d5d-b6c5-7a7bed99a439' class='xr-var-data-in' type='checkbox'><label for='data-f0875b3e-7077-4d5d-b6c5-7a7bed99a439' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-26T12:00:00.000000000&#x27;, &#x27;2020-07-27T00:00:00.000000000&#x27;,\n       &#x27;2020-07-27T12:00:00.000000000&#x27;, &#x27;2020-07-28T00:00:00.000000000&#x27;,\n       &#x27;2020-07-28T12:00:00.000000000&#x27;, &#x27;2020-07-29T00:00:00.000000000&#x27;,\n       &#x27;2020-07-29T12:00:00.000000000&#x27;, &#x27;2020-07-30T00:00:00.000000000&#x27;,\n       &#x27;2020-07-30T12:00:00.000000000&#x27;, &#x27;2020-07-31T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ad7f76e7-ca3c-4de6-bce6-7e1afbb2837d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad7f76e7-ca3c-4de6-bce6-7e1afbb2837d' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-22b0fb78-f348-4153-a091-a2ea9c41b0cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22b0fb78-f348-4153-a091-a2ea9c41b0cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eaa62873-1cf3-4e82-82b5-bbaaada5f034' class='xr-var-data-in' type='checkbox'><label for='data-eaa62873-1cf3-4e82-82b5-bbaaada5f034' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>248</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Fraction of cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Fraction of cloud cover</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ciwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03ab93e1-0769-46d2-b2b2-92afb876b3c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03ab93e1-0769-46d2-b2b2-92afb876b3c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40a69f1b-20d7-4695-a351-699f74173db2' class='xr-var-data-in' type='checkbox'><label for='data-40a69f1b-20d7-4695-a351-699f74173db2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>247</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ciwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific cloud ice water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ciwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific cloud ice water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c22be1a-84f3-4eb5-8242-e8fe9d65e124' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c22be1a-84f3-4eb5-8242-e8fe9d65e124' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9423d0c2-cc38-47a9-aa46-177621491fb8' class='xr-var-data-in' type='checkbox'><label for='data-9423d0c2-cc38-47a9-aa46-177621491fb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>246</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>clwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific cloud liquid water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>clwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 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1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific cloud liquid water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crwc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-10919800-f902-42f3-be3a-3177eeeff683' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10919800-f902-42f3-be3a-3177eeeff683' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77f2d46a-67a1-46bd-9eec-1e6577d633df' class='xr-var-data-in' type='checkbox'><label for='data-77f2d46a-67a1-46bd-9eec-1e6577d633df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>75</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>crwc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific rain water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>crwc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific rain water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cswc</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eac812d0-7f4e-4747-a34a-ded59e974553' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eac812d0-7f4e-4747-a34a-ded59e974553' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ac134c7-49f7-4f1f-8051-b10479cddb17' class='xr-var-data-in' type='checkbox'><label for='data-7ac134c7-49f7-4f1f-8051-b10479cddb17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>76</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cswc</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific snow water content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cswc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific snow water content</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f429b82e-00d2-4ad5-b09c-0af71f9d9ebb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f429b82e-00d2-4ad5-b09c-0af71f9d9ebb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2201d6f4-6c08-4d57-8234-269506d2db65' class='xr-var-data-in' type='checkbox'><label for='data-2201d6f4-6c08-4d57-8234-269506d2db65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>203</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>o3</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Ozone mass mixing ratio</dd><dt><span>GRIB_cfName :</span></dt><dd>mass_fraction_of_ozone_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>o3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Ozone mass mixing ratio</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, level, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9fd406e-b1a0-4f8d-8747-d3e3f102305c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9fd406e-b1a0-4f8d-8747-d3e3f102305c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ea68f6a-2dea-4de5-98c1-d994c7ae509d' class='xr-var-data-in' type='checkbox'><label for='data-8ea68f6a-2dea-4de5-98c1-d994c7ae509d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>133</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>hybrid</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>q</dd><dt><span>GRIB_units :</span></dt><dd>kg kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Specific humidity</dd><dt><span>GRIB_cfName :</span></dt><dd>specific_humidity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>q</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>276</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 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380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd></dl></div><div class='xr-var-data'><pre>[896528000 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-21bd5f72-d77f-4320-9bae-2ffece2ce37a' class='xr-section-summary-in' type='checkbox'  ><label for='section-21bd5f72-d77f-4320-9bae-2ffece2ce37a' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-5c4062a1-2a3f-4923-9c69-cd7e717d248d' class='xr-index-data-in' type='checkbox'/><label for='index-5c4062a1-2a3f-4923-9c69-cd7e717d248d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n       ...\n       128, 129, 130, 131, 132, 133, 134, 135, 136, 137],\n      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;, length=137))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-88504932-262b-4010-90f7-256622264b4a' class='xr-index-data-in' type='checkbox'/><label for='index-88504932-262b-4010-90f7-256622264b4a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-26 12:00:00&#x27;, &#x27;2020-07-27 00:00:00&#x27;,\n               &#x27;2020-07-27 12:00:00&#x27;, &#x27;2020-07-28 00:00:00&#x27;,\n               &#x27;2020-07-28 12:00:00&#x27;, &#x27;2020-07-29 00:00:00&#x27;,\n               &#x27;2020-07-29 12:00:00&#x27;, &#x27;2020-07-30 00:00:00&#x27;,\n               &#x27;2020-07-30 12:00:00&#x27;, &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-702476c3-11e2-437b-81c4-1efdb9c5bc5a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-702476c3-11e2-437b-81c4-1efdb9c5bc5a' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
           },
           "metadata": {}
         }
@@ -334,11 +334,6 @@
         "trusted": true
       },
       "outputs": [
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 892.6MiB (from 794.0MiB) for this notebook\n",
-          "output_type": "stream"
-        },
         {
           "output_type": "display_data",
           "data": {
@@ -353,7 +348,7 @@
     {
       "id": "0320963f-0d01-4ee8-9cd4-d2aa765ebac6",
       "cell_type": "markdown",
-      "source": "The surface level data is stored on a **reduced_gg** grid and can be loaded as in the following example.",
+      "source": "The surface level data are stored on a **reduced_gg** grid and can be loaded as in the following example.",
       "metadata": {}
     },
     {
@@ -374,7 +369,7 @@
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 11GB\nDimensions:  (time: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables: (12/101)\n    10fg     (time, value) float64 110MB ...\n    10u      (time, value) float64 110MB ...\n    10v      (time, value) float64 110MB ...\n    2d       (time, value) float64 110MB ...\n    2t       (time, value) float64 110MB ...\n    asn      (time, value) float64 110MB ...\n    ...       ...\n    ttr      (time, value) float64 110MB ...\n    ttrc     (time, value) float64 110MB ...\n    uvb      (time, value) float64 110MB ...\n    vimd     (time, value) float64 110MB ...\n    vis      (time, value) float64 110MB ...\n    z        (time, value) float64 110MB ...\nAttributes:\n    GRIB_edition:            2\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-af0b84c6-4690-4453-82a1-cd7a21be6c33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-af0b84c6-4690-4453-82a1-cd7a21be6c33' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e15a82b2-279a-432f-98b8-9a1d483d1fed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e15a82b2-279a-432f-98b8-9a1d483d1fed' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6e525395-3616-4e49-b9e4-a5cc45d1a793' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e525395-3616-4e49-b9e4-a5cc45d1a793' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-468911d2-f019-48be-b774-27babaa61404' class='xr-var-data-in' type='checkbox'><label for='data-468911d2-f019-48be-b774-27babaa61404' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-146465b6-25bf-4034-9121-88f49bb09bc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-146465b6-25bf-4034-9121-88f49bb09bc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2d141b7-4e86-4488-a796-dd530427a283' class='xr-var-data-in' type='checkbox'><label for='data-c2d141b7-4e86-4488-a796-dd530427a283' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-09be4c47-8b66-4f33-a583-3f467b675263' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09be4c47-8b66-4f33-a583-3f467b675263' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71deb6f2-28b0-4e39-b21a-3fd21baa4cc6' class='xr-var-data-in' type='checkbox'><label for='data-71deb6f2-28b0-4e39-b21a-3fd21baa4cc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fae185ab-33aa-4017-9fe9-896e1e92880c' class='xr-section-summary-in' type='checkbox'  ><label for='section-fae185ab-33aa-4017-9fe9-896e1e92880c' class='xr-section-summary' >Data variables: <span>(101)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10fg</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9bf62ce5-0b45-46ad-93f0-8317311e8cb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bf62ce5-0b45-46ad-93f0-8317311e8cb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dff66f8a-8e7c-4033-adf1-0ccd597f5f63' class='xr-var-data-in' type='checkbox'><label for='data-dff66f8a-8e7c-4033-adf1-0ccd597f5f63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>49</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10fg</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Maximum 10 metre wind gust since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fg10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 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1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Maximum 10 metre wind gust since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-22b3c579-e4b5-4879-ba97-2c19acd39571' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22b3c579-e4b5-4879-ba97-2c19acd39571' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31c9c467-91e8-45b3-ba56-f0226589c80b' class='xr-var-data-in' type='checkbox'><label for='data-31c9c467-91e8-45b3-ba56-f0226589c80b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>165</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10u</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre U wind component</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6a1bdd14-6e02-4741-bfc6-55d96909ac67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a1bdd14-6e02-4741-bfc6-55d96909ac67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adcac85d-05b4-491c-9934-aa41ab39ef95' class='xr-var-data-in' type='checkbox'><label for='data-adcac85d-05b4-491c-9934-aa41ab39ef95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>166</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10v</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre V wind component</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a0b83988-0d1f-455d-ae81-bb863eb42a63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0b83988-0d1f-455d-ae81-bb863eb42a63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f7ca0db-b55e-409e-8880-324a139339a8' class='xr-var-data-in' type='checkbox'><label for='data-4f7ca0db-b55e-409e-8880-324a139339a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-52daccda-3a23-46f1-b66a-2a2c0662da06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52daccda-3a23-46f1-b66a-2a2c0662da06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff527b16-27a2-419b-96d6-500207a660d0' class='xr-var-data-in' type='checkbox'><label for='data-ff527b16-27a2-419b-96d6-500207a660d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>asn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0f39135-59f4-4f2b-a6ed-8204f8f1c737' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0f39135-59f4-4f2b-a6ed-8204f8f1c737' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcf4938a-e4d7-4408-9702-19bc92613ed2' class='xr-var-data-in' type='checkbox'><label for='data-bcf4938a-e4d7-4408-9702-19bc92613ed2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>32</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>asn</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Snow albedo</dd><dt><span>GRIB_cfVarName :</span></dt><dd>asn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Snow albedo</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bld</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-512d3ec5-ea89-46f7-b549-2724c4e03770' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-512d3ec5-ea89-46f7-b549-2724c4e03770' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cff59088-0171-44f5-af1e-a54a132b7256' class='xr-var-data-in' type='checkbox'><label for='data-cff59088-0171-44f5-af1e-a54a132b7256' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>145</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>bld</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated boundary layer dissipation</dd><dt><span>GRIB_cfName :</span></dt><dd>kinetic_energy_dissipation_in_atmosphere_boundary_layer</dd><dt><span>GRIB_cfVarName :</span></dt><dd>bld</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated boundary layer dissipation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blh</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a9efe709-7ecc-41d3-94d5-764846b728d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9efe709-7ecc-41d3-94d5-764846b728d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3312b4f-c393-4c4d-92ca-415a24ce806b' class='xr-var-data-in' type='checkbox'><label for='data-e3312b4f-c393-4c4d-92ca-415a24ce806b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>159</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>blh</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Boundary layer height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>blh</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Boundary layer height</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cape</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3ae7b03-ad5e-4dda-be05-d111f6d8e57d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3ae7b03-ad5e-4dda-be05-d111f6d8e57d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-319e1d29-e506-43d8-a9be-1b8ce43fae7f' class='xr-var-data-in' type='checkbox'><label for='data-319e1d29-e506-43d8-a9be-1b8ce43fae7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>59</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cape</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective available potential energy</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cape</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective available potential energy</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chnk</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dde54a6f-06f0-48a3-93b1-04f71241b3b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dde54a6f-06f0-48a3-93b1-04f71241b3b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f26fe835-c7c1-44f7-a1f0-9f0c574bc77f' class='xr-var-data-in' type='checkbox'><label for='data-f26fe835-c7c1-44f7-a1f0-9f0c574bc77f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>148</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>chnk</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Charnock</dd><dt><span>GRIB_cfVarName :</span></dt><dd>chnk</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Charnock</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ci</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2fd17f42-0931-45ce-9a94-f5a5e900f54e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fd17f42-0931-45ce-9a94-f5a5e900f54e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-163c2443-64fe-4584-bb6b-e4b237f1ae6d' class='xr-var-data-in' type='checkbox'><label for='data-163c2443-64fe-4584-bb6b-e4b237f1ae6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>31</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ci</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>GRIB_cfName :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>siconc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cin</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-06ccb102-1256-4fe2-8bd0-eb5228b5f093' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06ccb102-1256-4fe2-8bd0-eb5228b5f093' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe59fb00-11de-480b-a9c6-a46f7a25f24d' class='xr-var-data-in' type='checkbox'><label for='data-fe59fb00-11de-480b-a9c6-a46f7a25f24d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228001</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cin</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective inhibition</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cin</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective inhibition</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a9081aca-f6c7-492d-b961-d81ccdae223b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9081aca-f6c7-492d-b961-d81ccdae223b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81197e59-51e0-41bc-ade6-7f171cec7ea2' class='xr-var-data-in' type='checkbox'><label for='data-81197e59-51e0-41bc-ade6-7f171cec7ea2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>143</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Convective precipitation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Convective precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-02b92619-1f42-4734-bff6-a8f8d1edd805' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02b92619-1f42-4734-bff6-a8f8d1edd805' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3af131e-484c-4a24-839d-57e503886af7' class='xr-var-data-in' type='checkbox'><label for='data-a3af131e-484c-4a24-839d-57e503886af7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228218</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>crr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective rain rate</dd><dt><span>GRIB_cfVarName :</span></dt><dd>crr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Convective rain rate</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>csfr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ffd0b5d-008d-40e8-9c22-027df91687e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ffd0b5d-008d-40e8-9c22-027df91687e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ae6f47c-98c5-4dad-99f3-d4135fe3939f' class='xr-var-data-in' type='checkbox'><label for='data-4ae6f47c-98c5-4dad-99f3-d4135fe3939f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228220</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>csfr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective snowfall rate water equivalent</dd><dt><span>GRIB_cfVarName :</span></dt><dd>csfr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Convective snowfall rate water equivalent</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dsrp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aefb4ed7-3098-4277-b927-1ee92582fdda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aefb4ed7-3098-4277-b927-1ee92582fdda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b3a42a4-ed0b-439b-ade2-c4fcfe389f80' class='xr-var-data-in' type='checkbox'><label for='data-2b3a42a4-ed0b-439b-ade2-c4fcfe389f80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>47</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>dsrp</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface direct normal short-wave (solar) radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dsrp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface direct normal short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dc722e7a-0ff6-4521-8f38-b0d5ee179c7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc722e7a-0ff6-4521-8f38-b0d5ee179c7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9852e538-5133-4d40-b8aa-d5d3e165b868' class='xr-var-data-in' type='checkbox'><label for='data-9852e538-5133-4d40-b8aa-d5d3e165b868' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>182</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>e</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Evaporation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_water_evaporation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>e</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2e9348f2-3097-4100-b941-0597784b77fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e9348f2-3097-4100-b941-0597784b77fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fe2c44b-0a4f-4c62-ba4b-7513b6155d97' class='xr-var-data-in' type='checkbox'><label for='data-0fe2c44b-0a4f-4c62-ba4b-7513b6155d97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>44</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>es</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow evaporation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>es</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 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1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snow evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ddb6319-0002-4586-8225-04a1c5a6e17d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ddb6319-0002-4586-8225-04a1c5a6e17d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6f8f2e9-18f1-484e-b3d3-f88ab354fdba' class='xr-var-data-in' type='checkbox'><label for='data-b6f8f2e9-18f1-484e-b3d3-f88ab354fdba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>180</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ewss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated eastward turbulent surface stress</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downward_eastward_stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ewss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 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1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Time-integrated eastward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fal</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d8b8dc36-d7ee-4c11-9dbd-07fef0e63d29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8b8dc36-d7ee-4c11-9dbd-07fef0e63d29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acef5167-c2bf-4a40-92f1-32c7b2acc116' class='xr-var-data-in' type='checkbox'><label for='data-acef5167-c2bf-4a40-92f1-32c7b2acc116' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>243</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fal</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Forecast albedo</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fal</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Forecast albedo</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-10df4cb3-09af-429e-a4ca-f806dfdd8d37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10df4cb3-09af-429e-a4ca-f806dfdd8d37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b7291a1-2846-46ee-9cc9-53ba90e24cd5' class='xr-var-data-in' type='checkbox'><label for='data-7b7291a1-2846-46ee-9cc9-53ba90e24cd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>245</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>flsr</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Forecast logarithm of surface roughness for heat</dd><dt><span>GRIB_cfVarName :</span></dt><dd>flsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Forecast logarithm of surface roughness for heat</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-abd23b65-1dba-48df-80a5-ab309709d5cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abd23b65-1dba-48df-80a5-ab309709d5cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29be5a78-3e3f-434d-b304-0ec21776e87a' class='xr-var-data-in' type='checkbox'><label for='data-29be5a78-3e3f-434d-b304-0ec21776e87a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>244</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fsr</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Forecast surface roughness</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Forecast surface roughness</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fzra</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eaa959a1-166d-4128-b23f-9f94f5d64c17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eaa959a1-166d-4128-b23f-9f94f5d64c17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de74c275-733c-4648-b5fe-d7deedc73e46' class='xr-var-data-in' type='checkbox'><label for='data-de74c275-733c-4648-b5fe-d7deedc73e46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228216</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fzra</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Accumulated freezing rain</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fzra</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Accumulated freezing rain</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gwd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b2e36c05-c2a4-43d1-9810-93eabd4ca6d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2e36c05-c2a4-43d1-9810-93eabd4ca6d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e778ab40-cf4f-4f8b-9804-3ec964b331f2' class='xr-var-data-in' type='checkbox'><label for='data-e778ab40-cf4f-4f8b-9804-3ec964b331f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>197</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>gwd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Gravity wave dissipation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>gwd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Gravity wave dissipation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1bae40ae-dffc-4c06-b58b-d43944ebc26c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bae40ae-dffc-4c06-b58b-d43944ebc26c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6324388-c8c4-464e-83dc-5e6dc1ebaed5' class='xr-var-data-in' type='checkbox'><label for='data-b6324388-c8c4-464e-83dc-5e6dc1ebaed5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>188</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>hcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>High cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>High cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>i10fg</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f3e74420-ec5f-4a8e-bf52-4706d325f508' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3e74420-ec5f-4a8e-bf52-4706d325f508' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57854756-4ab8-4a5c-8117-a560c9b52ee4' class='xr-var-data-in' type='checkbox'><label for='data-57854756-4ab8-4a5c-8117-a560c9b52ee4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228029</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>i10fg</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous 10 metre wind gust</dd><dt><span>GRIB_cfVarName :</span></dt><dd>i10fg</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Instantaneous 10 metre wind gust</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ie</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bb51bd9c-8a60-4dd6-8d56-d16033ff017e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb51bd9c-8a60-4dd6-8d56-d16033ff017e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18de1af7-fcb9-45ad-8b24-dd1a0c6e857f' class='xr-var-data-in' type='checkbox'><label for='data-18de1af7-fcb9-45ad-8b24-dd1a0c6e857f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>232</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ie</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous moisture flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ie</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Instantaneous moisture flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iews</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71cc9741-b711-404c-b588-0870b6062504' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71cc9741-b711-404c-b588-0870b6062504' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b90e40f3-8071-47d7-a14a-5ffe2d6fa10b' class='xr-var-data-in' type='checkbox'><label for='data-b90e40f3-8071-47d7-a14a-5ffe2d6fa10b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>229</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>iews</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous eastward turbulent surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>iews</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous eastward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ilspf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8a99e117-a1c2-48d6-bba8-18bcb5a53d1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a99e117-a1c2-48d6-bba8-18bcb5a53d1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b4b8950-3f46-4821-bf06-580d3b704e68' class='xr-var-data-in' type='checkbox'><label for='data-9b4b8950-3f46-4821-bf06-580d3b704e68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228217</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ilspf</dd><dt><span>GRIB_units :</span></dt><dd>Proportion</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous large-scale precipitation fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ilspf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Proportion</dd><dt><span>long_name :</span></dt><dd>Instantaneous large-scale precipitation fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3298d974-109e-4c1b-8c81-9b14b75a7161' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3298d974-109e-4c1b-8c81-9b14b75a7161' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5520e12-44ec-4c22-b6eb-265d589a9269' class='xr-var-data-in' type='checkbox'><label for='data-a5520e12-44ec-4c22-b6eb-265d589a9269' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>230</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>inss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous northward turbulent surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>inss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous northward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ishf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a379f0f4-7ece-4c70-a007-ed0373fd5c8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a379f0f4-7ece-4c70-a007-ed0373fd5c8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6025ae77-ca86-440a-b450-2394923b70e6' class='xr-var-data-in' type='checkbox'><label for='data-6025ae77-ca86-440a-b450-2394923b70e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>231</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ishf</dd><dt><span>GRIB_units :</span></dt><dd>W m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous surface sensible heat net flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ishf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>W m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous surface sensible heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1048ea07-f239-450c-8263-8f8ccf97ae6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1048ea07-f239-450c-8263-8f8ccf97ae6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ff0a7a9-ef74-4f8c-8603-5e9d8306c7bb' class='xr-var-data-in' type='checkbox'><label for='data-7ff0a7a9-ef74-4f8c-8603-5e9d8306c7bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>35</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl1</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 1</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2d1c441d-134c-43d3-a4bc-9a066fccaf1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d1c441d-134c-43d3-a4bc-9a066fccaf1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea89a450-97fb-414c-a6db-81d090d7ccd6' class='xr-var-data-in' type='checkbox'><label for='data-ea89a450-97fb-414c-a6db-81d090d7ccd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>36</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl2</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b4d6aa3c-952e-48e9-b996-afe3af1b5124' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4d6aa3c-952e-48e9-b996-afe3af1b5124' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afe591d7-3ba0-440e-9007-31f9fac20f90' class='xr-var-data-in' type='checkbox'><label for='data-afe591d7-3ba0-440e-9007-31f9fac20f90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>37</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl3</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-364e35bd-175f-4bbb-ab35-4281f824e0ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-364e35bd-175f-4bbb-ab35-4281f824e0ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4664d51a-5611-4732-bdf8-0f07e53366d1' class='xr-var-data-in' type='checkbox'><label for='data-4664d51a-5611-4732-bdf8-0f07e53366d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>38</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl4</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 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960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kx</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b45b364-fff9-46d3-b1e6-0443e67a3ec1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b45b364-fff9-46d3-b1e6-0443e67a3ec1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8d01c9e-d113-4925-aba2-df6a48771cf7' class='xr-var-data-in' type='checkbox'><label for='data-a8d01c9e-d113-4925-aba2-df6a48771cf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260121</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>kx</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>K index</dd><dt><span>GRIB_cfVarName :</span></dt><dd>kx</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>K index</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0207716b-9791-48b7-83e5-b8ceb4f84ea3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0207716b-9791-48b7-83e5-b8ceb4f84ea3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ceb0746b-aa98-49e2-a4b1-8caed48c34ba' class='xr-var-data-in' type='checkbox'><label for='data-ceb0746b-aa98-49e2-a4b1-8caed48c34ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>186</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Low cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Low cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lgws</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-61b0fe19-d2cc-4e1b-9722-1eaeb0dff220' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61b0fe19-d2cc-4e1b-9722-1eaeb0dff220' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91c22ff9-307f-48b4-9a92-0babadcac9ba' class='xr-var-data-in' type='checkbox'><label for='data-91c22ff9-307f-48b4-9a92-0babadcac9ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>195</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lgws</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Eastward gravity wave surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lgws</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Eastward gravity wave surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsm</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-317bb4a7-9f0c-4a79-ac95-2155cd8eface' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-317bb4a7-9f0c-4a79-ac95-2155cd8eface' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c690aec-3603-41ed-a133-e7008626465e' class='xr-var-data-in' type='checkbox'><label for='data-3c690aec-3603-41ed-a133-e7008626465e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>172</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsm</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Land-sea mask</dd><dt><span>GRIB_cfName :</span></dt><dd>land_binary_mask</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsm</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Land-sea mask</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2e950e65-2040-4206-8351-fba8e7255576' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e950e65-2040-4206-8351-fba8e7255576' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2690d435-5d4d-4a98-8088-c2d99dace832' class='xr-var-data-in' type='checkbox'><label for='data-2690d435-5d4d-4a98-8088-c2d99dace832' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>142</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Large-scale precipitation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_stratiform_precipitation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Large-scale precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lspf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe70be50-30fa-4e14-9486-fccc9ffd8a5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe70be50-30fa-4e14-9486-fccc9ffd8a5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b325328c-9cf1-4d7a-a6f8-330a94b1f923' class='xr-var-data-in' type='checkbox'><label for='data-b325328c-9cf1-4d7a-a6f8-330a94b1f923' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>50</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lspf</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Large-scale precipitation fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lspf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Large-scale precipitation fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsrr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3484e2ef-2b21-494e-ac54-df860a23c4df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3484e2ef-2b21-494e-ac54-df860a23c4df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb41c74c-1b78-44bd-8256-f505e48ab005' class='xr-var-data-in' type='checkbox'><label for='data-cb41c74c-1b78-44bd-8256-f505e48ab005' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228219</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsrr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Large scale rain rate</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsrr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Large scale rain rate</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lssfr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-57b3ae8e-8ca6-4bc3-b55e-8e3681cc838f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57b3ae8e-8ca6-4bc3-b55e-8e3681cc838f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7636e64d-bb84-4399-8a0e-3b8dd491fadd' class='xr-var-data-in' type='checkbox'><label for='data-7636e64d-bb84-4399-8a0e-3b8dd491fadd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228221</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lssfr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Large scale snowfall rate water equivalent</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lssfr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Large scale snowfall rate water equivalent</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9ada0a2-ba7d-44c1-9c0e-a309f8c52586' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9ada0a2-ba7d-44c1-9c0e-a309f8c52586' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e60ddb49-3d64-4571-980c-eb0d0f4aaeaa' class='xr-var-data-in' type='checkbox'><label for='data-e60ddb49-3d64-4571-980c-eb0d0f4aaeaa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>187</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Medium cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Medium cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mgws</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d6678fd-7769-43a0-8ccc-4e9c036ac87e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d6678fd-7769-43a0-8ccc-4e9c036ac87e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f248e374-3a3a-468b-b1f9-64316418ddcb' class='xr-var-data-in' type='checkbox'><label for='data-f248e374-3a3a-468b-b1f9-64316418ddcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>196</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mgws</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Northward gravity wave surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mgws</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Northward gravity wave surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mn2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d1d95ea7-9aae-4387-9e36-0dc4dcc6bc59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1d95ea7-9aae-4387-9e36-0dc4dcc6bc59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f46fd5ca-a543-42de-b39a-12dc176bf3c3' class='xr-var-data-in' type='checkbox'><label for='data-f46fd5ca-a543-42de-b39a-12dc176bf3c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>202</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mn2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mn2t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-57b40649-bec2-4419-9b9d-3dfd40691c17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57b40649-bec2-4419-9b9d-3dfd40691c17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b35e2f93-608a-47cd-9f8e-0bd008ba27a8' class='xr-var-data-in' type='checkbox'><label for='data-b35e2f93-608a-47cd-9f8e-0bd008ba27a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>msl</dd><dt><span>GRIB_units :</span></dt><dd>Pa</dd><dt><span>GRIB_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>GRIB_cfName :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>GRIB_cfVarName :</span></dt><dd>msl</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>Mean sea level pressure</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mx2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fdbc11be-81c0-4153-b9a5-b7331e741090' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdbc11be-81c0-4153-b9a5-b7331e741090' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-602f574b-f208-477d-bf6a-5d60d4c509a1' class='xr-var-data-in' type='checkbox'><label for='data-602f574b-f208-477d-bf6a-5d60d4c509a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>201</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mx2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mx2t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d40b7ba9-6d8a-478b-9bdf-a90b58f394ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d40b7ba9-6d8a-478b-9bdf-a90b58f394ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-619488ce-1a28-4bd7-9ee5-cc49833a3547' class='xr-var-data-in' type='checkbox'><label for='data-619488ce-1a28-4bd7-9ee5-cc49833a3547' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>181</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>nsss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated northward turbulent surface stress</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downward_northward_stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>nsss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Time-integrated northward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocu</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4020b49f-19a7-42ed-80b8-92f77239e006' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4020b49f-19a7-42ed-80b8-92f77239e006' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db8e6521-5896-4705-9beb-6e572cdcf9d5' class='xr-var-data-in' type='checkbox'><label for='data-db8e6521-5896-4705-9beb-6e572cdcf9d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151131</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ocu</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Eastward surface sea water velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>uoe</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Eastward surface sea water velocity</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocv</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd304fc2-3ac0-4fd1-90a5-a53905b5fa2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd304fc2-3ac0-4fd1-90a5-a53905b5fa2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e902536-6bae-44cc-86e7-cd3126b421b5' class='xr-var-data-in' type='checkbox'><label for='data-8e902536-6bae-44cc-86e7-cd3126b421b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151132</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ocv</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Northward surface sea water velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>von</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Northward surface sea water velocity</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>par</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1aef762c-1804-4cc9-aca6-52fa0338f644' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1aef762c-1804-4cc9-aca6-52fa0338f644' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6dcaa6c-33cf-4c92-96e7-e251e0c9b357' class='xr-var-data-in' type='checkbox'><label for='data-b6dcaa6c-33cf-4c92-96e7-e251e0c9b357' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>58</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>par</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Photosynthetically active radiation at the surface</dd><dt><span>GRIB_cfVarName :</span></dt><dd>par</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Photosynthetically active radiation at the surface</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pev</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0c714488-07dd-4175-83bc-75cf2ef4387e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c714488-07dd-4175-83bc-75cf2ef4387e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fa3f44e-f6eb-4f71-9010-03a0d991b955' class='xr-var-data-in' type='checkbox'><label for='data-7fa3f44e-f6eb-4f71-9010-03a0d991b955' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228251</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>pev</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Potential evaporation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>pev</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Potential evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ptype</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6b7581c0-70b5-4898-a2d9-768ae0bf5316' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b7581c0-70b5-4898-a2d9-768ae0bf5316' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e6e4e89-a1cd-4b94-aea4-6d3f2642438c' class='xr-var-data-in' type='checkbox'><label for='data-6e6e4e89-a1cd-4b94-aea4-6d3f2642438c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260015</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ptype</dd><dt><span>GRIB_units :</span></dt><dd>(Code table 4.201)</dd><dt><span>GRIB_name :</span></dt><dd>Precipitation type</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ptype</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(Code table 4.201)</dd><dt><span>long_name :</span></dt><dd>Precipitation type</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aef2a71a-3387-4e43-9a08-5b1f66191a7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aef2a71a-3387-4e43-9a08-5b1f66191a7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eade02b5-d3f3-4cfd-be29-06c336c65f56' class='xr-var-data-in' type='checkbox'><label for='data-eade02b5-d3f3-4cfd-be29-06c336c65f56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>205</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-de5e4fcd-c741-4bbe-8c82-840246fbdac0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de5e4fcd-c741-4bbe-8c82-840246fbdac0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eb6200f-56dc-4f22-9c97-fbb4ba2e601b' class='xr-var-data-in' type='checkbox'><label for='data-3eb6200f-56dc-4f22-9c97-fbb4ba2e601b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>33</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>rsn</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Snow density</dd><dt><span>GRIB_cfVarName :</span></dt><dd>rsn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-3</dd><dt><span>long_name :</span></dt><dd>Snow density</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6919c2f-8407-48ca-9c75-ab5832c99ceb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6919c2f-8407-48ca-9c75-ab5832c99ceb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5430264a-47af-418e-8ab9-edd73bcbab92' class='xr-var-data-in' type='checkbox'><label for='data-5430264a-47af-418e-8ab9-edd73bcbab92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>141</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sd</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow depth</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snow depth</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3fcebd12-1652-428a-937f-540d285b35cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fcebd12-1652-428a-937f-540d285b35cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c68793ab-9f7f-4520-a892-485f780a924f' class='xr-var-data-in' type='checkbox'><label for='data-c68793ab-9f7f-4520-a892-485f780a924f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>144</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sf</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snowfall</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snowfall</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-796280a9-653c-4ffc-85f6-e86581651155' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-796280a9-653c-4ffc-85f6-e86581651155' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0b2e815-b5bd-4448-884f-20c4d2cd7d26' class='xr-var-data-in' type='checkbox'><label for='data-a0b2e815-b5bd-4448-884f-20c4d2cd7d26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>235</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>skt</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Skin temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>skt</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Skin temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-56ed2c60-2ac2-494a-92a2-8b0a6b25a3fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56ed2c60-2ac2-494a-92a2-8b0a6b25a3fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faa336e5-f61d-4ca5-8779-b7967697e914' class='xr-var-data-in' type='checkbox'><label for='data-faa336e5-f61d-4ca5-8779-b7967697e914' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>147</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>slhf</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated surface latent heat net flux</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_upward_latent_heat_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>slhf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated surface latent heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fee51575-03c5-45a2-ad53-ff832fe28b24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fee51575-03c5-45a2-ad53-ff832fe28b24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bd33ae1-2542-4154-94fd-4b5f29772dea' class='xr-var-data-in' type='checkbox'><label for='data-3bd33ae1-2542-4154-94fd-4b5f29772dea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>45</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>smlt</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snowmelt</dd><dt><span>GRIB_cfVarName :</span></dt><dd>smlt</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snowmelt</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>src</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9205b419-7233-4b4a-a879-bb9ef1098049' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9205b419-7233-4b4a-a879-bb9ef1098049' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a53d241d-9bf7-4657-b088-63b84eb3a57d' class='xr-var-data-in' type='checkbox'><label for='data-a53d241d-9bf7-4657-b088-63b84eb3a57d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>198</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>src</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Skin reservoir content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>src</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Skin reservoir content</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e7f28a22-7fa2-4d6a-a662-3e72e4dff414' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7f28a22-7fa2-4d6a-a662-3e72e4dff414' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be186a07-a207-4092-9caa-43a60413d616' class='xr-var-data-in' type='checkbox'><label for='data-be186a07-a207-4092-9caa-43a60413d616' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>8</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Surface runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Surface runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-189c6cd7-00c0-40de-8c4e-f38a222ce192' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-189c6cd7-00c0-40de-8c4e-f38a222ce192' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d7ea1b5-1917-400e-8514-9d91ad2dda18' class='xr-var-data-in' type='checkbox'><label for='data-6d7ea1b5-1917-400e-8514-9d91ad2dda18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>146</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sshf</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated surface sensible heat net flux</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_upward_sensible_heat_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sshf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated surface sensible heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-66a35b14-0a20-4d6f-8055-0b01996490a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66a35b14-0a20-4d6f-8055-0b01996490a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eef8ec09-6c67-43b8-95cf-b7831311feef' class='xr-var-data-in' type='checkbox'><label for='data-eef8ec09-6c67-43b8-95cf-b7831311feef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>176</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net short-wave (solar) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c345174-d180-4eac-a926-b1c1c7990493' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c345174-d180-4eac-a926-b1c1c7990493' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38e699f2-aa1e-4a1f-aea7-9538bf36079f' class='xr-var-data-in' type='checkbox'><label for='data-38e699f2-aa1e-4a1f-aea7-9538bf36079f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>210</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net short-wave (solar) radiation, clear sky</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 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80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net short-wave (solar) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c59948a-471f-49b5-b2d3-4abe11cf21a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c59948a-471f-49b5-b2d3-4abe11cf21a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfb711c9-5c72-4679-9ebf-5e91585408b7' class='xr-var-data-in' type='checkbox'><label for='data-cfb711c9-5c72-4679-9ebf-5e91585408b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>169</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssrd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface short-wave (solar) radiation downwards</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downwelling_shortwave_flux_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssrd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 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680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface short-wave (solar) radiation downwards</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a0edc35d-4421-4907-9b2b-9683ef933366' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0edc35d-4421-4907-9b2b-9683ef933366' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7223cc7e-991b-4e6b-af92-259e71f7247b' class='xr-var-data-in' type='checkbox'><label for='data-7223cc7e-991b-4e6b-af92-259e71f7247b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>9</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Sub-surface runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Sub-surface runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ae9df472-984e-44c6-af08-e13cc68d664c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae9df472-984e-44c6-af08-e13cc68d664c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c2aec12-b511-46da-aa7f-eab776fbd6a9' class='xr-var-data-in' type='checkbox'><label for='data-8c2aec12-b511-46da-aa7f-eab776fbd6a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>34</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sst</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Sea surface temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sst</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Sea surface temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71428f56-419d-442d-9e45-40077196ab9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71428f56-419d-442d-9e45-40077196ab9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf8205a0-b8f6-472f-9cba-55cba4b43248' class='xr-var-data-in' type='checkbox'><label for='data-bf8205a0-b8f6-472f-9cba-55cba4b43248' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>139</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl1</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 1</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ec021033-a9bb-426b-bc51-72c3f5664c2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec021033-a9bb-426b-bc51-72c3f5664c2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0950d5c5-792a-4e41-9a2c-e2133a749fa2' class='xr-var-data-in' type='checkbox'><label for='data-0950d5c5-792a-4e41-9a2c-e2133a749fa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>170</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl2</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d78dcb46-83ae-47df-92b3-5762e8bbd057' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d78dcb46-83ae-47df-92b3-5762e8bbd057' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db7647bf-9cd7-4ac9-943f-b421bd843d5a' class='xr-var-data-in' type='checkbox'><label for='data-db7647bf-9cd7-4ac9-943f-b421bd843d5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>183</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl3</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-496bb793-77f4-422f-91fd-314e2984ba28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-496bb793-77f4-422f-91fd-314e2984ba28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-179218c6-f471-4bc6-82e3-c5ad7aa6fe7f' class='xr-var-data-in' type='checkbox'><label for='data-179218c6-f471-4bc6-82e3-c5ad7aa6fe7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>236</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl4</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d730cb9b-4ba7-4e85-8680-7819f50a3eb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d730cb9b-4ba7-4e85-8680-7819f50a3eb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3214c1c0-034d-4cad-a0cb-31896d0bb8a1' class='xr-var-data-in' type='checkbox'><label for='data-3214c1c0-034d-4cad-a0cb-31896d0bb8a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>177</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>str</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net long-wave (thermal) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_upward_longwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>str</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net long-wave (thermal) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7fa137bc-2612-41c8-b479-d2d553ec3618' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fa137bc-2612-41c8-b479-d2d553ec3618' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db36b9b2-212e-4b12-8c07-3594d2c55791' class='xr-var-data-in' type='checkbox'><label for='data-db36b9b2-212e-4b12-8c07-3594d2c55791' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>211</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>strc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net long-wave (thermal) radiation, clear sky</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>strc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 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80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net long-wave (thermal) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0294010e-5f30-4db4-a9d4-6ee3d2f98c54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0294010e-5f30-4db4-a9d4-6ee3d2f98c54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a681a71-c67d-45a3-bf2d-2ee6dacd31ba' class='xr-var-data-in' type='checkbox'><label for='data-1a681a71-c67d-45a3-bf2d-2ee6dacd31ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>175</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>strd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface long-wave (thermal) radiation downwards</dd><dt><span>GRIB_cfVarName :</span></dt><dd>strd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 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1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface long-wave (thermal) radiation downwards</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sund</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd1119ba-4b8e-4a2c-8388-13d7a53d4c08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd1119ba-4b8e-4a2c-8388-13d7a53d4c08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf6caaca-f6a6-4a6d-9e16-8a66ff57cc95' class='xr-var-data-in' type='checkbox'><label for='data-cf6caaca-f6a6-4a6d-9e16-8a66ff57cc95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>189</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sund</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Sunshine duration</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sund</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Sunshine duration</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf39951f-52b3-4066-bd6e-fb61ce1a581a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf39951f-52b3-4066-bd6e-fb61ce1a581a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10b9ae13-fb24-4324-b1f9-bdcb28160cbb' class='xr-var-data-in' type='checkbox'><label for='data-10b9ae13-fb24-4324-b1f9-bdcb28160cbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>39</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl1</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6f5069cc-e64d-4a07-a70c-0247ebdfe1aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f5069cc-e64d-4a07-a70c-0247ebdfe1aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fde99bc5-b764-453f-a215-3d18ecc8bdb7' class='xr-var-data-in' type='checkbox'><label for='data-fde99bc5-b764-453f-a215-3d18ecc8bdb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>40</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl2</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5445d33-591c-4d12-a3ab-a44466a144f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5445d33-591c-4d12-a3ab-a44466a144f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c51bf9e7-c832-4b93-9244-43a7928d75d4' class='xr-var-data-in' type='checkbox'><label for='data-c51bf9e7-c832-4b93-9244-43a7928d75d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>41</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl3</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f2cf340a-5977-4896-b030-ee67055d166b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2cf340a-5977-4896-b030-ee67055d166b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e7a5fd5-8649-48fa-b553-c67035250706' class='xr-var-data-in' type='checkbox'><label for='data-6e7a5fd5-8649-48fa-b553-c67035250706' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>42</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl4</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42b204a7-fabf-4129-9ef2-e60ecfdb477b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42b204a7-fabf-4129-9ef2-e60ecfdb477b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c1745a2-bb24-488f-99c2-9d3ee61d75db' class='xr-var-data-in' type='checkbox'><label for='data-6c1745a2-bb24-488f-99c2-9d3ee61d75db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>164</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Total cloud cover</dd><dt><span>GRIB_cfName :</span></dt><dd>cloud_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 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552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Total cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4eaf8857-e4f3-4cad-885f-08d76371cc98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4eaf8857-e4f3-4cad-885f-08d76371cc98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3941398-872b-49ef-b36e-08e8063534b7' class='xr-var-data-in' type='checkbox'><label for='data-b3941398-872b-49ef-b36e-08e8063534b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>79</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tciw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud ice water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tciw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 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1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud ice water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-36d13aca-5a0a-4e44-8a98-c016e3f8727e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36d13aca-5a0a-4e44-8a98-c016e3f8727e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca307c41-f4cd-4f9f-8a3b-ef052bbeed31' class='xr-var-data-in' type='checkbox'><label for='data-ca307c41-f4cd-4f9f-8a3b-ef052bbeed31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>78</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tclw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud liquid water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tclw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud liquid water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tco3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-170fe159-b55a-4970-ba67-3bc152e9a1ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-170fe159-b55a-4970-ba67-3bc152e9a1ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60b5f8ed-2cc5-4718-834f-40e352ef8265' class='xr-var-data-in' type='checkbox'><label for='data-60b5f8ed-2cc5-4718-834f-40e352ef8265' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>206</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tco3</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column ozone</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_mass_content_of_ozone</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tco3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column ozone</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcrw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-877c760b-a606-4e89-a7b3-d8f4f87d7e1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-877c760b-a606-4e89-a7b3-d8f4f87d7e1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-158868b7-f951-4127-bd97-0441b88ac9bb' class='xr-var-data-in' type='checkbox'><label for='data-158868b7-f951-4127-bd97-0441b88ac9bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228089</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcrw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column rain water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcrw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column rain water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcsw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-92accef0-9a54-4f3e-84ae-fa47de9fdb62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92accef0-9a54-4f3e-84ae-fa47de9fdb62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f803fae-5e96-4dd5-9c6f-4972e48e0580' class='xr-var-data-in' type='checkbox'><label for='data-2f803fae-5e96-4dd5-9c6f-4972e48e0580' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228090</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcsw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column snow water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcsw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column snow water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e9d6e8b8-49a0-4418-872b-741a94c2f3fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9d6e8b8-49a0-4418-872b-741a94c2f3fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-350d1d2b-f586-4fd4-b64d-c14a0f53c51f' class='xr-var-data-in' type='checkbox'><label for='data-350d1d2b-f586-4fd4-b64d-c14a0f53c51f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>136</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcwv</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-056eb36b-6643-488a-8198-a51a058203a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-056eb36b-6643-488a-8198-a51a058203a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-665b25b3-b3fc-4436-8329-d39e920e8976' class='xr-var-data-in' type='checkbox'><label for='data-665b25b3-b3fc-4436-8329-d39e920e8976' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>137</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcwv</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column vertically-integrated water vapour</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_atmosphere_mass_content_of_water_vapor</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcwv</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column vertically-integrated water vapour</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-977c09a5-749b-4868-b179-7e3a3c7b64a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-977c09a5-749b-4868-b179-7e3a3c7b64a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d8005af-5ec7-464d-a245-03f0f85d2605' class='xr-var-data-in' type='checkbox'><label for='data-8d8005af-5ec7-464d-a245-03f0f85d2605' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>212</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tisr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>TOA incident short-wave (solar) radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tisr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>TOA incident short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>totalx</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4eb27327-b609-40d4-ac09-5ad17e362fc2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4eb27327-b609-40d4-ac09-5ad17e362fc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fdb3047-44a8-4f4a-8906-2339e9cb186b' class='xr-var-data-in' type='checkbox'><label for='data-6fdb3047-44a8-4f4a-8906-2339e9cb186b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260123</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>totalx</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Total totals index</dd><dt><span>GRIB_cfVarName :</span></dt><dd>totalx</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Total totals index</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9a0403e5-48dc-4378-aefb-fecdcdb6d640' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a0403e5-48dc-4378-aefb-fecdcdb6d640' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10b275e5-6939-4a7f-9a57-ed6dd609d418' class='xr-var-data-in' type='checkbox'><label for='data-10b275e5-6939-4a7f-9a57-ed6dd609d418' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Total precipitation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c1584352-e35c-4fe1-9bce-564b752c647e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1584352-e35c-4fe1-9bce-564b752c647e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72cc0642-55c6-4820-86af-95683105e2d0' class='xr-var-data-in' type='checkbox'><label for='data-72cc0642-55c6-4820-86af-95683105e2d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>238</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsn</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Temperature of snow layer</dd><dt><span>GRIB_cfName :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature of snow layer</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9137f236-267a-4f40-a677-17cb074588b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9137f236-267a-4f40-a677-17cb074588b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0f9e621-ee64-4200-90fd-600b644eca0d' class='xr-var-data-in' type='checkbox'><label for='data-a0f9e621-ee64-4200-90fd-600b644eca0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>178</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net short-wave (solar) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>toa_net_upward_shortwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c6edeb83-8cf4-4a93-9e5c-f0b208894b68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6edeb83-8cf4-4a93-9e5c-f0b208894b68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c04f4766-f0d1-4ece-a540-d9973c339da7' class='xr-var-data-in' type='checkbox'><label for='data-c04f4766-f0d1-4ece-a540-d9973c339da7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>208</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net short-wave (solar) radiation, clear sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net short-wave (solar) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45647650-9932-4489-ae4f-ab832cf0169f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45647650-9932-4489-ae4f-ab832cf0169f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17ee677e-2541-48dd-95a6-b517e72b04a9' class='xr-var-data-in' type='checkbox'><label for='data-17ee677e-2541-48dd-95a6-b517e72b04a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>179</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ttr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net long-wave (thermal) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>toa_outgoing_longwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ttr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net long-wave (thermal) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aedaece5-3de1-4ed8-a3af-fb0cbb35a791' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aedaece5-3de1-4ed8-a3af-fb0cbb35a791' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8f0d149-8698-4e2f-8c0b-429ecc754bfb' class='xr-var-data-in' type='checkbox'><label for='data-f8f0d149-8698-4e2f-8c0b-429ecc754bfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>209</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ttrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net long-wave (thermal) radiation, clear sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ttrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net long-wave (thermal) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uvb</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84a29eb8-4c6b-4355-af42-86d1eeb9baf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84a29eb8-4c6b-4355-af42-86d1eeb9baf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f8e2fc5-3642-4f5c-8f24-495ecdfcba13' class='xr-var-data-in' type='checkbox'><label for='data-8f8e2fc5-3642-4f5c-8f24-495ecdfcba13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>57</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>uvb</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface downward UV radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>uvb</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface downward UV radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vimd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7225b934-1def-4765-83e4-0d12d78ce5de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7225b934-1def-4765-83e4-0d12d78ce5de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a065f09-0c53-4a1a-9a92-4a87d969c090' class='xr-var-data-in' type='checkbox'><label for='data-7a065f09-0c53-4a1a-9a92-4a87d969c090' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>213</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vimd</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Vertically integrated moisture divergence</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vimd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Vertically integrated moisture divergence</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vis</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e66c8d44-3798-42e2-8898-3d3869911b76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e66c8d44-3798-42e2-8898-3d3869911b76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee47a936-b761-466f-b684-0bcea74c5059' class='xr-var-data-in' type='checkbox'><label for='data-ee47a936-b761-466f-b684-0bcea74c5059' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>3020</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vis</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Visibility</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vis</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 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796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Visibility</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f14516ab-834c-4317-81b8-ef2860c2c963' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f14516ab-834c-4317-81b8-ef2860c2c963' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb76289e-33cb-474d-91ad-e449ce747980' class='xr-var-data-in' type='checkbox'><label for='data-bb76289e-33cb-474d-91ad-e449ce747980' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 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1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>long_name :</span></dt><dd>Geopotential</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3060d70-b3bb-4e40-9052-cf8684106005' class='xr-section-summary-in' type='checkbox'  ><label for='section-e3060d70-b3bb-4e40-9052-cf8684106005' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1c7349db-4c8f-4f3c-9d91-4a826100be10' class='xr-index-data-in' type='checkbox'/><label for='index-1c7349db-4c8f-4f3c-9d91-4a826100be10' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2910b6c0-c581-4723-a3d1-0324f0e344c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2910b6c0-c581-4723-a3d1-0324f0e344c4' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 11GB\nDimensions:  (time: 21, value: 654400)\nCoordinates:\n    lat      (value) float64 5MB ...\n    lon      (value) float64 5MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables: (12/101)\n    10fg     (time, value) float64 110MB ...\n    10u      (time, value) float64 110MB ...\n    10v      (time, value) float64 110MB ...\n    2d       (time, value) float64 110MB ...\n    2t       (time, value) float64 110MB ...\n    asn      (time, value) float64 110MB ...\n    ...       ...\n    ttr      (time, value) float64 110MB ...\n    ttrc     (time, value) float64 110MB ...\n    uvb      (time, value) float64 110MB ...\n    vimd     (time, value) float64 110MB ...\n    vis      (time, value) float64 110MB ...\n    z        (time, value) float64 110MB ...\nAttributes:\n    GRIB_edition:            2\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e0bd12ed-d685-4c27-afec-6bd2743c47c6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e0bd12ed-d685-4c27-afec-6bd2743c47c6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span>value</span>: 654400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d7b18631-fcf1-4c3a-8653-5a2b374cf0e0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d7b18631-fcf1-4c3a-8653-5a2b374cf0e0' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e820e58e-dd4a-4161-a4e0-217d3f13fa16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e820e58e-dd4a-4161-a4e0-217d3f13fa16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-950737d8-9c9d-45e3-8962-5f19b042b140' class='xr-var-data-in' type='checkbox'><label for='data-950737d8-9c9d-45e3-8962-5f19b042b140' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ffeb96a8-a4e2-4aa5-affc-49192a5c97a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffeb96a8-a4e2-4aa5-affc-49192a5c97a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-298f0ecc-e5d6-48e1-8646-1da5b4c4f458' class='xr-var-data-in' type='checkbox'><label for='data-298f0ecc-e5d6-48e1-8646-1da5b4c4f458' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[654400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-0dfb9582-cd23-4846-b4d1-0cf3fb7487c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dfb9582-cd23-4846-b4d1-0cf3fb7487c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0735ca2d-511a-4a6c-992d-811b4bfb3f12' class='xr-var-data-in' type='checkbox'><label for='data-0735ca2d-511a-4a6c-992d-811b4bfb3f12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c1d9947-df1a-4804-9c69-3d47f3f939cb' class='xr-section-summary-in' type='checkbox'  ><label for='section-3c1d9947-df1a-4804-9c69-3d47f3f939cb' class='xr-section-summary' >Data variables: <span>(101)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10fg</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1cd0f75e-b064-4415-8e71-edec67ecc0a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cd0f75e-b064-4415-8e71-edec67ecc0a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbc5df41-d5a1-429d-9687-9b9abca81704' class='xr-var-data-in' type='checkbox'><label for='data-fbc5df41-d5a1-429d-9687-9b9abca81704' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>49</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10fg</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Maximum 10 metre wind gust since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fg10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Maximum 10 metre wind gust since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-88184084-dacc-44b0-b2cf-65fcbe48f2aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88184084-dacc-44b0-b2cf-65fcbe48f2aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03b0c072-3f44-4b52-9e51-e643f60cbf85' class='xr-var-data-in' type='checkbox'><label for='data-03b0c072-3f44-4b52-9e51-e643f60cbf85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>165</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10u</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre U wind component</dd><dt><span>GRIB_cfVarName :</span></dt><dd>u10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3f93b353-c03c-44bd-8d16-44a3b97f9e01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f93b353-c03c-44bd-8d16-44a3b97f9e01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-369c3022-5d8f-4917-953b-114514b52dfd' class='xr-var-data-in' type='checkbox'><label for='data-369c3022-5d8f-4917-953b-114514b52dfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>166</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>10v</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre V wind component</dd><dt><span>GRIB_cfVarName :</span></dt><dd>v10</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-567f65ed-a061-476e-99ed-eb9545476043' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-567f65ed-a061-476e-99ed-eb9545476043' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad1877ef-1c0a-4eff-9f64-31a14c3b7a62' class='xr-var-data-in' type='checkbox'><label for='data-ad1877ef-1c0a-4eff-9f64-31a14c3b7a62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>168</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>2d</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>d2m</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-293b745c-e231-483a-9f2f-4f7b1d360d16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-293b745c-e231-483a-9f2f-4f7b1d360d16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ea3ee71-c26a-4bf3-a873-1718c00bd824' class='xr-var-data-in' type='checkbox'><label for='data-5ea3ee71-c26a-4bf3-a873-1718c00bd824' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>167</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>2 metre temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>t2m</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>asn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4c8f3dae-30cf-4bf1-ba97-3e5974feb2bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c8f3dae-30cf-4bf1-ba97-3e5974feb2bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91686152-4484-4257-b1c2-aa9194ca2b19' class='xr-var-data-in' type='checkbox'><label for='data-91686152-4484-4257-b1c2-aa9194ca2b19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>32</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>asn</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Snow albedo</dd><dt><span>GRIB_cfVarName :</span></dt><dd>asn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Snow albedo</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bld</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d93a75d3-1e8a-472b-b388-6adbe57075b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d93a75d3-1e8a-472b-b388-6adbe57075b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-592b17e2-09c1-4086-b548-1ca1ad7fb26b' class='xr-var-data-in' type='checkbox'><label for='data-592b17e2-09c1-4086-b548-1ca1ad7fb26b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>145</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>bld</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated boundary layer dissipation</dd><dt><span>GRIB_cfName :</span></dt><dd>kinetic_energy_dissipation_in_atmosphere_boundary_layer</dd><dt><span>GRIB_cfVarName :</span></dt><dd>bld</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated boundary layer dissipation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blh</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c8eae540-a65f-4174-ae03-c33cc42557d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8eae540-a65f-4174-ae03-c33cc42557d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6676d053-645f-4c3c-8b43-f55d83a334b1' class='xr-var-data-in' type='checkbox'><label for='data-6676d053-645f-4c3c-8b43-f55d83a334b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>159</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>blh</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Boundary layer height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>blh</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Boundary layer height</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cape</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8944bbe3-d6f0-4afe-8736-52dc98009375' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8944bbe3-d6f0-4afe-8736-52dc98009375' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce75d039-cbfb-4867-80d8-a789855d211d' class='xr-var-data-in' type='checkbox'><label for='data-ce75d039-cbfb-4867-80d8-a789855d211d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>59</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cape</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective available potential energy</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cape</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective available potential energy</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chnk</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2e31cdf4-3a2c-4e02-bb9f-49bed7306cb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e31cdf4-3a2c-4e02-bb9f-49bed7306cb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d1d74ff-7bc1-480b-984c-bbb46eb9b53a' class='xr-var-data-in' type='checkbox'><label for='data-9d1d74ff-7bc1-480b-984c-bbb46eb9b53a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>148</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>chnk</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Charnock</dd><dt><span>GRIB_cfVarName :</span></dt><dd>chnk</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Charnock</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ci</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-61ced41b-4e34-4d86-8d70-181a002c68c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61ced41b-4e34-4d86-8d70-181a002c68c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8fda386-f281-4270-9ab9-ac19aa11ff0f' class='xr-var-data-in' type='checkbox'><label for='data-f8fda386-f281-4270-9ab9-ac19aa11ff0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>31</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ci</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>GRIB_cfName :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>siconc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cin</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-59317f9e-3a6c-4110-8a20-405643e86a34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59317f9e-3a6c-4110-8a20-405643e86a34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d03b7cc7-5d79-4c60-85e1-fa248c30d51e' class='xr-var-data-in' type='checkbox'><label for='data-d03b7cc7-5d79-4c60-85e1-fa248c30d51e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228001</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cin</dd><dt><span>GRIB_units :</span></dt><dd>J kg**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective inhibition</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cin</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J kg**-1</dd><dt><span>long_name :</span></dt><dd>Convective inhibition</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ef42c459-fa6b-4aea-accc-e7097821589b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef42c459-fa6b-4aea-accc-e7097821589b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-553bc244-13ee-4c40-aa38-1591f8d4677f' class='xr-var-data-in' type='checkbox'><label for='data-553bc244-13ee-4c40-aa38-1591f8d4677f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>143</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Convective precipitation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Convective precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a237c903-49e1-44e5-bed0-46713a798463' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a237c903-49e1-44e5-bed0-46713a798463' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-437bde91-7e7d-406b-94c6-f604a48d7f25' class='xr-var-data-in' type='checkbox'><label for='data-437bde91-7e7d-406b-94c6-f604a48d7f25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228218</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>crr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective rain rate</dd><dt><span>GRIB_cfVarName :</span></dt><dd>crr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Convective rain rate</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>csfr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cab090c6-64b5-4e05-9b4e-cb0990aaf976' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cab090c6-64b5-4e05-9b4e-cb0990aaf976' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99e7e35e-a43f-409d-b82e-cffd66e57f15' class='xr-var-data-in' type='checkbox'><label for='data-99e7e35e-a43f-409d-b82e-cffd66e57f15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228220</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>csfr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Convective snowfall rate water equivalent</dd><dt><span>GRIB_cfVarName :</span></dt><dd>csfr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Convective snowfall rate water equivalent</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dsrp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9cfbd7d5-4de1-4fd7-99c6-fa0764742ad7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9cfbd7d5-4de1-4fd7-99c6-fa0764742ad7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98700154-0b51-4bfb-8213-3e77ad06f75f' class='xr-var-data-in' type='checkbox'><label for='data-98700154-0b51-4bfb-8213-3e77ad06f75f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>47</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>dsrp</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface direct normal short-wave (solar) radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dsrp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface direct normal short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c9c27d9-2cb1-4abe-8d11-b759a67de326' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c9c27d9-2cb1-4abe-8d11-b759a67de326' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cd91935-d7b3-4c71-845a-8d13213876c9' class='xr-var-data-in' type='checkbox'><label for='data-8cd91935-d7b3-4c71-845a-8d13213876c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>182</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>e</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Evaporation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_water_evaporation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>e</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4505b6cb-3620-4271-84ce-84faad04e498' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4505b6cb-3620-4271-84ce-84faad04e498' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6c41d48-d99b-403d-bd93-6260a3b037eb' class='xr-var-data-in' type='checkbox'><label for='data-c6c41d48-d99b-403d-bd93-6260a3b037eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>44</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>es</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow evaporation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>es</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snow evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-da303981-433b-4d64-9658-1927d51e0178' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da303981-433b-4d64-9658-1927d51e0178' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c1ab404-bdc9-4bf4-b25d-485146a52239' class='xr-var-data-in' type='checkbox'><label for='data-8c1ab404-bdc9-4bf4-b25d-485146a52239' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>180</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ewss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated eastward turbulent surface stress</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downward_eastward_stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ewss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Time-integrated eastward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fal</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-92868dc5-b916-420b-a276-15581f94ed1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92868dc5-b916-420b-a276-15581f94ed1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c932bf52-133e-429a-8c89-a326fb378b92' class='xr-var-data-in' type='checkbox'><label for='data-c932bf52-133e-429a-8c89-a326fb378b92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>243</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fal</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Forecast albedo</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fal</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Forecast albedo</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1b85f667-058f-4889-9216-a8001833ac95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b85f667-058f-4889-9216-a8001833ac95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-684f692c-ca41-4102-9c21-5849ceaf3ffb' class='xr-var-data-in' type='checkbox'><label for='data-684f692c-ca41-4102-9c21-5849ceaf3ffb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>245</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>flsr</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Forecast logarithm of surface roughness for heat</dd><dt><span>GRIB_cfVarName :</span></dt><dd>flsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Forecast logarithm of surface roughness for heat</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3987d3ff-9b6d-44af-a6c2-6bacadc487a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3987d3ff-9b6d-44af-a6c2-6bacadc487a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-807c7129-1c3f-4aa1-a10f-b00ef7eadc91' class='xr-var-data-in' type='checkbox'><label for='data-807c7129-1c3f-4aa1-a10f-b00ef7eadc91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>244</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fsr</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Forecast surface roughness</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Forecast surface roughness</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fzra</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-75564b0c-c8e7-4979-9839-85dcd54e5363' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75564b0c-c8e7-4979-9839-85dcd54e5363' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9459cf5e-b133-421d-aecd-1b951f6203aa' class='xr-var-data-in' type='checkbox'><label for='data-9459cf5e-b133-421d-aecd-1b951f6203aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228216</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>fzra</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Accumulated freezing rain</dd><dt><span>GRIB_cfVarName :</span></dt><dd>fzra</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Accumulated freezing rain</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gwd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a1dc6895-e53e-4e4c-b42f-b69cdec86086' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1dc6895-e53e-4e4c-b42f-b69cdec86086' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3601e8e6-4f98-4584-9c58-95eee02694b1' class='xr-var-data-in' type='checkbox'><label for='data-3601e8e6-4f98-4584-9c58-95eee02694b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>197</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>gwd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Gravity wave dissipation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>gwd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Gravity wave dissipation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-04a32253-7690-4683-98d4-8b028ad7f57f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04a32253-7690-4683-98d4-8b028ad7f57f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bbc211f-9503-4ad7-9689-c43762d3eb24' class='xr-var-data-in' type='checkbox'><label for='data-3bbc211f-9503-4ad7-9689-c43762d3eb24' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>188</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>hcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>High cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 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1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>High cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>i10fg</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9d72fe3d-b94e-467c-89bb-03c665aa062d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d72fe3d-b94e-467c-89bb-03c665aa062d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-449939dc-83d6-4ec1-aba1-9befe33c6a17' class='xr-var-data-in' type='checkbox'><label for='data-449939dc-83d6-4ec1-aba1-9befe33c6a17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228029</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>i10fg</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous 10 metre wind gust</dd><dt><span>GRIB_cfVarName :</span></dt><dd>i10fg</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Instantaneous 10 metre wind gust</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ie</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5a690cc4-198d-4dff-8a3f-6fd5a1dad3ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a690cc4-198d-4dff-8a3f-6fd5a1dad3ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dde9ca70-6023-4dfd-b6e7-a6bc22828b71' class='xr-var-data-in' type='checkbox'><label for='data-dde9ca70-6023-4dfd-b6e7-a6bc22828b71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>232</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ie</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous moisture flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ie</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Instantaneous moisture flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iews</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a5f81763-f3c0-4662-8b99-a1734ac88cfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5f81763-f3c0-4662-8b99-a1734ac88cfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9c061b8-d47f-4e6f-9f42-b895bfd8c112' class='xr-var-data-in' type='checkbox'><label for='data-f9c061b8-d47f-4e6f-9f42-b895bfd8c112' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>229</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>iews</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous eastward turbulent surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>iews</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous eastward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ilspf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7c48ad6c-5728-4265-9ff3-8037a5a55cc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c48ad6c-5728-4265-9ff3-8037a5a55cc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d3753c7-b6d3-48c6-9c43-247b620f354d' class='xr-var-data-in' type='checkbox'><label for='data-2d3753c7-b6d3-48c6-9c43-247b620f354d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228217</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ilspf</dd><dt><span>GRIB_units :</span></dt><dd>Proportion</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous large-scale precipitation fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ilspf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Proportion</dd><dt><span>long_name :</span></dt><dd>Instantaneous large-scale precipitation fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f4fc4b81-7c76-46b3-9df2-68e895add0aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4fc4b81-7c76-46b3-9df2-68e895add0aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6fe01e2-e4ed-48d1-950b-d015e386e260' class='xr-var-data-in' type='checkbox'><label for='data-b6fe01e2-e4ed-48d1-950b-d015e386e260' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>230</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>inss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous northward turbulent surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>inss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous northward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ishf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7e3332d8-6308-487d-9fd9-d122d2a89fd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e3332d8-6308-487d-9fd9-d122d2a89fd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-362a5030-46ab-4304-a067-e5cb54ec802e' class='xr-var-data-in' type='checkbox'><label for='data-362a5030-46ab-4304-a067-e5cb54ec802e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>231</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ishf</dd><dt><span>GRIB_units :</span></dt><dd>W m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Instantaneous surface sensible heat net flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ishf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>W m**-2</dd><dt><span>long_name :</span></dt><dd>Instantaneous surface sensible heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8880b974-facb-4a92-92bd-25019c742fe7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8880b974-facb-4a92-92bd-25019c742fe7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb3f5c76-1ba9-4714-b0e6-cb12d28f86d6' class='xr-var-data-in' type='checkbox'><label for='data-bb3f5c76-1ba9-4714-b0e6-cb12d28f86d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>35</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl1</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 1</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-da806f4e-3130-4e75-a3f9-4b4a3b51587a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da806f4e-3130-4e75-a3f9-4b4a3b51587a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d36a5701-09ee-4bc3-9c5e-0c87d66bad2a' class='xr-var-data-in' type='checkbox'><label for='data-d36a5701-09ee-4bc3-9c5e-0c87d66bad2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>36</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl2</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1e400281-6cf9-47f4-903b-f8601792c849' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e400281-6cf9-47f4-903b-f8601792c849' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-648f592c-f541-4726-bc7b-605fb1f90d4d' class='xr-var-data-in' type='checkbox'><label for='data-648f592c-f541-4726-bc7b-605fb1f90d4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>37</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl3</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>istl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9ec91ecf-56ee-4bad-a16f-5d0dfc5f396c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ec91ecf-56ee-4bad-a16f-5d0dfc5f396c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3ef841a-cb9f-4c5c-83ad-f8bd00da7b1b' class='xr-var-data-in' type='checkbox'><label for='data-a3ef841a-cb9f-4c5c-83ad-f8bd00da7b1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>38</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>istl4</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Ice temperature layer 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>istl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Ice temperature layer 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kx</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ab52727e-e36f-4edc-9e47-2b330dda4138' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab52727e-e36f-4edc-9e47-2b330dda4138' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c29980f9-6fb7-4dc9-9f94-a75ea4640b1d' class='xr-var-data-in' type='checkbox'><label for='data-c29980f9-6fb7-4dc9-9f94-a75ea4640b1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260121</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>kx</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>K index</dd><dt><span>GRIB_cfVarName :</span></dt><dd>kx</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>K index</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-777f2029-b58a-4554-9780-bca2b1117c57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-777f2029-b58a-4554-9780-bca2b1117c57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b32f36e-9917-42ca-9a81-c866368dea29' class='xr-var-data-in' type='checkbox'><label for='data-5b32f36e-9917-42ca-9a81-c866368dea29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>186</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Low cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Low cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lgws</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49bbb910-d196-4dd4-8fc7-c3d8eb1e3f67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49bbb910-d196-4dd4-8fc7-c3d8eb1e3f67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7979523e-dc98-4cb2-ab14-974dbd3e5c80' class='xr-var-data-in' type='checkbox'><label for='data-7979523e-dc98-4cb2-ab14-974dbd3e5c80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>195</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lgws</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Eastward gravity wave surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lgws</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Eastward gravity wave surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsm</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c8233dbb-e4f7-433a-b51d-3a03b1ad336c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8233dbb-e4f7-433a-b51d-3a03b1ad336c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8839ecc8-ad26-41bd-92dd-307377452056' class='xr-var-data-in' type='checkbox'><label for='data-8839ecc8-ad26-41bd-92dd-307377452056' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>172</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsm</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Land-sea mask</dd><dt><span>GRIB_cfName :</span></dt><dd>land_binary_mask</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsm</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Land-sea mask</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf40e1d8-3c95-4def-8810-774d7b89a164' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf40e1d8-3c95-4def-8810-774d7b89a164' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6fa939f-5873-4c8f-828a-cbf5f7de1a39' class='xr-var-data-in' type='checkbox'><label for='data-d6fa939f-5873-4c8f-828a-cbf5f7de1a39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>142</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Large-scale precipitation</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_stratiform_precipitation_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Large-scale precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lspf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-15759632-9c31-4df7-8abd-9dad0964fcf5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15759632-9c31-4df7-8abd-9dad0964fcf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1861375-e632-4c9e-8081-50e16d7c3dc1' class='xr-var-data-in' type='checkbox'><label for='data-a1861375-e632-4c9e-8081-50e16d7c3dc1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>50</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lspf</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Large-scale precipitation fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lspf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Large-scale precipitation fraction</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lsrr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a35ab3df-b22c-45ab-aa23-d736ee6d519a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a35ab3df-b22c-45ab-aa23-d736ee6d519a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-182dacef-1362-410d-9473-40f1d70a1445' class='xr-var-data-in' type='checkbox'><label for='data-182dacef-1362-410d-9473-40f1d70a1445' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228219</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lsrr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Large scale rain rate</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lsrr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Large scale rain rate</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lssfr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-554c13e4-73d9-45e9-b279-8c682e5779a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-554c13e4-73d9-45e9-b279-8c682e5779a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3aabc51-879d-44eb-8be3-2a71b9bc11a2' class='xr-var-data-in' type='checkbox'><label for='data-d3aabc51-879d-44eb-8be3-2a71b9bc11a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228221</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>lssfr</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Large scale snowfall rate water equivalent</dd><dt><span>GRIB_cfVarName :</span></dt><dd>lssfr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd><dt><span>long_name :</span></dt><dd>Large scale snowfall rate water equivalent</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-616adfea-bdb0-4ae9-b9dc-641ceeddcbe4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-616adfea-bdb0-4ae9-b9dc-641ceeddcbe4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-721a1b9a-0f7d-4740-b91b-3cb28f47c500' class='xr-var-data-in' type='checkbox'><label for='data-721a1b9a-0f7d-4740-b91b-3cb28f47c500' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>187</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Medium cloud cover</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Medium cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mgws</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-70857eda-28f7-41a5-888c-246a68e93a00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70857eda-28f7-41a5-888c-246a68e93a00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39e13911-c38e-4977-af2b-b21e62010856' class='xr-var-data-in' type='checkbox'><label for='data-39e13911-c38e-4977-af2b-b21e62010856' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>196</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mgws</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Northward gravity wave surface stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mgws</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Northward gravity wave surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mn2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d8a1a66d-6d12-4bd5-9f3e-efddd44763c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8a1a66d-6d12-4bd5-9f3e-efddd44763c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e847091-1b14-483b-be72-cb1e4424f649' class='xr-var-data-in' type='checkbox'><label for='data-1e847091-1b14-483b-be72-cb1e4424f649' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>202</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mn2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mn2t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Minimum temperature at 2 metres since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d263a5f-ff9e-46c3-ad1d-37dec299bf53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d263a5f-ff9e-46c3-ad1d-37dec299bf53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8df8979f-d8d9-4ecb-a0b0-0ca4aab4586d' class='xr-var-data-in' type='checkbox'><label for='data-8df8979f-d8d9-4ecb-a0b0-0ca4aab4586d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>msl</dd><dt><span>GRIB_units :</span></dt><dd>Pa</dd><dt><span>GRIB_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>GRIB_cfName :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>GRIB_cfVarName :</span></dt><dd>msl</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>long_name :</span></dt><dd>Mean sea level pressure</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mx2t</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a102b528-7638-4ebe-b966-dc0374883269' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a102b528-7638-4ebe-b966-dc0374883269' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2571793-c7a0-42b3-aca3-92f79a0d1567' class='xr-var-data-in' type='checkbox'><label for='data-d2571793-c7a0-42b3-aca3-92f79a0d1567' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>201</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>max</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mx2t</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mx2t</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>2</dd><dt><span>GRIB_P1 :</span></dt><dd>228</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Maximum temperature at 2 metres since previous post-processing</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b1928ed0-34d0-4f85-959c-75ba5d312e96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1928ed0-34d0-4f85-959c-75ba5d312e96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bf88cd7-c2f7-42ff-9478-745a215009d5' class='xr-var-data-in' type='checkbox'><label for='data-6bf88cd7-c2f7-42ff-9478-745a215009d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>181</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>nsss</dd><dt><span>GRIB_units :</span></dt><dd>N m**-2 s</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated northward turbulent surface stress</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downward_northward_stress</dd><dt><span>GRIB_cfVarName :</span></dt><dd>nsss</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>N m**-2 s</dd><dt><span>long_name :</span></dt><dd>Time-integrated northward turbulent surface stress</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocu</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ffdd623-5fc1-4ca4-b2ec-d412556eab9b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ffdd623-5fc1-4ca4-b2ec-d412556eab9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d009314d-b3cd-41b3-add4-600e273628af' class='xr-var-data-in' type='checkbox'><label for='data-d009314d-b3cd-41b3-add4-600e273628af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151131</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ocu</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Eastward surface sea water velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>uoe</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Eastward surface sea water velocity</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ocv</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e4ef7f8f-dedc-425d-88f4-4da1e703210d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4ef7f8f-dedc-425d-88f4-4da1e703210d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfbde6d6-4283-4735-8c98-1ac3c79af226' class='xr-var-data-in' type='checkbox'><label for='data-cfbde6d6-4283-4735-8c98-1ac3c79af226' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>151132</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ocv</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Northward surface sea water velocity</dd><dt><span>GRIB_cfName :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>GRIB_cfVarName :</span></dt><dd>von</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 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352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Northward surface sea water velocity</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>par</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c4db531f-85b2-4ff5-abab-06ceac2867e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4db531f-85b2-4ff5-abab-06ceac2867e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e9c9be4-31c8-4589-bfcf-a7213ce23012' class='xr-var-data-in' type='checkbox'><label for='data-8e9c9be4-31c8-4589-bfcf-a7213ce23012' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>58</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>par</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Photosynthetically active radiation at the surface</dd><dt><span>GRIB_cfVarName :</span></dt><dd>par</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 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840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Photosynthetically active radiation at the surface</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pev</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-28482853-35e9-4f7d-bb27-bce23ecf89e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28482853-35e9-4f7d-bb27-bce23ecf89e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76afdf7f-367b-48d2-9b1c-95a69293e236' class='xr-var-data-in' type='checkbox'><label for='data-76afdf7f-367b-48d2-9b1c-95a69293e236' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228251</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>pev</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Potential evaporation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>pev</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Potential evaporation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ptype</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd843dbf-25f7-4f14-b44f-cf180d1e4f13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd843dbf-25f7-4f14-b44f-cf180d1e4f13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b6da709-0030-4122-87d4-c5ff7f431160' class='xr-var-data-in' type='checkbox'><label for='data-8b6da709-0030-4122-87d4-c5ff7f431160' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260015</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ptype</dd><dt><span>GRIB_units :</span></dt><dd>(Code table 4.201)</dd><dt><span>GRIB_name :</span></dt><dd>Precipitation type</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ptype</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(Code table 4.201)</dd><dt><span>long_name :</span></dt><dd>Precipitation type</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b353c8bd-d657-4b8f-85e6-80dddd9b94d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b353c8bd-d657-4b8f-85e6-80dddd9b94d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62d1a74a-0ca5-4bd5-9b01-e9cccdff724a' class='xr-var-data-in' type='checkbox'><label for='data-62d1a74a-0ca5-4bd5-9b01-e9cccdff724a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>205</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-de17b385-f34c-4c20-83ad-d936e907b239' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de17b385-f34c-4c20-83ad-d936e907b239' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b88b486b-2552-4eb9-b844-5141c6c4aaf5' class='xr-var-data-in' type='checkbox'><label for='data-b88b486b-2552-4eb9-b844-5141c6c4aaf5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>33</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>rsn</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Snow density</dd><dt><span>GRIB_cfVarName :</span></dt><dd>rsn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-3</dd><dt><span>long_name :</span></dt><dd>Snow density</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b84002a7-d49c-4762-8851-f8db043b9a2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b84002a7-d49c-4762-8851-f8db043b9a2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-249e21a5-736f-4ca3-8b8c-1cec7f347961' class='xr-var-data-in' type='checkbox'><label for='data-249e21a5-736f-4ca3-8b8c-1cec7f347961' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>141</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sd</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snow depth</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snow depth</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8fb8109a-24b2-407f-9c26-ca2c37feee46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fb8109a-24b2-407f-9c26-ca2c37feee46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ea79958-85c5-4548-93d8-6d8e6c18a4cb' class='xr-var-data-in' type='checkbox'><label for='data-8ea79958-85c5-4548-93d8-6d8e6c18a4cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>144</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sf</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snowfall</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snowfall</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3c65356e-2a03-4a7e-9b89-70156fddc42b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c65356e-2a03-4a7e-9b89-70156fddc42b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-032c90cf-cfdc-4e14-a0fd-255dfd4ff7e9' class='xr-var-data-in' type='checkbox'><label for='data-032c90cf-cfdc-4e14-a0fd-255dfd4ff7e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>235</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>skt</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Skin temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>skt</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Skin temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2802bdbe-d31d-49c5-bd88-f22420dcb67f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2802bdbe-d31d-49c5-bd88-f22420dcb67f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3e4b050-bb94-42a8-83dc-83a2e501f754' class='xr-var-data-in' type='checkbox'><label for='data-a3e4b050-bb94-42a8-83dc-83a2e501f754' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>147</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>slhf</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated surface latent heat net flux</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_upward_latent_heat_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>slhf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated surface latent heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-905e3751-09cc-4a50-9383-874cbe7b36a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-905e3751-09cc-4a50-9383-874cbe7b36a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8935e287-6372-4679-a231-a8662d2e5284' class='xr-var-data-in' type='checkbox'><label for='data-8935e287-6372-4679-a231-a8662d2e5284' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>45</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>smlt</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Snowmelt</dd><dt><span>GRIB_cfVarName :</span></dt><dd>smlt</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Snowmelt</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>src</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-99f8ea9f-8ae5-44c5-844a-8500eec1bb34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99f8ea9f-8ae5-44c5-844a-8500eec1bb34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fef22357-6092-4c7c-9d7e-28afeb3d3171' class='xr-var-data-in' type='checkbox'><label for='data-fef22357-6092-4c7c-9d7e-28afeb3d3171' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>198</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>src</dd><dt><span>GRIB_units :</span></dt><dd>m of water equivalent</dd><dt><span>GRIB_name :</span></dt><dd>Skin reservoir content</dd><dt><span>GRIB_cfVarName :</span></dt><dd>src</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd><dt><span>long_name :</span></dt><dd>Skin reservoir content</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d19ead0f-1012-4377-8aa7-e9671f9dba14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d19ead0f-1012-4377-8aa7-e9671f9dba14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d8efc31-55d1-45ab-95b0-b002af015061' class='xr-var-data-in' type='checkbox'><label for='data-5d8efc31-55d1-45ab-95b0-b002af015061' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>8</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Surface runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Surface runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-69a2b7de-9ce6-423a-a116-169e6aafece7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69a2b7de-9ce6-423a-a116-169e6aafece7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf959aa1-8683-4e0e-8001-72580beed72e' class='xr-var-data-in' type='checkbox'><label for='data-bf959aa1-8683-4e0e-8001-72580beed72e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>146</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sshf</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Time-integrated surface sensible heat net flux</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_upward_sensible_heat_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sshf</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Time-integrated surface sensible heat net flux</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b39a7655-4a15-4fa4-a67d-1484a861f62b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b39a7655-4a15-4fa4-a67d-1484a861f62b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dcc00cd-5fd1-4e33-809c-e9caa121b615' class='xr-var-data-in' type='checkbox'><label for='data-7dcc00cd-5fd1-4e33-809c-e9caa121b615' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>176</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net short-wave (solar) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2c0097f3-fd04-464c-98ce-9608a33477cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c0097f3-fd04-464c-98ce-9608a33477cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4eeb90c-adee-4bae-9f77-812a851c9099' class='xr-var-data-in' type='checkbox'><label for='data-d4eeb90c-adee-4bae-9f77-812a851c9099' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>210</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net short-wave (solar) radiation, clear sky</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 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80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net short-wave (solar) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-33df8cd8-a6d0-4143-8273-1f8253179392' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33df8cd8-a6d0-4143-8273-1f8253179392' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb590776-e61a-45df-b3cc-2847ecc85b59' class='xr-var-data-in' type='checkbox'><label for='data-eb590776-e61a-45df-b3cc-2847ecc85b59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>169</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssrd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface short-wave (solar) radiation downwards</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_downwelling_shortwave_flux_in_air</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssrd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 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1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface short-wave (solar) radiation downwards</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssro</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3e75fcd2-8a28-4e6a-8bdd-df23ac8c9dfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e75fcd2-8a28-4e6a-8bdd-df23ac8c9dfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23f7d5c9-96bb-43c4-9f22-cec672cc782d' class='xr-var-data-in' type='checkbox'><label for='data-23f7d5c9-96bb-43c4-9f22-cec672cc782d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>9</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ssro</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Sub-surface runoff</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ssro</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 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1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Sub-surface runoff</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c4028cc2-4900-458c-b660-c7b505c3650d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4028cc2-4900-458c-b660-c7b505c3650d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc801e0f-8ff2-4353-9a1f-759dd05568e2' class='xr-var-data-in' type='checkbox'><label for='data-cc801e0f-8ff2-4353-9a1f-759dd05568e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>34</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sst</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Sea surface temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sst</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Sea surface temperature</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9894bafd-79ae-441a-b622-c404f2ee02ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9894bafd-79ae-441a-b622-c404f2ee02ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3ba0121-48c9-43bd-8d32-f802e62faa54' class='xr-var-data-in' type='checkbox'><label for='data-f3ba0121-48c9-43bd-8d32-f802e62faa54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>139</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl1</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 1</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_temperature</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dfeb7a51-4612-48b0-ac8a-8fae8b4b4d1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfeb7a51-4612-48b0-ac8a-8fae8b4b4d1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c23d4c45-ac5c-4d7b-917f-df5a7675a370' class='xr-var-data-in' type='checkbox'><label for='data-c23d4c45-ac5c-4d7b-917f-df5a7675a370' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>170</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl2</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-87d37e6d-20c7-4179-8e13-65295aa4fb51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87d37e6d-20c7-4179-8e13-65295aa4fb51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c98db0b-2518-40f4-a2fb-6a9af8d45ff7' class='xr-var-data-in' type='checkbox'><label for='data-7c98db0b-2518-40f4-a2fb-6a9af8d45ff7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>183</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl3</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0fc7dccd-fca3-47d9-95b7-0b3b1e4c74ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fc7dccd-fca3-47d9-95b7-0b3b1e4c74ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d03888d5-0bd5-4096-bc21-828751fab515' class='xr-var-data-in' type='checkbox'><label for='data-d03888d5-0bd5-4096-bc21-828751fab515' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>236</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>stl4</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Soil temperature level 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>stl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Soil temperature level 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-70bf0fe4-0259-43e3-84cf-8ad4c5a359fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70bf0fe4-0259-43e3-84cf-8ad4c5a359fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59ffc71e-9414-4678-938e-d9aa3f2be43d' class='xr-var-data-in' type='checkbox'><label for='data-59ffc71e-9414-4678-938e-d9aa3f2be43d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>177</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>str</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net long-wave (thermal) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_upward_longwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>str</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net long-wave (thermal) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7c30ebcd-0a9e-4297-b3ca-6814cf1d7d28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c30ebcd-0a9e-4297-b3ca-6814cf1d7d28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f821d5f2-1741-4ce5-9b1d-df5efce878a1' class='xr-var-data-in' type='checkbox'><label for='data-f821d5f2-1741-4ce5-9b1d-df5efce878a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>211</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>strc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface net long-wave (thermal) radiation, clear sky</dd><dt><span>GRIB_cfName :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>strc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 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1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface net long-wave (thermal) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-040320c5-3f30-4746-8882-76aeaee666f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-040320c5-3f30-4746-8882-76aeaee666f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-425b71ad-2563-45f5-b46e-9404c6425dd9' class='xr-var-data-in' type='checkbox'><label for='data-425b71ad-2563-45f5-b46e-9404c6425dd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>175</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>strd</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface long-wave (thermal) radiation downwards</dd><dt><span>GRIB_cfVarName :</span></dt><dd>strd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface long-wave (thermal) radiation downwards</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sund</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-108fe5b6-5f8d-454e-8e3c-b089e093972e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-108fe5b6-5f8d-454e-8e3c-b089e093972e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ee44a01-ffea-476c-a9f8-793d9223b4ff' class='xr-var-data-in' type='checkbox'><label for='data-9ee44a01-ffea-476c-a9f8-793d9223b4ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>189</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>sund</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Sunshine duration</dd><dt><span>GRIB_cfVarName :</span></dt><dd>sund</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Sunshine duration</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e2e09ccf-f8ad-4c0f-aa86-390005a5d2c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2e09ccf-f8ad-4c0f-aa86-390005a5d2c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-193683bf-1d85-4c70-87e6-5d4850e79d55' class='xr-var-data-in' type='checkbox'><label for='data-193683bf-1d85-4c70-87e6-5d4850e79d55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>39</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl1</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 1</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed8caee6-eea7-44ee-869e-52bf13844d55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed8caee6-eea7-44ee-869e-52bf13844d55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91232af1-3e54-4053-a788-35233909b98a' class='xr-var-data-in' type='checkbox'><label for='data-91232af1-3e54-4053-a788-35233909b98a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>40</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl2</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 2</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-84ff2536-fa5b-454d-8fc5-75136429c4c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84ff2536-fa5b-454d-8fc5-75136429c4c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5656a123-4ec2-4354-8af7-1a079bfaa261' class='xr-var-data-in' type='checkbox'><label for='data-5656a123-4ec2-4354-8af7-1a079bfaa261' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>41</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl3</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 3</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e06f5ad9-d7b6-4909-b1ae-f96ff8304ece' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e06f5ad9-d7b6-4909-b1ae-f96ff8304ece' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dc2c464-505f-463f-8c72-a85fc63fb881' class='xr-var-data-in' type='checkbox'><label for='data-3dc2c464-505f-463f-8c72-a85fc63fb881' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>42</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>depthBelowLandLayer</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swvl4</dd><dt><span>GRIB_units :</span></dt><dd>m**3 m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swvl4</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 4</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b834d293-1649-4f63-b254-5fcf2b5e0591' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b834d293-1649-4f63-b254-5fcf2b5e0591' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e58bb41b-c155-4be7-9660-ec7dfdada8af' class='xr-var-data-in' type='checkbox'><label for='data-e58bb41b-c155-4be7-9660-ec7dfdada8af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>164</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcc</dd><dt><span>GRIB_units :</span></dt><dd>(0 - 1)</dd><dt><span>GRIB_name :</span></dt><dd>Total cloud cover</dd><dt><span>GRIB_cfName :</span></dt><dd>cloud_area_fraction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd><dt><span>long_name :</span></dt><dd>Total cloud cover</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-989d2205-f870-4e3d-bffb-26e84b894a1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-989d2205-f870-4e3d-bffb-26e84b894a1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fadb059-7bd5-4b85-9820-7bbec086dcb2' class='xr-var-data-in' type='checkbox'><label for='data-9fadb059-7bd5-4b85-9820-7bbec086dcb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>79</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tciw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud ice water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tciw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud ice water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d23eac4f-1a7f-4469-b2c6-44293ea63579' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d23eac4f-1a7f-4469-b2c6-44293ea63579' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ad8a139-f1a6-4d62-ae2d-acde068b150a' class='xr-var-data-in' type='checkbox'><label for='data-4ad8a139-f1a6-4d62-ae2d-acde068b150a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>78</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tclw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column cloud liquid water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tclw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column cloud liquid water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tco3</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-878bf64b-465e-4762-8018-e7edf247ff93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-878bf64b-465e-4762-8018-e7edf247ff93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11e74d62-062e-464b-bede-e6ea685a7a56' class='xr-var-data-in' type='checkbox'><label for='data-11e74d62-062e-464b-bede-e6ea685a7a56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>206</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tco3</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column ozone</dd><dt><span>GRIB_cfName :</span></dt><dd>atmosphere_mass_content_of_ozone</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tco3</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column ozone</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcrw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-17c1c60f-7549-4070-bd72-f9c957aea345' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17c1c60f-7549-4070-bd72-f9c957aea345' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a96447ee-9ae6-463d-9dcf-841e67c6f949' class='xr-var-data-in' type='checkbox'><label for='data-a96447ee-9ae6-463d-9dcf-841e67c6f949' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228089</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcrw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column rain water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcrw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column rain water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcsw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-373e1042-cceb-4636-ae41-5bb5b48e42ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-373e1042-cceb-4636-ae41-5bb5b48e42ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a018dce8-1471-497e-ab5e-83599f7ad7b1' class='xr-var-data-in' type='checkbox'><label for='data-a018dce8-1471-497e-ab5e-83599f7ad7b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228090</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcsw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column snow water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcsw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column snow water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d4866a1-f8fe-47e9-8b9a-8b6fbf8eebbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d4866a1-f8fe-47e9-8b9a-8b6fbf8eebbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0ec84b4-96a0-45c1-ab1c-1aefe452263f' class='xr-var-data-in' type='checkbox'><label for='data-d0ec84b4-96a0-45c1-ab1c-1aefe452263f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>136</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcw</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column water</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column water</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcwv</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a6a74512-23d1-4ef4-aaf6-d9b9d300a14c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6a74512-23d1-4ef4-aaf6-d9b9d300a14c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47045c14-2f0b-49ab-a5f0-bee8b1decfc9' class='xr-var-data-in' type='checkbox'><label for='data-47045c14-2f0b-49ab-a5f0-bee8b1decfc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>137</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tcwv</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Total column vertically-integrated water vapour</dd><dt><span>GRIB_cfName :</span></dt><dd>lwe_thickness_of_atmosphere_mass_content_of_water_vapor</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tcwv</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Total column vertically-integrated water vapour</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6abe2c55-36eb-479c-96eb-9ffa55dc7710' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6abe2c55-36eb-479c-96eb-9ffa55dc7710' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f4691fc-3c29-4f5c-8e3a-05dc6f685d4d' class='xr-var-data-in' type='checkbox'><label for='data-4f4691fc-3c29-4f5c-8e3a-05dc6f685d4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>212</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tisr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>TOA incident short-wave (solar) radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tisr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>TOA incident short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>totalx</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2cce1683-80d0-4a88-9f8a-774ddd9bcd36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cce1683-80d0-4a88-9f8a-774ddd9bcd36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e488da63-ee53-4061-bae9-691c3ca17867' class='xr-var-data-in' type='checkbox'><label for='data-e488da63-ee53-4061-bae9-691c3ca17867' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>260123</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>totalx</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Total totals index</dd><dt><span>GRIB_cfVarName :</span></dt><dd>totalx</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian latitude/longitude</dd><dt><span>GRIB_forecastTime :</span></dt><dd>240</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Total totals index</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fa0636e3-785a-47a6-9666-b1aa8b05d4f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa0636e3-785a-47a6-9666-b1aa8b05d4f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57f5c2ec-c39a-407e-8111-320563b48349' class='xr-var-data-in' type='checkbox'><label for='data-57f5c2ec-c39a-407e-8111-320563b48349' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>228</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tp</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Total precipitation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Total precipitation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-262d81af-95e8-4aca-a945-43bfe8858333' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-262d81af-95e8-4aca-a945-43bfe8858333' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-690e2219-6dd0-4dc4-97d3-4d2fe9e79a5b' class='xr-var-data-in' type='checkbox'><label for='data-690e2219-6dd0-4dc4-97d3-4d2fe9e79a5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>238</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsn</dd><dt><span>GRIB_units :</span></dt><dd>K</dd><dt><span>GRIB_name :</span></dt><dd>Temperature of snow layer</dd><dt><span>GRIB_cfName :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsn</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>long_name :</span></dt><dd>Temperature of snow layer</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-117d4978-bbd3-4c45-a50d-267633a35e39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-117d4978-bbd3-4c45-a50d-267633a35e39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d8b1496-1317-4dd7-ae6e-5a92302d478b' class='xr-var-data-in' type='checkbox'><label for='data-1d8b1496-1317-4dd7-ae6e-5a92302d478b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>178</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net short-wave (solar) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>toa_net_upward_shortwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net short-wave (solar) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-15af2807-f50b-4d5b-b341-2177bb4cf52e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15af2807-f50b-4d5b-b341-2177bb4cf52e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d9fe9c4-172a-4c7a-8029-7ee6d4f2d03c' class='xr-var-data-in' type='checkbox'><label for='data-5d9fe9c4-172a-4c7a-8029-7ee6d4f2d03c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>208</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tsrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net short-wave (solar) radiation, clear sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tsrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net short-wave (solar) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0e147062-27b9-4902-b4cf-2707254b987a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e147062-27b9-4902-b4cf-2707254b987a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12f20852-d3d5-4eb6-8ffc-3f011a0d1a12' class='xr-var-data-in' type='checkbox'><label for='data-12f20852-d3d5-4eb6-8ffc-3f011a0d1a12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>179</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ttr</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net long-wave (thermal) radiation</dd><dt><span>GRIB_cfName :</span></dt><dd>toa_outgoing_longwave_flux</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ttr</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net long-wave (thermal) radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttrc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-275c473e-7c97-48f1-b726-e1aed36ff054' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-275c473e-7c97-48f1-b726-e1aed36ff054' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47ecd539-38c9-4fd5-ae12-2a098d9f5dc2' class='xr-var-data-in' type='checkbox'><label for='data-47ecd539-38c9-4fd5-ae12-2a098d9f5dc2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>209</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ttrc</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Top net long-wave (thermal) radiation, clear sky</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ttrc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Top net long-wave (thermal) radiation, clear sky</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uvb</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b4d34abd-a0fb-4d9f-91e8-2b0064f3c997' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4d34abd-a0fb-4d9f-91e8-2b0064f3c997' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f60e2c32-a003-4833-b916-aa6b8fbc6684' class='xr-var-data-in' type='checkbox'><label for='data-f60e2c32-a003-4833-b916-aa6b8fbc6684' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>57</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>uvb</dd><dt><span>GRIB_units :</span></dt><dd>J m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Surface downward UV radiation</dd><dt><span>GRIB_cfVarName :</span></dt><dd>uvb</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>J m**-2</dd><dt><span>long_name :</span></dt><dd>Surface downward UV radiation</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vimd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5dee6a63-47b3-46fb-a964-5c3b29d4d59e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dee6a63-47b3-46fb-a964-5c3b29d4d59e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69200937-cbf2-43fa-ba5b-537a7221bd1e' class='xr-var-data-in' type='checkbox'><label for='data-69200937-cbf2-43fa-ba5b-537a7221bd1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>213</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vimd</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-2</dd><dt><span>GRIB_name :</span></dt><dd>Vertically integrated moisture divergence</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vimd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>8</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>kg m**-2</dd><dt><span>long_name :</span></dt><dd>Vertically integrated moisture divergence</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vis</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-04da1a23-8ab5-4617-a027-76d12b4718e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04da1a23-8ab5-4617-a027-76d12b4718e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4911a9f9-ac53-452d-8a35-4f2f5b18b867' class='xr-var-data-in' type='checkbox'><label for='data-4911a9f9-ac53-452d-8a35-4f2f5b18b867' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>3020</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vis</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Visibility</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vis</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 1292, 1288, 1284, 1280, 1276, 1272, 1268, 1264, 1260, 1256, 1252, 1248, 1244, 1240, 1236, 1232, 1228, 1224, 1220, 1216, 1212, 1208, 1204, 1200, 1196, 1192, 1188, 1184, 1180, 1176, 1172, 1168, 1164, 1160, 1156, 1152, 1148, 1144, 1140, 1136, 1132, 1128, 1124, 1120, 1116, 1112, 1108, 1104, 1100, 1096, 1092, 1088, 1084, 1080, 1076, 1072, 1068, 1064, 1060, 1056, 1052, 1048, 1044, 1040, 1036, 1032, 1028, 1024, 1020, 1016, 1012, 1008, 1004, 1000, 996, 992, 988, 984, 980, 976, 972, 968, 964, 960, 956, 952, 948, 944, 940, 936, 932, 928, 924, 920, 916, 912, 908, 904, 900, 896, 892, 888, 884, 880, 876, 872, 868, 864, 860, 856, 852, 848, 844, 840, 836, 832, 828, 824, 820, 816, 812, 808, 804, 800, 796, 792, 788, 784, 780, 776, 772, 768, 764, 760, 756, 752, 748, 744, 740, 736, 732, 728, 724, 720, 716, 712, 708, 704, 700, 696, 692, 688, 684, 680, 676, 672, 668, 664, 660, 656, 652, 648, 644, 640, 636, 632, 628, 624, 620, 616, 612, 608, 604, 600, 596, 592, 588, 584, 580, 576, 572, 568, 564, 560, 556, 552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Visibility</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f817713c-2172-4960-a84a-004efac6c3bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f817713c-2172-4960-a84a-004efac6c3bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd259518-bc1a-4254-972a-a12f9a6f568b' class='xr-var-data-in' type='checkbox'><label for='data-cd259518-bc1a-4254-972a-a12f9a6f568b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>129</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>654400</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>surface</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_gg</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>z</dd><dt><span>GRIB_units :</span></dt><dd>m**2 s**-2</dd><dt><span>GRIB_name :</span></dt><dd>Geopotential</dd><dt><span>GRIB_cfName :</span></dt><dd>geopotential</dd><dt><span>GRIB_cfVarName :</span></dt><dd>z</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Gaussian Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_N :</span></dt><dd>400</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>0</dd><dt><span>GRIB_P1 :</span></dt><dd>240</dd><dt><span>GRIB_P2 :</span></dt><dd>0</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[20, 24, 28, 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72, 76, 80, 84, 88, 92, 96, 100, 104, 108, 112, 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 160, 164, 168, 172, 176, 180, 184, 188, 192, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276, 280, 284, 288, 292, 296, 300, 304, 308, 312, 316, 320, 324, 328, 332, 336, 340, 344, 348, 352, 356, 360, 364, 368, 372, 376, 380, 384, 388, 392, 396, 400, 404, 408, 412, 416, 420, 424, 428, 432, 436, 440, 444, 448, 452, 456, 460, 464, 468, 472, 476, 480, 484, 488, 492, 496, 500, 504, 508, 512, 516, 520, 524, 528, 532, 536, 540, 544, 548, 552, 556, 560, 564, 568, 572, 576, 580, 584, 588, 592, 596, 600, 604, 608, 612, 616, 620, 624, 628, 632, 636, 640, 644, 648, 652, 656, 660, 664, 668, 672, 676, 680, 684, 688, 692, 696, 700, 704, 708, 712, 716, 720, 724, 728, 732, 736, 740, 744, 748, 752, 756, 760, 764, 768, 772, 776, 780, 784, 788, 792, 796, 800, 804, 808, 812, 816, 820, 824, 828, 832, 836, 840, 844, 848, 852, 856, 860, 864, 868, 872, 876, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 964, 968, 972, 976, 980, 984, 988, 992, 996, 1000, 1004, 1008, 1012, 1016, 1020, 1024, 1028, 1032, 1036, 1040, 1044, 1048, 1052, 1056, 1060, 1064, 1068, 1072, 1076, 1080, 1084, 1088, 1092, 1096, 1100, 1104, 1108, 1112, 1116, 1120, 1124, 1128, 1132, 1136, 1140, 1144, 1148, 1152, 1156, 1160, 1164, 1168, 1172, 1176, 1180, 1184, 1188, 1192, 1196, 1200, 1204, 1208, 1212, 1216, 1220, 1224, 1228, 1232, 1236, 1240, 1244, 1248, 1252, 1256, 1260, 1264, 1268, 1272, 1276, 1280, 1284, 1288, 1292, 1296, 1300, 1304, 1308, 1312, 1316, 1320, 1324, 1328, 1332, 1336, 1340, 1344, 1348, 1352, 1356, 1360, 1364, 1368, 1372, 1376, 1380, 1384, 1388, 1392, 1396, 1400, 1404, 1408, 1412, 1416, 1420, 1424, 1428, 1432, 1436, 1440, 1444, 1448, 1452, 1456, 1460, 1464, 1468, 1472, 1476, 1480, 1484, 1488, 1492, 1496, 1500, 1504, 1508, 1512, 1516, 1520, 1524, 1528, 1532, 1536, 1540, 1544, 1548, 1552, 1556, 1560, 1564, 1568, 1572, 1576, 1580, 1584, 1588, 1592, 1596, 1600, 1604, 1608, 1612, 1616, 1616, 1612, 1608, 1604, 1600, 1596, 1592, 1588, 1584, 1580, 1576, 1572, 1568, 1564, 1560, 1556, 1552, 1548, 1544, 1540, 1536, 1532, 1528, 1524, 1520, 1516, 1512, 1508, 1504, 1500, 1496, 1492, 1488, 1484, 1480, 1476, 1472, 1468, 1464, 1460, 1456, 1452, 1448, 1444, 1440, 1436, 1432, 1428, 1424, 1420, 1416, 1412, 1408, 1404, 1400, 1396, 1392, 1388, 1384, 1380, 1376, 1372, 1368, 1364, 1360, 1356, 1352, 1348, 1344, 1340, 1336, 1332, 1328, 1324, 1320, 1316, 1312, 1308, 1304, 1300, 1296, 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552, 548, 544, 540, 536, 532, 528, 524, 520, 516, 512, 508, 504, 500, 496, 492, 488, 484, 480, 476, 472, 468, 464, 460, 456, 452, 448, 444, 440, 436, 432, 428, 424, 420, 416, 412, 408, 404, 400, 396, 392, 388, 384, 380, 376, 372, 368, 364, 360, 356, 352, 348, 344, 340, 336, 332, 328, 324, 320, 316, 312, 308, 304, 300, 296, 292, 288, 284, 280, 276, 272, 268, 264, 260, 256, 252, 248, 244, 240, 236, 232, 228, 224, 220, 216, 212, 208, 204, 200, 196, 192, 188, 184, 180, 176, 172, 168, 164, 160, 156, 152, 148, 144, 140, 136, 132, 128, 124, 120, 116, 112, 108, 104, 100, 96, 92, 88, 84, 80, 76, 72, 68, 64, 60, 56, 52, 48, 44, 40, 36, 32, 28, 24, 20]</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd><dt><span>long_name :</span></dt><dd>Geopotential</dd></dl></div><div class='xr-var-data'><pre>[13742400 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5e75b8de-e629-456a-b604-213edcb12404' class='xr-section-summary-in' type='checkbox'  ><label for='section-5e75b8de-e629-456a-b604-213edcb12404' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9940b0a3-0f9d-4c32-bace-1c89b323a1b5' class='xr-index-data-in' type='checkbox'/><label for='index-9940b0a3-0f9d-4c32-bace-1c89b323a1b5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4f2af43-b467-44ef-a6a6-dac79381eae8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f4f2af43-b467-44ef-a6a6-dac79381eae8' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>2</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
           },
           "metadata": {}
         }
@@ -409,7 +404,7 @@
     {
       "id": "658ac441-3d9d-46ab-9e5a-d4c5a159679b",
       "cell_type": "markdown",
-      "source": "The wave model data is stored on a **reduced_ll** grid and can be loaded as in the following example.\n",
+      "source": "The wave model data are stored on a **reduced_ll** grid and can be loaded as in the following example.\n",
       "metadata": {}
     },
     {
@@ -430,7 +425,7 @@
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 958MB\nDimensions:  (time: 21, value: 315258)\nCoordinates:\n    lat      (value) float64 3MB ...\n    lon      (value) float64 3MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables: (12/18)\n    cdww     (time, value) float64 53MB ...\n    dwi      (time, value) float64 53MB ...\n    hmax     (time, value) float64 53MB ...\n    mp1      (time, value) float64 53MB ...\n    mp2      (time, value) float64 53MB ...\n    mwd      (time, value) float64 53MB ...\n    ...       ...\n    ust      (time, value) float64 53MB ...\n    vst      (time, value) float64 53MB ...\n    wdw      (time, value) float64 53MB ...\n    wind     (time, value) float64 53MB ...\n    wmb      (time, value) float64 53MB ...\n    wstar    (time, value) float64 53MB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8105cfed-3f2d-4e7d-a850-8be5c6b5b2d6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8105cfed-3f2d-4e7d-a850-8be5c6b5b2d6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span>value</span>: 315258</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f257bf13-6edd-4102-b6d5-c3b2b9693858' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f257bf13-6edd-4102-b6d5-c3b2b9693858' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ef55012-4ee5-4c93-82e1-a61330efe517' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ef55012-4ee5-4c93-82e1-a61330efe517' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37c24a6c-66a5-4b7f-9d30-2efce3db7d44' class='xr-var-data-in' type='checkbox'><label for='data-37c24a6c-66a5-4b7f-9d30-2efce3db7d44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[315258 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0e7c571a-105d-49a3-9eee-399e47243e8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e7c571a-105d-49a3-9eee-399e47243e8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc7d8872-ab86-4404-b05a-e261e7bfd9e9' class='xr-var-data-in' type='checkbox'><label for='data-fc7d8872-ab86-4404-b05a-e261e7bfd9e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[315258 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-c4a9c41b-ea28-45da-920b-d7d4f8405df6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4a9c41b-ea28-45da-920b-d7d4f8405df6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2edcbc56-7d74-4025-b20d-96d7ec1478c7' class='xr-var-data-in' type='checkbox'><label for='data-2edcbc56-7d74-4025-b20d-96d7ec1478c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4f309929-5349-44c4-a14e-52badffde462' class='xr-section-summary-in' type='checkbox'  ><label for='section-4f309929-5349-44c4-a14e-52badffde462' class='xr-section-summary' >Data variables: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cdww</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4029d5cb-c983-4a4f-93bb-a9858463acb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4029d5cb-c983-4a4f-93bb-a9858463acb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93bb45ef-2795-48c8-868e-55089693207c' class='xr-var-data-in' type='checkbox'><label for='data-93bb45ef-2795-48c8-868e-55089693207c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140233</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cdww</dd><dt><span>GRIB_units :</span></dt><dd>dimensionless</dd><dt><span>GRIB_name :</span></dt><dd>Coefficient of drag with waves</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cdww</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>dimensionless</dd><dt><span>long_name :</span></dt><dd>Coefficient of drag with waves</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dwi</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe22f2d5-b77b-424e-8f20-696765dca6a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe22f2d5-b77b-424e-8f20-696765dca6a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8850d462-00a8-4440-8192-49111ae82f4c' class='xr-var-data-in' type='checkbox'><label for='data-8850d462-00a8-4440-8192-49111ae82f4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140249</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>dwi</dd><dt><span>GRIB_units :</span></dt><dd>degrees</dd><dt><span>GRIB_name :</span></dt><dd>10 metre wind direction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dwi</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>10 metre wind direction</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hmax</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-87ca1bb3-a2fb-46c1-bbd2-dc3d82e8a70a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87ca1bb3-a2fb-46c1-bbd2-dc3d82e8a70a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15b7c019-bdca-435d-8457-f9617b665425' class='xr-var-data-in' type='checkbox'><label for='data-15b7c019-bdca-435d-8457-f9617b665425' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140218</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>hmax</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Envelop-maximum individual wave height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hmax</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Envelop-maximum individual wave height</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mp1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d0d4caa8-c91c-4215-8d66-45c5111bce63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0d4caa8-c91c-4215-8d66-45c5111bce63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44a27e2e-3fd8-4b3a-8385-ddad347ca406' class='xr-var-data-in' type='checkbox'><label for='data-44a27e2e-3fd8-4b3a-8385-ddad347ca406' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140220</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mp1</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave period based on first moment</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mp1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean wave period based on first moment</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mp2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b3551d0b-2e45-46ab-bf26-06df25ad9063' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3551d0b-2e45-46ab-bf26-06df25ad9063' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83f9ae82-e126-4691-ac3e-7c420da750d0' class='xr-var-data-in' type='checkbox'><label for='data-83f9ae82-e126-4691-ac3e-7c420da750d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140221</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mp2</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean zero-crossing wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mp2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean zero-crossing wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mwd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1c8095f3-236c-4f33-9ae5-8f860d64f9e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c8095f3-236c-4f33-9ae5-8f860d64f9e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06203a28-54d6-4da7-8478-a0a766c45f3e' class='xr-var-data-in' type='checkbox'><label for='data-06203a28-54d6-4da7-8478-a0a766c45f3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140230</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mwd</dd><dt><span>GRIB_units :</span></dt><dd>Degree true</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave direction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mwd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Degree true</dd><dt><span>long_name :</span></dt><dd>Mean wave direction</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mwp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-97d2221c-6f0a-4609-ab3c-d54dc758e16d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97d2221c-6f0a-4609-ab3c-d54dc758e16d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0a5af34-4115-490d-9847-14e8d5f035d8' class='xr-var-data-in' type='checkbox'><label for='data-b0a5af34-4115-490d-9847-14e8d5f035d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140232</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mwp</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mwp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phioc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1310c403-b780-4dda-b2c8-abb424cdfcd5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1310c403-b780-4dda-b2c8-abb424cdfcd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee7253d5-1f1d-483f-a779-47d6c10cc488' class='xr-var-data-in' type='checkbox'><label for='data-ee7253d5-1f1d-483f-a779-47d6c10cc488' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140212</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>phioc</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Normalized energy flux into ocean</dd><dt><span>GRIB_cfVarName :</span></dt><dd>phioc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Normalized energy flux into ocean</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pp1d</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9d2a128-5503-4312-8d95-590acc4c6af8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d2a128-5503-4312-8d95-590acc4c6af8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4e84d0c-3adb-42d5-ba74-9b81ebf1c409' class='xr-var-data-in' type='checkbox'><label for='data-e4e84d0c-3adb-42d5-ba74-9b81ebf1c409' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140231</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>pp1d</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Peak wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>pp1d</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Peak wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rhoao</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-61e96263-b167-46d3-a64f-da78076a484d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61e96263-b167-46d3-a64f-da78076a484d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-994e354e-46ec-4195-b9f2-6cbc0b15bd15' class='xr-var-data-in' type='checkbox'><label for='data-994e354e-46ec-4195-b9f2-6cbc0b15bd15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140209</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>rhoao</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Air density over the oceans</dd><dt><span>GRIB_cfVarName :</span></dt><dd>rhoao</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>kg m**-3</dd><dt><span>long_name :</span></dt><dd>Air density over the oceans</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swh</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e9680dc2-c9bb-4371-aa64-457a32b5767a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9680dc2-c9bb-4371-aa64-457a32b5767a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc50ad2a-4967-4a3f-8f4d-0b036310cc5a' class='xr-var-data-in' type='checkbox'><label for='data-cc50ad2a-4967-4a3f-8f4d-0b036310cc5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140229</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swh</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Significant height of combined wind waves and swell</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swh</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Significant height of combined wind waves and swell</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tauoc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d2fd8cba-5d81-4a60-9c6d-634c47ebb317' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2fd8cba-5d81-4a60-9c6d-634c47ebb317' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6a28518-f08e-41f3-a34f-071776d99005' class='xr-var-data-in' type='checkbox'><label for='data-d6a28518-f08e-41f3-a34f-071776d99005' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140214</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tauoc</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Normalized stress into ocean</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tauoc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Normalized stress into ocean</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ust</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c4337b14-88f5-4432-a55a-892729cbdd3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4337b14-88f5-4432-a55a-892729cbdd3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04762794-8824-4d71-9a94-875b1772234f' class='xr-var-data-in' type='checkbox'><label for='data-04762794-8824-4d71-9a94-875b1772234f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140215</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ust</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>U-component surface stokes drift</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ust</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>U-component surface stokes drift</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vst</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b96a8188-2481-4c84-97af-4f9dc395a103' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b96a8188-2481-4c84-97af-4f9dc395a103' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46fa62aa-a8cf-411f-8e56-945fcc05a28d' class='xr-var-data-in' type='checkbox'><label for='data-46fa62aa-a8cf-411f-8e56-945fcc05a28d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140216</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vst</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>V-component surface stokes drift</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vst</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>V-component surface stokes drift</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wdw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ff5cd1a6-cf86-4373-b56b-0731b174a775' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff5cd1a6-cf86-4373-b56b-0731b174a775' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe4b550c-2336-466e-8ded-0b9a164418ca' class='xr-var-data-in' type='checkbox'><label for='data-fe4b550c-2336-466e-8ded-0b9a164418ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140222</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wdw</dd><dt><span>GRIB_units :</span></dt><dd>radians</dd><dt><span>GRIB_name :</span></dt><dd>Wave spectral directional width</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wdw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>radians</dd><dt><span>long_name :</span></dt><dd>Wave spectral directional width</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-281124df-5411-44f5-b63a-06c5563f56ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-281124df-5411-44f5-b63a-06c5563f56ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1410bd4-43b7-433c-987f-cfce33e9c483' class='xr-var-data-in' type='checkbox'><label for='data-f1410bd4-43b7-433c-987f-cfce33e9c483' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140245</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wind</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre wind speed</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wind</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre wind speed</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wmb</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cc050fd1-bf53-44bc-ad2d-b65a4925c14e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc050fd1-bf53-44bc-ad2d-b65a4925c14e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e784c9e6-4b38-49dd-b07a-6699baf553c5' class='xr-var-data-in' type='checkbox'><label for='data-e784c9e6-4b38-49dd-b07a-6699baf553c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140219</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wmb</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Model bathymetry</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wmb</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Model bathymetry</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wstar</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-68895f68-4f1f-440a-9d3e-409b3e188601' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68895f68-4f1f-440a-9d3e-409b3e188601' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37b8255-3e8d-40a9-8d2b-a82db87af10e' class='xr-var-data-in' type='checkbox'><label for='data-d37b8255-3e8d-40a9-8d2b-a82db87af10e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140208</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wstar</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Free convective velocity over the oceans</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wstar</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Free convective velocity over the oceans</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f7d85a4-fc6a-4813-831c-4a7bf3c49722' class='xr-section-summary-in' type='checkbox'  ><label for='section-7f7d85a4-fc6a-4813-831c-4a7bf3c49722' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8401f7d-2872-4a5b-bf08-6e38359f9c32' class='xr-index-data-in' type='checkbox'/><label for='index-b8401f7d-2872-4a5b-bf08-6e38359f9c32' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c007c16e-5b23-4c25-8f3a-bf89112ccbbc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c007c16e-5b23-4c25-8f3a-bf89112ccbbc' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
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li:nth-child(odd) > .xr-var-name span {\n  background-color: var(--xr-background-color-row-odd);\n}\n\n.xr-var-name {\n  grid-column: 1;\n}\n\n.xr-var-dims {\n  grid-column: 2;\n}\n\n.xr-var-dtype {\n  grid-column: 3;\n  text-align: right;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-preview {\n  grid-column: 4;\n}\n\n.xr-index-preview {\n  grid-column: 2 / 5;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-name,\n.xr-var-dims,\n.xr-var-dtype,\n.xr-preview,\n.xr-attrs dt {\n  white-space: nowrap;\n  overflow: hidden;\n  text-overflow: ellipsis;\n  padding-right: 10px;\n}\n\n.xr-var-name:hover,\n.xr-var-dims:hover,\n.xr-var-dtype:hover,\n.xr-attrs dt:hover {\n  overflow: visible;\n  width: auto;\n  z-index: 1;\n}\n\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  display: none;\n  background-color: var(--xr-background-color) !important;\n  padding-bottom: 5px !important;\n}\n\n.xr-var-attrs-in:checked ~ .xr-var-attrs,\n.xr-var-data-in:checked ~ .xr-var-data,\n.xr-index-data-in:checked ~ .xr-index-data {\n  display: block;\n}\n\n.xr-var-data > table {\n  float: right;\n}\n\n.xr-var-name span,\n.xr-var-data,\n.xr-index-name div,\n.xr-index-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2,\n.xr-no-icon {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 958MB\nDimensions:  (time: 21, value: 315258)\nCoordinates:\n    lat      (value) float64 3MB ...\n    lon      (value) float64 3MB ...\n  * time     (time) datetime64[ns] 168B 2020-07-21 ... 2020-07-31\nDimensions without coordinates: value\nData variables: (12/18)\n    cdww     (time, value) float64 53MB ...\n    dwi      (time, value) float64 53MB ...\n    hmax     (time, value) float64 53MB ...\n    mp1      (time, value) float64 53MB ...\n    mp2      (time, value) float64 53MB ...\n    mwd      (time, value) float64 53MB ...\n    ...       ...\n    ust      (time, value) float64 53MB ...\n    vst      (time, value) float64 53MB ...\n    wdw      (time, value) float64 53MB ...\n    wind     (time, value) float64 53MB ...\n    wmb      (time, value) float64 53MB ...\n    wstar    (time, value) float64 53MB ...\nAttributes:\n    GRIB_edition:            1\n    GRIB_centre:             ecmf\n    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n    GRIB_subCentre:          0\n    history:                 🪄🧙‍♂️🔮 magic dataset assembly provided by gribsc...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4ea5733a-4e04-4d6e-853b-ed98d076fa8c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4ea5733a-4e04-4d6e-853b-ed98d076fa8c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 21</li><li><span>value</span>: 315258</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-96c3bd81-aa95-4219-bc18-30e9ed909893' class='xr-section-summary-in' type='checkbox'  checked><label for='section-96c3bd81-aa95-4219-bc18-30e9ed909893' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a67d27a7-b600-42f0-bc2a-d7ba97331eb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a67d27a7-b600-42f0-bc2a-d7ba97331eb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-246a9aeb-ebfc-4cd3-b842-0d4407e7f819' class='xr-var-data-in' type='checkbox'><label for='data-246a9aeb-ebfc-4cd3-b842-0d4407e7f819' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[315258 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-44d9d1a2-784e-4001-97b2-aca9c3199613' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44d9d1a2-784e-4001-97b2-aca9c3199613' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b455b148-d9c9-4532-9640-4f1f6311f3ae' class='xr-var-data-in' type='checkbox'><label for='data-b455b148-d9c9-4532-9640-4f1f6311f3ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>GRIB_units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[315258 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-07-21 ... 2020-07-31</div><input id='attrs-36792981-1c8f-4eb5-b828-c45ad35a7036' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36792981-1c8f-4eb5-b828-c45ad35a7036' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e11f22b3-c002-47ce-ae5d-ecc0ac277d4a' class='xr-var-data-in' type='checkbox'><label for='data-e11f22b3-c002-47ce-ae5d-ecc0ac277d4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_units :</span></dt><dd>seconds since 1970-01-01T00:00:00</dd><dt><span>GRIB_calendar :</span></dt><dd>proleptic_gregorian</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-07-21T00:00:00.000000000&#x27;, &#x27;2020-07-21T12:00:00.000000000&#x27;,\n       &#x27;2020-07-22T00:00:00.000000000&#x27;, &#x27;2020-07-22T12:00:00.000000000&#x27;,\n       &#x27;2020-07-23T00:00:00.000000000&#x27;, &#x27;2020-07-23T12:00:00.000000000&#x27;,\n       &#x27;2020-07-24T00:00:00.000000000&#x27;, &#x27;2020-07-24T12:00:00.000000000&#x27;,\n       &#x27;2020-07-25T00:00:00.000000000&#x27;, &#x27;2020-07-25T12:00:00.000000000&#x27;,\n       &#x27;2020-07-26T00:00:00.000000000&#x27;, &#x27;2020-07-26T12:00:00.000000000&#x27;,\n       &#x27;2020-07-27T00:00:00.000000000&#x27;, &#x27;2020-07-27T12:00:00.000000000&#x27;,\n       &#x27;2020-07-28T00:00:00.000000000&#x27;, &#x27;2020-07-28T12:00:00.000000000&#x27;,\n       &#x27;2020-07-29T00:00:00.000000000&#x27;, &#x27;2020-07-29T12:00:00.000000000&#x27;,\n       &#x27;2020-07-30T00:00:00.000000000&#x27;, &#x27;2020-07-30T12:00:00.000000000&#x27;,\n       &#x27;2020-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-88cc0108-00cd-40b1-9137-1262927ff3a1' class='xr-section-summary-in' type='checkbox'  ><label for='section-88cc0108-00cd-40b1-9137-1262927ff3a1' class='xr-section-summary' >Data variables: <span>(18)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>cdww</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1b883a05-d68c-417b-ac39-7b90d6ee855d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b883a05-d68c-417b-ac39-7b90d6ee855d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b9a0fc9-bead-4284-9e8c-8d694c1e7024' class='xr-var-data-in' type='checkbox'><label for='data-3b9a0fc9-bead-4284-9e8c-8d694c1e7024' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140233</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>cdww</dd><dt><span>GRIB_units :</span></dt><dd>dimensionless</dd><dt><span>GRIB_name :</span></dt><dd>Coefficient of drag with waves</dd><dt><span>GRIB_cfVarName :</span></dt><dd>cdww</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>dimensionless</dd><dt><span>long_name :</span></dt><dd>Coefficient of drag with waves</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dwi</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0994d2af-af64-48ea-b80a-79ff19d78097' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0994d2af-af64-48ea-b80a-79ff19d78097' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8cc0a37-1e43-4946-bbad-1667434a01fd' class='xr-var-data-in' type='checkbox'><label for='data-f8cc0a37-1e43-4946-bbad-1667434a01fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140249</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>dwi</dd><dt><span>GRIB_units :</span></dt><dd>degrees</dd><dt><span>GRIB_name :</span></dt><dd>10 metre wind direction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>dwi</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>10 metre wind direction</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hmax</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-789b02f4-de11-4763-bcc9-23d2fec847f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-789b02f4-de11-4763-bcc9-23d2fec847f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-626e0f3e-a0d2-4209-a270-c94f8c89e8e8' class='xr-var-data-in' type='checkbox'><label for='data-626e0f3e-a0d2-4209-a270-c94f8c89e8e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140218</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>hmax</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Envelop-maximum individual wave height</dd><dt><span>GRIB_cfVarName :</span></dt><dd>hmax</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Envelop-maximum individual wave height</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mp1</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b3a1e4fc-902b-4d58-a197-0c26cbc6e5ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3a1e4fc-902b-4d58-a197-0c26cbc6e5ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efb9a309-4d61-4ec6-8855-fe7f83f5dd54' class='xr-var-data-in' type='checkbox'><label for='data-efb9a309-4d61-4ec6-8855-fe7f83f5dd54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140220</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mp1</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave period based on first moment</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mp1</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean wave period based on first moment</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mp2</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8fc7cc53-3e4f-4abd-86e5-b9882f54f69a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fc7cc53-3e4f-4abd-86e5-b9882f54f69a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-535af538-8486-4f7c-84e2-839a72804a49' class='xr-var-data-in' type='checkbox'><label for='data-535af538-8486-4f7c-84e2-839a72804a49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140221</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mp2</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean zero-crossing wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mp2</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean zero-crossing wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mwd</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c2ae5d3a-33ca-4764-878a-43b7cce8c974' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2ae5d3a-33ca-4764-878a-43b7cce8c974' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d0bb70d-ebb8-4917-a768-ba9cb7173935' class='xr-var-data-in' type='checkbox'><label for='data-8d0bb70d-ebb8-4917-a768-ba9cb7173935' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140230</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mwd</dd><dt><span>GRIB_units :</span></dt><dd>Degree true</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave direction</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mwd</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Degree true</dd><dt><span>long_name :</span></dt><dd>Mean wave direction</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mwp</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-81ec4963-bafe-4a6d-828d-962231e6e9b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81ec4963-bafe-4a6d-828d-962231e6e9b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-796fa08b-05c9-4335-9001-a09acb097aa0' class='xr-var-data-in' type='checkbox'><label for='data-796fa08b-05c9-4335-9001-a09acb097aa0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140232</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>mwp</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Mean wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>mwp</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Mean wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>phioc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cbf1614c-aef0-47ca-a55c-ce14b57e914e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbf1614c-aef0-47ca-a55c-ce14b57e914e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f45b1ad-8dc4-450d-9790-ca55e66bdcf4' class='xr-var-data-in' type='checkbox'><label for='data-8f45b1ad-8dc4-450d-9790-ca55e66bdcf4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140212</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>phioc</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Normalized energy flux into ocean</dd><dt><span>GRIB_cfVarName :</span></dt><dd>phioc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Normalized energy flux into ocean</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pp1d</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4af89ab0-d723-4dec-94f8-3822da85b632' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4af89ab0-d723-4dec-94f8-3822da85b632' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-512401d3-4207-4957-85ed-0f79ac44094a' class='xr-var-data-in' type='checkbox'><label for='data-512401d3-4207-4957-85ed-0f79ac44094a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140231</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>pp1d</dd><dt><span>GRIB_units :</span></dt><dd>s</dd><dt><span>GRIB_name :</span></dt><dd>Peak wave period</dd><dt><span>GRIB_cfVarName :</span></dt><dd>pp1d</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Peak wave period</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rhoao</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a0edf9dc-a2ed-4880-acc2-9928ca444709' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0edf9dc-a2ed-4880-acc2-9928ca444709' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e7ce128-6f7a-4a04-accd-a5e4eb97bcbf' class='xr-var-data-in' type='checkbox'><label for='data-9e7ce128-6f7a-4a04-accd-a5e4eb97bcbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140209</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>rhoao</dd><dt><span>GRIB_units :</span></dt><dd>kg m**-3</dd><dt><span>GRIB_name :</span></dt><dd>Air density over the oceans</dd><dt><span>GRIB_cfVarName :</span></dt><dd>rhoao</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>kg m**-3</dd><dt><span>long_name :</span></dt><dd>Air density over the oceans</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swh</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8b0112ef-2659-4467-b390-90e78dcd8a6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b0112ef-2659-4467-b390-90e78dcd8a6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37149231-84d8-4440-88e3-fdf016de4014' class='xr-var-data-in' type='checkbox'><label for='data-37149231-84d8-4440-88e3-fdf016de4014' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140229</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>swh</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Significant height of combined wind waves and swell</dd><dt><span>GRIB_cfVarName :</span></dt><dd>swh</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Significant height of combined wind waves and swell</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tauoc</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-26414ada-f24a-4301-b7dc-dbe56cf4053b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26414ada-f24a-4301-b7dc-dbe56cf4053b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f1afe14-2810-4959-b414-e2a6758fb5dc' class='xr-var-data-in' type='checkbox'><label for='data-3f1afe14-2810-4959-b414-e2a6758fb5dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140214</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>tauoc</dd><dt><span>GRIB_units :</span></dt><dd>Numeric</dd><dt><span>GRIB_name :</span></dt><dd>Normalized stress into ocean</dd><dt><span>GRIB_cfVarName :</span></dt><dd>tauoc</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>Numeric</dd><dt><span>long_name :</span></dt><dd>Normalized stress into ocean</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ust</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-99722ff6-1cc5-4183-b5f6-a45d3232116b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99722ff6-1cc5-4183-b5f6-a45d3232116b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0acbd1eb-a908-4f46-bdc9-58a45bcdcf19' class='xr-var-data-in' type='checkbox'><label for='data-0acbd1eb-a908-4f46-bdc9-58a45bcdcf19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140215</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>ust</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>U-component surface stokes drift</dd><dt><span>GRIB_cfVarName :</span></dt><dd>ust</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>U-component surface stokes drift</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vst</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eece6f07-f7bd-4f98-a108-2d11b7cb1407' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eece6f07-f7bd-4f98-a108-2d11b7cb1407' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c5e1de2-84c2-4453-a555-5d6b789a1521' class='xr-var-data-in' type='checkbox'><label for='data-1c5e1de2-84c2-4453-a555-5d6b789a1521' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140216</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>vst</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>V-component surface stokes drift</dd><dt><span>GRIB_cfVarName :</span></dt><dd>vst</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>V-component surface stokes drift</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wdw</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7633110f-d3d0-4a1f-aa82-6b6b1a2ff8c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7633110f-d3d0-4a1f-aa82-6b6b1a2ff8c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dda6530-7305-4436-b726-4f6183de8e52' class='xr-var-data-in' type='checkbox'><label for='data-2dda6530-7305-4436-b726-4f6183de8e52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140222</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wdw</dd><dt><span>GRIB_units :</span></dt><dd>radians</dd><dt><span>GRIB_name :</span></dt><dd>Wave spectral directional width</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wdw</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>radians</dd><dt><span>long_name :</span></dt><dd>Wave spectral directional width</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bef24d68-0257-4535-bbd4-0c00eaac85e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bef24d68-0257-4535-bbd4-0c00eaac85e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef6a2e66-e41c-4a6b-badb-2614b6414d4b' class='xr-var-data-in' type='checkbox'><label for='data-ef6a2e66-e41c-4a6b-badb-2614b6414d4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140245</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wind</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>10 metre wind speed</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wind</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>10 metre wind speed</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wmb</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cacc1fe2-8fed-4b35-8850-065188fa10d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cacc1fe2-8fed-4b35-8850-065188fa10d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a03d0fd-b1aa-416c-be11-da5148624a8a' class='xr-var-data-in' type='checkbox'><label for='data-1a03d0fd-b1aa-416c-be11-da5148624a8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140219</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wmb</dd><dt><span>GRIB_units :</span></dt><dd>m</dd><dt><span>GRIB_name :</span></dt><dd>Model bathymetry</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wmb</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Model bathymetry</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wstar</span></div><div class='xr-var-dims'>(time, value)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca6afa41-9c04-4fb0-b4ad-8e37faf1bd3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca6afa41-9c04-4fb0-b4ad-8e37faf1bd3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29c60c52-16c6-496f-b916-2a30b102af7d' class='xr-var-data-in' type='checkbox'><label for='data-29c60c52-16c6-496f-b916-2a30b102af7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>GRIB_paramId :</span></dt><dd>140208</dd><dt><span>GRIB_dataType :</span></dt><dd>fc</dd><dt><span>GRIB_numberOfPoints :</span></dt><dd>315258</dd><dt><span>GRIB_typeOfLevel :</span></dt><dd>meanSea</dd><dt><span>GRIB_stepUnits :</span></dt><dd>1</dd><dt><span>GRIB_stepType :</span></dt><dd>instant</dd><dt><span>GRIB_gridType :</span></dt><dd>reduced_ll</dd><dt><span>GRIB_uvRelativeToGrid :</span></dt><dd>0</dd><dt><span>GRIB_shortName :</span></dt><dd>wstar</dd><dt><span>GRIB_units :</span></dt><dd>m s**-1</dd><dt><span>GRIB_name :</span></dt><dd>Free convective velocity over the oceans</dd><dt><span>GRIB_cfVarName :</span></dt><dd>wstar</dd><dt><span>GRIB_missingValue :</span></dt><dd>9999</dd><dt><span>GRIB_totalNumber :</span></dt><dd>0</dd><dt><span>GRIB_NV :</span></dt><dd>0</dd><dt><span>GRIB_gridDefinitionDescription :</span></dt><dd>Latitude/Longitude Grid</dd><dt><span>GRIB_indicatorOfUnitOfTimeRange :</span></dt><dd>1</dd><dt><span>GRIB_productDefinitionTemplateNumber :</span></dt><dd>0</dd><dt><span>GRIB_timeRangeIndicator :</span></dt><dd>10</dd><dt><span>GRIB_P1 :</span></dt><dd>0</dd><dt><span>GRIB_P2 :</span></dt><dd>240</dd><dt><span>GRIB_numberIncludedInAverage :</span></dt><dd>0</dd><dt><span>GRIB_pl :</span></dt><dd>[2, 6, 14, 20, 26, 32, 38, 44, 50, 58, 64, 70, 76, 82, 88, 94, 100, 108, 114, 120, 126, 132, 138, 144, 150, 156, 164, 170, 176, 182, 188, 194, 200, 206, 212, 218, 224, 230, 236, 244, 250, 256, 262, 268, 274, 280, 286, 292, 298, 304, 310, 316, 322, 328, 334, 340, 346, 352, 356, 362, 368, 374, 380, 386, 392, 398, 404, 410, 414, 420, 426, 432, 438, 444, 448, 454, 460, 466, 472, 476, 482, 488, 494, 498, 504, 510, 514, 520, 526, 532, 536, 542, 546, 552, 558, 562, 568, 572, 578, 584, 588, 594, 598, 604, 608, 614, 618, 624, 628, 634, 638, 642, 648, 652, 658, 662, 666, 672, 676, 680, 686, 690, 694, 698, 704, 708, 712, 716, 720, 726, 730, 734, 738, 742, 746, 750, 754, 758, 762, 766, 772, 776, 778, 782, 786, 790, 794, 798, 802, 806, 810, 814, 816, 820, 824, 828, 832, 834, 838, 842, 844, 848, 852, 854, 858, 862, 864, 868, 870, 874, 876, 880, 882, 886, 888, 892, 894, 898, 900, 902, 906, 908, 910, 914, 916, 918, 920, 924, 926, 928, 930, 932, 934, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 960, 962, 964, 966, 968, 970, 970, 972, 974, 976, 976, 978, 980, 980, 982, 982, 984, 986, 986, 988, 988, 990, 990, 990, 992, 992, 994, 994, 994, 996, 996, 996, 998, 998, 998, 998, 998, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 998, 998, 998, 998, 998, 996, 996, 996, 994, 994, 994, 992, 992, 990, 990, 990, 988, 988, 986, 986, 984, 982, 982, 980, 980, 978, 976, 976, 974, 972, 970, 970, 968, 966, 964, 962, 960, 960, 958, 956, 954, 952, 950, 948, 946, 944, 942, 940, 938, 934, 932, 930, 928, 926, 924, 920, 918, 916, 914, 910, 908, 906, 902, 900, 898, 894, 892, 888, 886, 882, 880, 876, 874, 870, 868, 864, 862, 858, 854, 852, 848, 844, 842, 838, 834, 832, 828, 824, 820, 816, 814, 810, 806, 802, 798, 794, 790, 786, 782, 778, 776, 772, 766, 762, 758, 754, 750, 746, 742, 738, 734, 730, 726, 720, 716, 712, 708, 704, 698, 694, 690, 686, 680, 676, 672, 666, 662, 658, 652, 648, 642, 638, 634, 628, 624, 618, 614, 608, 604, 598, 594, 588, 584, 578, 572, 568, 562, 558, 552, 546, 542, 536, 532, 526, 520, 514, 510, 504, 498, 494, 488, 482, 476, 472, 466, 460, 454, 448, 444, 438, 432, 426, 420, 414, 410, 404, 398, 392, 386, 380, 374, 368, 362, 356, 352, 346, 340, 334, 328, 322, 316, 310, 304, 298, 292, 286, 280, 274, 268, 262, 256, 250, 244, 236, 230, 224, 218, 212, 206, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</dd><dt><span>units :</span></dt><dd>m s**-1</dd><dt><span>long_name :</span></dt><dd>Free convective velocity over the oceans</dd></dl></div><div class='xr-var-data'><pre>[6620418 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de861815-db8d-48c8-9c7c-f6f32f9cb011' class='xr-section-summary-in' type='checkbox'  ><label for='section-de861815-db8d-48c8-9c7c-f6f32f9cb011' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-313b2caa-aea0-4e04-a162-470be5335c8c' class='xr-index-data-in' type='checkbox'/><label for='index-313b2caa-aea0-4e04-a162-470be5335c8c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-07-21 00:00:00&#x27;, &#x27;2020-07-21 12:00:00&#x27;,\n               &#x27;2020-07-22 00:00:00&#x27;, &#x27;2020-07-22 12:00:00&#x27;,\n               &#x27;2020-07-23 00:00:00&#x27;, &#x27;2020-07-23 12:00:00&#x27;,\n               &#x27;2020-07-24 00:00:00&#x27;, &#x27;2020-07-24 12:00:00&#x27;,\n               &#x27;2020-07-25 00:00:00&#x27;, &#x27;2020-07-25 12:00:00&#x27;,\n               &#x27;2020-07-26 00:00:00&#x27;, &#x27;2020-07-26 12:00:00&#x27;,\n               &#x27;2020-07-27 00:00:00&#x27;, &#x27;2020-07-27 12:00:00&#x27;,\n               &#x27;2020-07-28 00:00:00&#x27;, &#x27;2020-07-28 12:00:00&#x27;,\n               &#x27;2020-07-29 00:00:00&#x27;, &#x27;2020-07-29 12:00:00&#x27;,\n               &#x27;2020-07-30 00:00:00&#x27;, &#x27;2020-07-30 12:00:00&#x27;,\n               &#x27;2020-07-31 00:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8806bfa5-ae1d-4b37-bf45-4ff00174780e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8806bfa5-ae1d-4b37-bf45-4ff00174780e' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>GRIB_edition :</span></dt><dd>1</dd><dt><span>GRIB_centre :</span></dt><dd>ecmf</dd><dt><span>GRIB_centreDescription :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>GRIB_subCentre :</span></dt><dd>0</dd><dt><span>history :</span></dt><dd>🪄🧙‍♂️🔮 magic dataset assembly provided by gribscan.IFSMagician\r\n</dd></dl></div></li></ul></div></div>"
           },
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diff --git a/04-example-datasets/02-OpenIFS.ipynb b/04-example-datasets/02-OpenIFS.ipynb
index bcb9a0f..95500ff 100644
--- a/04-example-datasets/02-OpenIFS.ipynb
+++ b/04-example-datasets/02-OpenIFS.ipynb
@@ -36,7 +36,7 @@
     {
       "id": "463bf2c0-ac3f-4786-9687-362393802dce",
       "cell_type": "markdown",
-      "source": "This notebook is a comprehensive guide to accessing NetCDF files for an OpenIFS simulation. It includes details about the available parameters, levels, and steps, along with instructions for using the `load_OpenIFS_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point output from the OpenIFS 43r3 model.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco1279), corresponding to a horizontal resolution of approximately 9 km.\nThe 5-day forecast was run with a time step length of 600 seconds using a HighResMIP based output configuration. \nThe total volume of the available dataset is about 258 GB.\n\n\nThere is a single NetCDF file for the surface level fields and separate files for each pressure level field.\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in Pa):\n```python\n\n[100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 25000, 20000, 15000, 10000, 7000, 5000, 3000, 2000, 1000, 500, 100]\n\n```\nThe available time steps start at 2009-10-15 18:00:00 UTC, increasing in 6-hour steps to 2009-10-20 12:00:00 UTC.\n\n### Surface Level Parameters\n\nThe following parameters are available as surface level fields:\n```python\n[\"10u\", \"10v\", \"2d\", \"2t\", \"cp\", \"e\", \"es\", \"ewss\", \"msl\", \"nsss\", \"ro\", \"rsn\", \"sd\", \"sf\", \"skt\", \"slhf\", \"smlt\", \"sp\", \"sro\", \"sshf\", \"ssr\", \"ssrc\", \"ssrd\", \"stl1\", \"stl2\", \"stl3\", \"stl4\", \"str\", \"strc\", \"strd\", \"swvl1\", \"swvl2\", \"swvl3\", \"swvl4\", \"tcc\", \"tciw\", \"tclw\", \"tcwv\", \"tisr\", \"tp\", \"tsn\", \"tsr\", \"tsrc\", \"ttr\", \"ttrc\"]\n```\n\nHere, the available time steps start at 2009-10-15 15:00:00 UTC, increasing in 3-hour steps to 2009-10-20 12:00:00 UTC.",
+      "source": "This notebook is a comprehensive guide to accessing NetCDF files for an OpenIFS simulation. It includes details about the available parameters, levels, and steps, along with instructions for using the `load_OpenIFS_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point output from the OpenIFS 43r3 model.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco1279), corresponding to a horizontal resolution of approximately 9 km.\nThe 5-day forecast was run with a time step length of 600 seconds using a HighResMIP based output configuration. \nThe total volume of the available dataset is about 258 GB.\n\n\nThere is a single NetCDF file for the surface level fields and separate files for each pressure level field. This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/).\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in Pa):\n```python\n\n[100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 25000, 20000, 15000, 10000, 7000, 5000, 3000, 2000, 1000, 500, 100]\n\n```\nThe available time steps start at 2009-10-15 18:00:00 UTC, increasing in 6-hour steps to 2009-10-20 12:00:00 UTC.\n\n### Surface Level Parameters\n\nThe following parameters are available as surface level fields:\n```python\n[\"10u\", \"10v\", \"2d\", \"2t\", \"cp\", \"e\", \"es\", \"ewss\", \"msl\", \"nsss\", \"ro\", \"rsn\", \"sd\", \"sf\", \"skt\", \"slhf\", \"smlt\", \"sp\", \"sro\", \"sshf\", \"ssr\", \"ssrc\", \"ssrd\", \"stl1\", \"stl2\", \"stl3\", \"stl4\", \"str\", \"strc\", \"strd\", \"swvl1\", \"swvl2\", \"swvl3\", \"swvl4\", \"tcc\", \"tciw\", \"tclw\", \"tcwv\", \"tisr\", \"tp\", \"tsn\", \"tsr\", \"tsrc\", \"ttr\", \"ttrc\"]\n```\n\nHere, the available time steps start at 2009-10-15 15:00:00 UTC, increasing in 3-hour steps to 2009-10-20 12:00:00 UTC.",
       "metadata": {}
     },
     {
@@ -71,7 +71,7 @@
       "outputs": [
         {
           "name": "stdout",
-          "text": "[pyodide]: Loading Jinja2, MarkupSafe, aiohttp, aiosignal, asciitree, async-timeout, attrs, click, cloudpickle, dask, frozenlist, fsspec, importlib_metadata, locket, msgpack, multidict, numcodecs, numpy, partd, propcache, pyyaml, toolz, yarl, zarr, zipp\n[pyodide]: Loaded Jinja2, MarkupSafe, aiohttp, aiosignal, asciitree, async-timeout, attrs, click, cloudpickle, dask, frozenlist, fsspec, importlib_metadata, locket, msgpack, multidict, numcodecs, numpy, partd, propcache, pyyaml, toolz, yarl, zarr, zipp\n[pyodide]: Memory usage has grown to 71.9MiB (from 49.9MiB) for this notebook\n",
+          "text": "[pyodide]: Memory usage has grown to 71.9MiB (from 49.9MiB) for this notebook\n",
           "output_type": "stream"
         }
       ],
@@ -93,7 +93,7 @@
       "outputs": [
         {
           "name": "stdout",
-          "text": "[pyodide]: Loading pandas, python-dateutil, pytz, tzdata, xarray\n[pyodide]: Loaded pandas, python-dateutil, pytz, tzdata, xarray\n[pyodide]: Memory usage has grown to 124.2MiB (from 71.9MiB) for this notebook\n",
+          "text": "[pyodide]: Memory usage has grown to 149.1MiB (from 71.9MiB) for this notebook\n",
           "output_type": "stream"
         }
       ],
@@ -123,7 +123,7 @@
           "output_type": "execute_result",
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2;\n}\n\n.xr-array-data,\n.xr-array-in:checked ~ .xr-array-preview {\n  display: none;\n}\n\n.xr-array-in:checked ~ .xr-array-data,\n.xr-array-preview {\n  display: inline-block;\n}\n\n.xr-dim-list {\n  display: inline-block !important;\n  list-style: none;\n  padding: 0 !important;\n  margin: 0;\n}\n\n.xr-dim-list li {\n  display: inline-block;\n  padding: 0;\n  margin: 0;\n}\n\n.xr-dim-list:before {\n  content: '(';\n}\n\n.xr-dim-list:after {\n  content: ')';\n}\n\n.xr-dim-list li:not(:last-child):after {\n  content: ',';\n  padding-right: 5px;\n}\n\n.xr-has-index {\n  font-weight: bold;\n}\n\n.xr-var-list,\n.xr-var-item {\n  display: contents;\n}\n\n.xr-var-item > div,\n.xr-var-item label,\n.xr-var-item > .xr-var-name span {\n  background-color: var(--xr-background-color-row-even);\n  margin-bottom: 0;\n}\n\n.xr-var-item > .xr-var-name:hover span {\n  padding-right: 5px;\n}\n\n.xr-var-list > li:nth-child(odd) > div,\n.xr-var-list > li:nth-child(odd) > label,\n.xr-var-list > li:nth-child(odd) > .xr-var-name span {\n  background-color: var(--xr-background-color-row-odd);\n}\n\n.xr-var-name {\n  grid-column: 1;\n}\n\n.xr-var-dims {\n  grid-column: 2;\n}\n\n.xr-var-dtype {\n  grid-column: 3;\n  text-align: right;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-preview {\n  grid-column: 4;\n}\n\n.xr-index-preview {\n  grid-column: 2 / 5;\n  color: var(--xr-font-color2);\n}\n\n.xr-var-name,\n.xr-var-dims,\n.xr-var-dtype,\n.xr-preview,\n.xr-attrs dt {\n  white-space: nowrap;\n  overflow: hidden;\n  text-overflow: ellipsis;\n  padding-right: 10px;\n}\n\n.xr-var-name:hover,\n.xr-var-dims:hover,\n.xr-var-dtype:hover,\n.xr-attrs dt:hover {\n  overflow: visible;\n  width: auto;\n  z-index: 1;\n}\n\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  display: none;\n  background-color: var(--xr-background-color) !important;\n  padding-bottom: 5px !important;\n}\n\n.xr-var-attrs-in:checked ~ .xr-var-attrs,\n.xr-var-data-in:checked ~ .xr-var-data,\n.xr-index-data-in:checked ~ .xr-index-data {\n  display: block;\n}\n\n.xr-var-data > table {\n  float: right;\n}\n\n.xr-var-name span,\n.xr-var-data,\n.xr-index-name div,\n.xr-index-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2,\n.xr-no-icon {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 20GB\nDimensions:              (cell: 6599680, nvertex: 4, pressure_levels: 19,\n                          time_counter: 20, axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n  * pressure_levels      (pressure_levels) float32 76B 1e+05 9.25e+04 ... 100.0\n  * time_counter         (time_counter) datetime64[ns] 160B 2009-10-15T18:00:...\n    time_instant         (time_counter) datetime64[ns] 160B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables:\n    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    q                    (time_counter, pressure_levels, cell) float64 20GB dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;\n    time_counter_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n    time_instant_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_6h_reduced_pl_q\n    timeStamp:    2024-Apr-05 11:05:21 GMT\n    title:        Created by xios\n    uuid:         25d5f0a2-ceae-47e1-936f-5b1804228ca6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bea1a9f3-f540-43a9-be4c-8ab97ddcd21e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bea1a9f3-f540-43a9-be4c-8ab97ddcd21e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span class='xr-has-index'>pressure_levels</span>: 19</li><li><span class='xr-has-index'>time_counter</span>: 20</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-49c9c384-0b04-4b88-b953-141b2290df25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49c9c384-0b04-4b88-b953-141b2290df25' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-ef3828e5-6f81-4c62-982c-99156a79089b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef3828e5-6f81-4c62-982c-99156a79089b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b4ea86c-2f32-410f-ba7a-eda539454221' class='xr-var-data-in' type='checkbox'><label for='data-8b4ea86c-2f32-410f-ba7a-eda539454221' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-91ad89a3-cd3d-459d-b1b7-45690b571784' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91ad89a3-cd3d-459d-b1b7-45690b571784' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd3ee627-e840-45fc-9af9-c6f908f2af5c' class='xr-var-data-in' type='checkbox'><label for='data-fd3ee627-e840-45fc-9af9-c6f908f2af5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure_levels</span></div><div class='xr-var-dims'>(pressure_levels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.25e+04 ... 500.0 100.0</div><input id='attrs-75f299b8-e823-4c88-92f5-65f2aa1c2d6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75f299b8-e823-4c88-92f5-65f2aa1c2d6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1751944-7c46-4794-b263-8649e59c73ab' class='xr-var-data-in' type='checkbox'><label for='data-b1751944-7c46-4794-b263-8649e59c73ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertical pressure levels</dd><dt><span>name :</span></dt><dd>pressure_levels</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  92500.,  85000.,  70000.,  60000.,  50000.,  40000.,  30000.,\n        25000.,  20000.,  15000.,  10000.,   7000.,   5000.,   3000.,   2000.,\n         1000.,    500.,    100.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T18:00:00 ... 2009-10-...</div><input id='attrs-1a8e5215-742c-4b20-8998-ca234c1f9365' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a8e5215-742c-4b20-8998-ca234c1f9365' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-675da0a7-d5df-4b0c-8399-ab8d79ce6146' class='xr-var-data-in' type='checkbox'><label for='data-675da0a7-d5df-4b0c-8399-ab8d79ce6146' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T18:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n       &#x27;2009-10-16T06:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n       &#x27;2009-10-16T18:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n       &#x27;2009-10-17T06:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n       &#x27;2009-10-17T18:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n       &#x27;2009-10-18T06:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n       &#x27;2009-10-18T18:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n       &#x27;2009-10-19T06:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n       &#x27;2009-10-19T18:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n       &#x27;2009-10-20T06:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-2a6fba6e-e9a9-479f-9857-df4b65f76986' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a6fba6e-e9a9-479f-9857-df4b65f76986' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29a61859-d162-4940-83cd-f3ec27778d85' class='xr-var-data-in' type='checkbox'><label for='data-29a61859-d162-4940-83cd-f3ec27778d85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 160 B </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"84\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"34\" style=\"stroke-width:2\" />\n  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"34\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"34\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"34\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"34\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"34\" />\n  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"34\" />\n  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"34\" />\n  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"34\" />\n  <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"34\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"34\" />\n  <line x1=\"66\" y1=\"0\" x2=\"66\" y2=\"34\" />\n  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"34\" />\n  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"34\" />\n  <line x1=\"84\" y1=\"0\" x2=\"84\" y2=\"34\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"34\" />\n  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"34\" />\n  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"34\" />\n  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"34\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,34.50101617379934 0.0,34.50101617379934\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"54.501016\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >20</text>\n  <text x=\"140.000000\" y=\"17.250508\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,17.250508)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d3e3d3e1-7fba-4ecb-b06e-9d7340e66189' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3e3d3e1-7fba-4ecb-b06e-9d7340e66189' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-a47e1d00-b136-490b-af47-b41dbeedb289' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a47e1d00-b136-490b-af47-b41dbeedb289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d59cc35c-1c97-488d-9811-d775ac3cd1d6' class='xr-var-data-in' type='checkbox'><label for='data-d59cc35c-1c97-488d-9811-d775ac3cd1d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-16fa068e-fdae-4088-b7c1-2c030d70a24a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16fa068e-fdae-4088-b7c1-2c030d70a24a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-394b0ad5-5da3-4605-b690-fdb4307efabd' class='xr-var-data-in' type='checkbox'><label for='data-394b0ad5-5da3-4605-b690-fdb4307efabd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time_counter, pressure_levels, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0d955b0b-e47a-4b3f-a4e1-5768b3ed54b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d955b0b-e47a-4b3f-a4e1-5768b3ed54b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34f327da-b097-402f-9e80-b1c1e480b3d7' class='xr-var-data-in' type='checkbox'><label for='data-34f327da-b097-402f-9e80-b1c1e480b3d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>6 h</dd><dt><span>interval_write :</span></dt><dd>6 h</dd><dt><span>long_name :</span></dt><dd>specific humidity</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg*kg-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.69 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 19, 6599680) </td>\n                        <td> (1, 1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 6080 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"1\" x2=\"24\" y2=\"16\" />\n  <line x1=\"10\" y1=\"2\" x2=\"24\" 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x2=\"144\" y2=\"25\" />\n  <line x1=\"24\" y1=\"26\" x2=\"144\" y2=\"26\" />\n  <line x1=\"24\" y1=\"28\" x2=\"144\" y2=\"28\" />\n  <line x1=\"24\" y1=\"29\" x2=\"144\" y2=\"29\" />\n  <line x1=\"24\" y1=\"30\" x2=\"144\" y2=\"30\" />\n  <line x1=\"24\" y1=\"32\" x2=\"144\" y2=\"32\" />\n  <line x1=\"24\" y1=\"33\" x2=\"144\" y2=\"33\" />\n  <line x1=\"24\" y1=\"35\" x2=\"144\" y2=\"35\" />\n  <line x1=\"24\" y1=\"36\" x2=\"144\" y2=\"36\" />\n  <line x1=\"24\" y1=\"37\" x2=\"144\" y2=\"37\" />\n  <line x1=\"24\" y1=\"39\" x2=\"144\" y2=\"39\" />\n  <line x1=\"24\" y1=\"40\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n  <line x1=\"32\" y1=\"14\" x2=\"32\" y2=\"40\" />\n  <line x1=\"39\" y1=\"14\" x2=\"39\" y2=\"40\" />\n  <line x1=\"47\" y1=\"14\" x2=\"47\" y2=\"40\" />\n  <line x1=\"54\" y1=\"14\" x2=\"54\" y2=\"40\" />\n  <line x1=\"62\" y1=\"14\" x2=\"62\" y2=\"40\" />\n  <line x1=\"69\" y1=\"14\" x2=\"69\" y2=\"40\" />\n  <line x1=\"77\" y1=\"14\" x2=\"77\" y2=\"40\" />\n  <line x1=\"84\" y1=\"14\" x2=\"84\" y2=\"40\" />\n  <line x1=\"92\" y1=\"14\" x2=\"92\" y2=\"40\" />\n  <line x1=\"99\" y1=\"14\" x2=\"99\" y2=\"40\" />\n  <line x1=\"107\" y1=\"14\" x2=\"107\" y2=\"40\" />\n  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"40\" />\n  <line x1=\"122\" y1=\"14\" x2=\"122\" y2=\"40\" />\n  <line x1=\"129\" y1=\"14\" x2=\"129\" y2=\"40\" />\n  <line x1=\"137\" y1=\"14\" x2=\"137\" y2=\"40\" />\n  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,40.36121446433689 24.9485979497544,40.36121446433689\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">19</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-bf10a11c-d841-42d5-86dc-6924bdfa4518' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bf10a11c-d841-42d5-86dc-6924bdfa4518' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bea82c6a-3dee-457a-9ca2-7504c95c4b26' class='xr-var-data-in' type='checkbox'><label for='data-bea82c6a-3dee-457a-9ca2-7504c95c4b26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"38\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" 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style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"19.298432\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"58.596863\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,58.596863,60.000000)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-7bb5c160-0c1f-468c-8650-0da89be06ec0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7bb5c160-0c1f-468c-8650-0da89be06ec0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-469e5b83-44f0-4ae4-a9e3-4ca99a8c0cac' class='xr-var-data-in' type='checkbox'><label for='data-469e5b83-44f0-4ae4-a9e3-4ca99a8c0cac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"38\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"38\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"38\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"38\" y2=\"54\" />\n  <line x1=\"0\" y1=\"60\" x2=\"38\" y2=\"60\" />\n  <line x1=\"0\" y1=\"66\" x2=\"38\" y2=\"66\" />\n  <line x1=\"0\" y1=\"72\" x2=\"38\" y2=\"72\" />\n  <line x1=\"0\" y1=\"78\" x2=\"38\" y2=\"78\" />\n  <line x1=\"0\" y1=\"84\" x2=\"38\" y2=\"84\" />\n  <line x1=\"0\" y1=\"90\" x2=\"38\" y2=\"90\" />\n  <line x1=\"0\" y1=\"96\" x2=\"38\" y2=\"96\" />\n  <line x1=\"0\" y1=\"102\" x2=\"38\" y2=\"102\" />\n  <line x1=\"0\" y1=\"108\" x2=\"38\" y2=\"108\" />\n  <line x1=\"0\" y1=\"120\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 38.596863036086,0.0 38.596863036086,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"19.298432\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"58.596863\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,58.596863,60.000000)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6822f1fa-e183-4ead-9bf4-dcaebf4c09e9' class='xr-section-summary-in' type='checkbox'  ><label for='section-6822f1fa-e183-4ead-9bf4-dcaebf4c09e9' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>pressure_levels</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1a32cb15-0053-4488-a983-5ff8ea7011c0' class='xr-index-data-in' type='checkbox'/><label for='index-1a32cb15-0053-4488-a983-5ff8ea7011c0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000.0,  92500.0,  85000.0,  70000.0,  60000.0,  50000.0,  40000.0,\n        30000.0,  25000.0,  20000.0,  15000.0,  10000.0,   7000.0,   5000.0,\n         3000.0,   2000.0,   1000.0,    500.0,    100.0],\n      dtype=&#x27;float32&#x27;, name=&#x27;pressure_levels&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5f7a1eeb-3f63-494a-b5af-dc7a1749f0bc' class='xr-index-data-in' type='checkbox'/><label for='index-5f7a1eeb-3f63-494a-b5af-dc7a1749f0bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 18:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n               &#x27;2009-10-16 06:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n               &#x27;2009-10-16 18:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n               &#x27;2009-10-17 06:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n               &#x27;2009-10-17 18:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n               &#x27;2009-10-18 06:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n               &#x27;2009-10-18 18:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n               &#x27;2009-10-19 06:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n               &#x27;2009-10-19 18:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n               &#x27;2009-10-20 06:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6a16d5f-b5e6-4a38-8948-d8b49bbe3d03' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6a16d5f-b5e6-4a38-8948-d8b49bbe3d03' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_6h_reduced_pl_q</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 11:05:21 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>25d5f0a2-ceae-47e1-936f-5b1804228ca6</dd></dl></div></li></ul></div></div>"
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 20GB\nDimensions:              (cell: 6599680, nvertex: 4, pressure_levels: 19,\n                          time_counter: 20, axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n  * pressure_levels      (pressure_levels) float32 76B 1e+05 9.25e+04 ... 100.0\n  * time_counter         (time_counter) datetime64[ns] 160B 2009-10-15T18:00:...\n    time_instant         (time_counter) datetime64[ns] 160B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables:\n    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    q                    (time_counter, pressure_levels, cell) float64 20GB dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;\n    time_counter_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n    time_instant_bounds  (time_counter, axis_nbounds) datetime64[ns] 320B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_6h_reduced_pl_q\n    timeStamp:    2024-Apr-05 11:05:21 GMT\n    title:        Created by xios\n    uuid:         25d5f0a2-ceae-47e1-936f-5b1804228ca6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5a1538e8-3c13-4ba2-85f2-45aebc198db7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5a1538e8-3c13-4ba2-85f2-45aebc198db7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span class='xr-has-index'>pressure_levels</span>: 19</li><li><span class='xr-has-index'>time_counter</span>: 20</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e18ac7cf-ed4a-446c-842a-3794091a9cf4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e18ac7cf-ed4a-446c-842a-3794091a9cf4' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-48953c5c-e49c-4c29-b6c8-cbd215cc9d9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48953c5c-e49c-4c29-b6c8-cbd215cc9d9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75466bec-b12f-444d-b385-ab957c65d479' class='xr-var-data-in' type='checkbox'><label for='data-75466bec-b12f-444d-b385-ab957c65d479' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon 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id='data-b0931252-75d9-453e-b1bc-bd5b8160112e' class='xr-var-data-in' type='checkbox'><label for='data-b0931252-75d9-453e-b1bc-bd5b8160112e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure_levels</span></div><div class='xr-var-dims'>(pressure_levels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+05 9.25e+04 ... 500.0 100.0</div><input id='attrs-14390ffe-145e-4938-8001-20413c658331' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14390ffe-145e-4938-8001-20413c658331' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb86e82e-8175-4fbb-8949-cab4de7fdaa1' class='xr-var-data-in' type='checkbox'><label for='data-cb86e82e-8175-4fbb-8949-cab4de7fdaa1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertical pressure levels</dd><dt><span>name :</span></dt><dd>pressure_levels</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><pre>array([100000.,  92500.,  85000.,  70000.,  60000.,  50000.,  40000.,  30000.,\n        25000.,  20000.,  15000.,  10000.,   7000.,   5000.,   3000.,   2000.,\n         1000.,    500.,    100.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T18:00:00 ... 2009-10-...</div><input id='attrs-a9d900ae-6b5c-4cc1-997c-8f707f646e71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9d900ae-6b5c-4cc1-997c-8f707f646e71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fc07f9b-31ff-47df-9065-a42fe31d7b97' class='xr-var-data-in' type='checkbox'><label for='data-3fc07f9b-31ff-47df-9065-a42fe31d7b97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T18:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n       &#x27;2009-10-16T06:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n       &#x27;2009-10-16T18:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n       &#x27;2009-10-17T06:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n       &#x27;2009-10-17T18:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n       &#x27;2009-10-18T06:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n       &#x27;2009-10-18T18:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n       &#x27;2009-10-19T06:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n       &#x27;2009-10-19T18:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n       &#x27;2009-10-20T06:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-4c53eb55-22d9-4dab-acf0-7a40afd410c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c53eb55-22d9-4dab-acf0-7a40afd410c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c77417e0-803c-4b5a-b078-e00748c1172b' class='xr-var-data-in' type='checkbox'><label for='data-c77417e0-803c-4b5a-b078-e00748c1172b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 160 B </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"84\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"34\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"34\" style=\"stroke-width:2\" />\n  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"34\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"34\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"34\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"34\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"34\" />\n  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"34\" />\n  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"34\" />\n  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"34\" />\n  <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"34\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"34\" />\n  <line x1=\"66\" y1=\"0\" x2=\"66\" y2=\"34\" />\n  <line x1=\"72\" y1=\"0\" x2=\"72\" y2=\"34\" />\n  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"34\" />\n  <line x1=\"84\" y1=\"0\" x2=\"84\" y2=\"34\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"34\" />\n  <line x1=\"96\" y1=\"0\" x2=\"96\" y2=\"34\" />\n  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"34\" />\n  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"34\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"34\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,34.50101617379934 0.0,34.50101617379934\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"54.501016\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >20</text>\n  <text x=\"140.000000\" y=\"17.250508\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,17.250508)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3da4fb7-6b0e-479d-8a59-e5574b616416' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a3da4fb7-6b0e-479d-8a59-e5574b616416' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-a52230ba-b399-406d-bee0-3354ddad38e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a52230ba-b399-406d-bee0-3354ddad38e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d301c85-bff1-4e9f-a8a7-d9b201d1decc' class='xr-var-data-in' type='checkbox'><label for='data-5d301c85-bff1-4e9f-a8a7-d9b201d1decc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-4232b776-a787-4d81-b99c-73994cad0265' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4232b776-a787-4d81-b99c-73994cad0265' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4a83fe1-ff65-4daa-86e4-9eea5894de7e' class='xr-var-data-in' type='checkbox'><label for='data-f4a83fe1-ff65-4daa-86e4-9eea5894de7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time_counter, pressure_levels, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 412480), meta=np.ndarray&gt;</div><input id='attrs-acf0848c-1fa1-42d2-b507-873ad341e070' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acf0848c-1fa1-42d2-b507-873ad341e070' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a78c32a2-7d60-491c-b246-d1f99d63659f' class='xr-var-data-in' type='checkbox'><label for='data-a78c32a2-7d60-491c-b246-d1f99d63659f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>6 h</dd><dt><span>interval_write :</span></dt><dd>6 h</dd><dt><span>long_name :</span></dt><dd>specific humidity</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg*kg-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.69 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 19, 6599680) </td>\n                        <td> (1, 1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 6080 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"1\" x2=\"24\" y2=\"16\" />\n  <line x1=\"10\" y1=\"2\" x2=\"24\" 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x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">19</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-deede84d-98e0-4d0d-b85b-f17c914e332e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-deede84d-98e0-4d0d-b85b-f17c914e332e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57493555-80d5-4c4d-afe6-79d62b8c9922' class='xr-var-data-in' type='checkbox'><label for='data-57493555-80d5-4c4d-afe6-79d62b8c9922' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"38\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"38\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"38\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"38\" y2=\"54\" />\n  <line x1=\"0\" y1=\"60\" x2=\"38\" y2=\"60\" />\n  <line x1=\"0\" y1=\"66\" x2=\"38\" y2=\"66\" />\n  <line x1=\"0\" y1=\"72\" x2=\"38\" y2=\"72\" />\n  <line x1=\"0\" y1=\"78\" x2=\"38\" y2=\"78\" />\n  <line x1=\"0\" y1=\"84\" x2=\"38\" y2=\"84\" />\n  <line x1=\"0\" y1=\"90\" x2=\"38\" y2=\"90\" />\n  <line x1=\"0\" y1=\"96\" x2=\"38\" y2=\"96\" />\n  <line x1=\"0\" y1=\"102\" x2=\"38\" y2=\"102\" />\n  <line x1=\"0\" y1=\"108\" x2=\"38\" y2=\"108\" />\n  <line x1=\"0\" y1=\"120\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 38.596863036086,0.0 38.596863036086,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"19.298432\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"58.596863\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,58.596863,60.000000)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-29f7b240-1731-4c39-9983-fda1ba95a439' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29f7b240-1731-4c39-9983-fda1ba95a439' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7b1a728-4d93-408e-8198-e7cf4a75ee4a' class='xr-var-data-in' type='checkbox'><label for='data-c7b1a728-4d93-408e-8198-e7cf4a75ee4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (20, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 20 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"38\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"38\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"38\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"38\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"38\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"38\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"38\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"38\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"38\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"38\" y2=\"54\" />\n  <line x1=\"0\" y1=\"60\" x2=\"38\" y2=\"60\" />\n  <line x1=\"0\" y1=\"66\" x2=\"38\" y2=\"66\" />\n  <line x1=\"0\" y1=\"72\" x2=\"38\" y2=\"72\" />\n  <line x1=\"0\" y1=\"78\" x2=\"38\" y2=\"78\" />\n  <line x1=\"0\" y1=\"84\" x2=\"38\" y2=\"84\" />\n  <line x1=\"0\" y1=\"90\" x2=\"38\" y2=\"90\" />\n  <line x1=\"0\" y1=\"96\" x2=\"38\" y2=\"96\" />\n  <line x1=\"0\" y1=\"102\" x2=\"38\" y2=\"102\" />\n  <line x1=\"0\" y1=\"108\" x2=\"38\" y2=\"108\" />\n  <line x1=\"0\" y1=\"120\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 38.596863036086,0.0 38.596863036086,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"19.298432\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"58.596863\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,58.596863,60.000000)\">20</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b23ebe0-8db3-47f9-b1b2-79900e202062' class='xr-section-summary-in' type='checkbox'  ><label for='section-0b23ebe0-8db3-47f9-b1b2-79900e202062' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>pressure_levels</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8dd5e75b-4b35-4bc2-b0fb-becc7bf92a16' class='xr-index-data-in' type='checkbox'/><label for='index-8dd5e75b-4b35-4bc2-b0fb-becc7bf92a16' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000.0,  92500.0,  85000.0,  70000.0,  60000.0,  50000.0,  40000.0,\n        30000.0,  25000.0,  20000.0,  15000.0,  10000.0,   7000.0,   5000.0,\n         3000.0,   2000.0,   1000.0,    500.0,    100.0],\n      dtype=&#x27;float32&#x27;, name=&#x27;pressure_levels&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-84d29b14-3cea-490c-b69c-3dddc929b223' class='xr-index-data-in' type='checkbox'/><label for='index-84d29b14-3cea-490c-b69c-3dddc929b223' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 18:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n               &#x27;2009-10-16 06:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n               &#x27;2009-10-16 18:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n               &#x27;2009-10-17 06:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n               &#x27;2009-10-17 18:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n               &#x27;2009-10-18 06:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n               &#x27;2009-10-18 18:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n               &#x27;2009-10-19 06:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n               &#x27;2009-10-19 18:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n               &#x27;2009-10-20 06:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b2571945-b5d9-458f-8d79-db1ca5ead964' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b2571945-b5d9-458f-8d79-db1ca5ead964' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_6h_reduced_pl_q</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 11:05:21 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>25d5f0a2-ceae-47e1-936f-5b1804228ca6</dd></dl></div></li></ul></div></div>"
           },
           "metadata": {}
         }
@@ -144,11 +144,6 @@
         "trusted": true
       },
       "outputs": [
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Loading Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cftime, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n[pyodide]: Loaded Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cftime, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n",
-          "output_type": "stream"
-        },
         {
           "name": "stderr",
           "text": "/lib/python3.12/site-packages/earthkit/regrid/utils/caching.py:27: UserWarning: only soft file lock is available\n  from filelock import FileLock\n",
@@ -156,7 +151,7 @@
         },
         {
           "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 309.4MiB (from 124.2MiB) for this notebook\n",
+          "text": "[pyodide]: Memory usage has grown to 309.4MiB (from 149.1MiB) for this notebook\n",
           "output_type": "stream"
         }
       ],
@@ -175,27 +170,8 @@
       "metadata": {
         "trusted": true
       },
-      "outputs": [
-        {
-          "name": "stderr",
-          "text": "/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip\n  warnings.warn(f'Downloading: {url}', DownloadWarning)\n",
-          "output_type": "stream"
-        },
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 735.0MiB (from 309.4MiB) for this notebook\n",
-          "output_type": "stream"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 700x600 with 2 Axes>",
-            "image/png": 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"
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 6
+      "outputs": [],
+      "execution_count": null
     },
     {
       "id": "f2d66bcd-cb4b-4a03-970e-3ce49d68380e",
@@ -210,23 +186,8 @@
       "metadata": {
         "trusted": true
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/HighResMIP_3h_reduced_sfc.nc\n",
-          "output_type": "stream"
-        },
-        {
-          "execution_count": 7,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "<xarray.Dataset> Size: 95GB\nDimensions:              (time_counter: 40, cell: 6599680, nvertex: 4,\n                          axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array<chunksize=(824960,), meta=np.ndarray>\n    lon                  (cell) float32 26MB dask.array<chunksize=(824960,), meta=np.ndarray>\n  * time_counter         (time_counter) datetime64[ns] 320B 2009-10-15T15:00:...\n    time_instant         (time_counter) datetime64[ns] 320B dask.array<chunksize=(1,), meta=np.ndarray>\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables: (12/49)\n    10u                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    10v                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    2d                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    2t                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    bounds_lat           (cell, nvertex) float32 106MB dask.array<chunksize=(206240, 4), meta=np.ndarray>\n    bounds_lon           (cell, nvertex) float32 106MB dask.array<chunksize=(206240, 4), meta=np.ndarray>\n    ...                   ...\n    tp                   (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    tsn                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    tsr                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    tsrc                 (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    ttr                  (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\n    ttrc                 (time_counter, cell) float64 2GB dask.array<chunksize=(1, 412480), meta=np.ndarray>\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_3h_reduced_sfc\n    timeStamp:    2024-Apr-05 10:55:20 GMT\n    title:        Created by xios\n    uuid:         dbac5691-165e-4465-bd75-e403aa400071",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 95GB\nDimensions:              (time_counter: 40, cell: 6599680, nvertex: 4,\n                          axis_nbounds: 2)\nCoordinates:\n    lat                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n    lon                  (cell) float32 26MB dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;\n  * time_counter         (time_counter) datetime64[ns] 320B 2009-10-15T15:00:...\n    time_instant         (time_counter) datetime64[ns] 320B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\nDimensions without coordinates: cell, nvertex, axis_nbounds\nData variables: (12/49)\n    10u                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    10v                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    2d                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    2t                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    bounds_lat           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    bounds_lon           (cell, nvertex) float32 106MB dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;\n    ...                   ...\n    tp                   (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsn                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    tsrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    ttr                  (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\n    ttrc                 (time_counter, cell) float64 2GB dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;\nAttributes:\n    Conventions:  CF-1.6\n    description:  Created by xios\n    name:         HighResMIP_3h_reduced_sfc\n    timeStamp:    2024-Apr-05 10:55:20 GMT\n    title:        Created by xios\n    uuid:         dbac5691-165e-4465-bd75-e403aa400071</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-93f5b631-d231-4e53-a7c2-ee5596b9eefc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-93f5b631-d231-4e53-a7c2-ee5596b9eefc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time_counter</span>: 40</li><li><span>cell</span>: 6599680</li><li><span>nvertex</span>: 4</li><li><span>axis_nbounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c7ae1b65-2bab-46b1-a41b-6819c7a102d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c7ae1b65-2bab-46b1-a41b-6819c7a102d5' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-710b5f8d-82f1-40ed-a8d4-867396917854' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-710b5f8d-82f1-40ed-a8d4-867396917854' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fef01ac-5671-4d66-975b-57c92dd78212' class='xr-var-data-in' type='checkbox'><label for='data-1fef01ac-5671-4d66-975b-57c92dd78212' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" 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text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(824960,), meta=np.ndarray&gt;</div><input id='attrs-15c94b6e-4c95-4f6a-86f4-1e0b9e761b2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15c94b6e-4c95-4f6a-86f4-1e0b9e761b2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18cbc34b-ebde-49ba-b8df-5069090f631f' class='xr-var-data-in' type='checkbox'><label for='data-18cbc34b-ebde-49ba-b8df-5069090f631f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>bounds_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 25.18 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680,) </td>\n                        <td> (824960,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 8 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2009-10-15T15:00:00 ... 2009-10-...</div><input id='attrs-784ebecb-941d-465b-954d-2a5ddd2c51be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-784ebecb-941d-465b-954d-2a5ddd2c51be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5ec7afe-33ea-4881-a79d-d5e7ba6a2484' class='xr-var-data-in' type='checkbox'><label for='data-a5ec7afe-33ea-4881-a79d-d5e7ba6a2484' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2009-10-15T15:00:00.000000000&#x27;, &#x27;2009-10-15T18:00:00.000000000&#x27;,\n       &#x27;2009-10-15T21:00:00.000000000&#x27;, &#x27;2009-10-16T00:00:00.000000000&#x27;,\n       &#x27;2009-10-16T03:00:00.000000000&#x27;, &#x27;2009-10-16T06:00:00.000000000&#x27;,\n       &#x27;2009-10-16T09:00:00.000000000&#x27;, &#x27;2009-10-16T12:00:00.000000000&#x27;,\n       &#x27;2009-10-16T15:00:00.000000000&#x27;, &#x27;2009-10-16T18:00:00.000000000&#x27;,\n       &#x27;2009-10-16T21:00:00.000000000&#x27;, &#x27;2009-10-17T00:00:00.000000000&#x27;,\n       &#x27;2009-10-17T03:00:00.000000000&#x27;, &#x27;2009-10-17T06:00:00.000000000&#x27;,\n       &#x27;2009-10-17T09:00:00.000000000&#x27;, &#x27;2009-10-17T12:00:00.000000000&#x27;,\n       &#x27;2009-10-17T15:00:00.000000000&#x27;, &#x27;2009-10-17T18:00:00.000000000&#x27;,\n       &#x27;2009-10-17T21:00:00.000000000&#x27;, &#x27;2009-10-18T00:00:00.000000000&#x27;,\n       &#x27;2009-10-18T03:00:00.000000000&#x27;, &#x27;2009-10-18T06:00:00.000000000&#x27;,\n       &#x27;2009-10-18T09:00:00.000000000&#x27;, &#x27;2009-10-18T12:00:00.000000000&#x27;,\n       &#x27;2009-10-18T15:00:00.000000000&#x27;, &#x27;2009-10-18T18:00:00.000000000&#x27;,\n       &#x27;2009-10-18T21:00:00.000000000&#x27;, &#x27;2009-10-19T00:00:00.000000000&#x27;,\n       &#x27;2009-10-19T03:00:00.000000000&#x27;, &#x27;2009-10-19T06:00:00.000000000&#x27;,\n       &#x27;2009-10-19T09:00:00.000000000&#x27;, &#x27;2009-10-19T12:00:00.000000000&#x27;,\n       &#x27;2009-10-19T15:00:00.000000000&#x27;, &#x27;2009-10-19T18:00:00.000000000&#x27;,\n       &#x27;2009-10-19T21:00:00.000000000&#x27;, &#x27;2009-10-20T00:00:00.000000000&#x27;,\n       &#x27;2009-10-20T03:00:00.000000000&#x27;, &#x27;2009-10-20T06:00:00.000000000&#x27;,\n       &#x27;2009-10-20T09:00:00.000000000&#x27;, &#x27;2009-10-20T12:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-3256d8ea-dd3b-4360-a3af-d937806639bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3256d8ea-dd3b-4360-a3af-d937806639bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23b5bf28-ea0a-437f-9c3e-51e1c0dece33' class='xr-var-data-in' type='checkbox'><label for='data-23b5bf28-ea0a-437f-9c3e-51e1c0dece33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_instant_bounds</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>2009-10-15 12:00:00</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320 B </td>\n                        <td> 8 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40,) </td>\n                        <td> (1,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"80\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"30\" style=\"stroke-width:2\" />\n  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"30\" />\n  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"30\" />\n  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"30\" />\n  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"30\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"30\" />\n  <line x1=\"36\" y1=\"0\" x2=\"36\" y2=\"30\" />\n  <line x1=\"42\" y1=\"0\" x2=\"42\" y2=\"30\" />\n  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"30\" />\n  <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"30\" />\n  <line 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      </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0827f46b-b1da-4d47-b2b6-dc5f50eb5155' class='xr-section-summary-in' type='checkbox'  ><label for='section-0827f46b-b1da-4d47-b2b6-dc5f50eb5155' class='xr-section-summary' >Data variables: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10u</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-bd0e7193-f53a-4b88-af35-61c44ffa83d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd0e7193-f53a-4b88-af35-61c44ffa83d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77725653-c67a-4cec-8ead-6ab19722193d' class='xr-var-data-in' type='checkbox'><label for='data-77725653-c67a-4cec-8ead-6ab19722193d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>u-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n 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x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10v</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-54124f61-1628-49c8-9c58-b52b293fa367' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54124f61-1628-49c8-9c58-b52b293fa367' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a02acd3-c4ab-48d2-bf5d-e98bbedb16ce' class='xr-var-data-in' type='checkbox'><label for='data-5a02acd3-c4ab-48d2-bf5d-e98bbedb16ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>v-component of 10m wind</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2d</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0794923c-5f9a-40b4-a63d-5f57d4bc44ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0794923c-5f9a-40b4-a63d-5f57d4bc44ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14e560c0-66ef-4e19-8193-9058fe4e1b3f' class='xr-var-data-in' type='checkbox'><label for='data-14e560c0-66ef-4e19-8193-9058fe4e1b3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m dewpoint temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>dew_point_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2t</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-45308492-e817-47e5-9207-3ad777f802c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45308492-e817-47e5-9207-3ad777f802c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0593fdea-764d-4fec-a97b-9beb6851ae58' class='xr-var-data-in' type='checkbox'><label for='data-0593fdea-764d-4fec-a97b-9beb6851ae58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>2m air temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lat</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-e2e77bcf-899a-4d29-af41-c8f6290c8c7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2e77bcf-899a-4d29-af41-c8f6290c8c7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74ef8eaf-790f-4861-bd68-b1491e0a5d78' class='xr-var-data-in' type='checkbox'><label for='data-74ef8eaf-790f-4861-bd68-b1491e0a5d78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bounds_lon</span></div><div class='xr-var-dims'>(cell, nvertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(206240, 4), meta=np.ndarray&gt;</div><input id='attrs-58cc5438-d7c8-4718-ac0a-622ab889504c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58cc5438-d7c8-4718-ac0a-622ab889504c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f242099d-1020-44bd-8b02-eb9468b46e72' class='xr-var-data-in' type='checkbox'><label for='data-f242099d-1020-44bd-8b02-eb9468b46e72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 100.70 MiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (6599680, 4) </td>\n                        <td> (206240, 4) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 32 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"25\" y2=\"3\" />\n  <line x1=\"0\" y1=\"11\" x2=\"25\" y2=\"11\" />\n  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n  <line x1=\"0\" y1=\"22\" x2=\"25\" y2=\"22\" />\n  <line x1=\"0\" y1=\"30\" x2=\"25\" y2=\"30\" />\n  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n  <line x1=\"0\" y1=\"41\" x2=\"25\" y2=\"41\" />\n  <line x1=\"0\" y1=\"48\" x2=\"25\" y2=\"48\" />\n  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n  <line x1=\"0\" y1=\"60\" x2=\"25\" y2=\"60\" />\n  <line x1=\"0\" y1=\"67\" x2=\"25\" y2=\"67\" />\n  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n  <line x1=\"0\" y1=\"78\" x2=\"25\" y2=\"78\" />\n  <line x1=\"0\" y1=\"86\" x2=\"25\" y2=\"86\" />\n  <line x1=\"0\" y1=\"93\" x2=\"25\" y2=\"93\" />\n  <line x1=\"0\" y1=\"97\" x2=\"25\" y2=\"97\" />\n  <line x1=\"0\" y1=\"105\" x2=\"25\" y2=\"105\" />\n  <line x1=\"0\" y1=\"112\" x2=\"25\" y2=\"112\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">6599680</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-d02f5ab7-5779-424e-94a4-a1f130117e3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d02f5ab7-5779-424e-94a4-a1f130117e3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d4076cc-5869-4acc-b6ae-9fb522685661' class='xr-var-data-in' type='checkbox'><label for='data-9d4076cc-5869-4acc-b6ae-9fb522685661' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>convective precipitation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_convective_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>e</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-cafb6306-ceb6-476b-8a98-9c80056cabee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cafb6306-ceb6-476b-8a98-9c80056cabee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d414cd41-b532-44eb-ab17-59548bb2e762' class='xr-var-data-in' type='checkbox'><label for='data-d414cd41-b532-44eb-ab17-59548bb2e762' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>es</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-62d4f7a1-c8e1-47c8-9c30-2859daf37c38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62d4f7a1-c8e1-47c8-9c30-2859daf37c38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f94c35d-1566-4bb4-9ebb-b83a7088f3c5' class='xr-var-data-in' type='checkbox'><label for='data-5f94c35d-1566-4bb4-9ebb-b83a7088f3c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow evaporation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_evaporation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ewss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-00e9d24a-4d38-4a66-980a-7d75344bc4eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00e9d24a-4d38-4a66-980a-7d75344bc4eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5143919-9865-4425-9fc4-9d8d6483c8f6' class='xr-var-data-in' type='checkbox'><label for='data-d5143919-9865-4425-9fc4-9d8d6483c8f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>eastward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_eastward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-cd5f13d8-7eec-4281-a028-228ee355ccec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd5f13d8-7eec-4281-a028-228ee355ccec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9542286d-4182-445a-94e3-e0f3a5225c66' class='xr-var-data-in' type='checkbox'><label for='data-9542286d-4182-445a-94e3-e0f3a5225c66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_vel</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nsss</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e87fcf8a-1309-4a9b-b456-b4e3565d244c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e87fcf8a-1309-4a9b-b456-b4e3565d244c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3059715-6e94-4b52-9dbe-0b12d1c9dbbc' class='xr-var-data-in' type='checkbox'><label for='data-e3059715-6e94-4b52-9dbe-0b12d1c9dbbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>northward turbulent surface stress</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_northward_turbulent_stress_amount</dd><dt><span>units :</span></dt><dd>N m-2 s</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-87305c73-b485-4da9-a193-8ba202ad62a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87305c73-b485-4da9-a193-8ba202ad62a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4326fe14-5b67-4312-9178-81d6e5c36269' class='xr-var-data-in' type='checkbox'><label for='data-4326fe14-5b67-4312-9178-81d6e5c36269' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e697ff14-e5d9-4a17-a31a-b8ecc602265a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e697ff14-e5d9-4a17-a31a-b8ecc602265a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7d46d84-fc0c-4de3-a23e-8a668947b1a2' class='xr-var-data-in' type='checkbox'><label for='data-f7d46d84-fc0c-4de3-a23e-8a668947b1a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow density</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>snow_density</dd><dt><span>units :</span></dt><dd>kg m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4fe6c36e-b666-4c60-8845-4d994743c4bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fe6c36e-b666-4c60-8845-4d994743c4bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b7d7137-23fd-44aa-afed-3a0efe1f55f2' class='xr-var-data-in' type='checkbox'><label for='data-9b7d7137-23fd-44aa-afed-3a0efe1f55f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow depth</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-80ed7e3c-798d-49e7-880a-e977d8c5bf10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80ed7e3c-798d-49e7-880a-e977d8c5bf10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3520188c-e3e8-4a3c-afca-d952c87c8e2f' class='xr-var-data-in' type='checkbox'><label for='data-3520188c-e3e8-4a3c-afca-d952c87c8e2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snowfall</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_snowfall_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>skt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-f98afa4f-5e5c-4d00-8e1c-605b4c2a524e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f98afa4f-5e5c-4d00-8e1c-605b4c2a524e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce2d0bc3-ce0f-4945-8264-daa78f13f63a' class='xr-var-data-in' type='checkbox'><label for='data-ce2d0bc3-ce0f-4945-8264-daa78f13f63a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>skin temperature</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>sea_surface_skin_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slhf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-fed11ba3-344a-4067-803c-df853b46236d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fed11ba3-344a-4067-803c-df853b46236d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2560c92-2abf-42b0-8c04-b01c5cf49c9f' class='xr-var-data-in' type='checkbox'><label for='data-c2560c92-2abf-42b0-8c04-b01c5cf49c9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_latent_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-deb511cb-25bb-40cf-bd64-20b49519cfe8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-deb511cb-25bb-40cf-bd64-20b49519cfe8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac16e041-32da-4684-921d-c30734cb734c' class='xr-var-data-in' type='checkbox'><label for='data-ac16e041-32da-4684-921d-c30734cb734c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>snow melt</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_snow_melt_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-40a81105-fa21-4888-b0ef-1afe102415dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40a81105-fa21-4888-b0ef-1afe102415dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04e5e43c-aeab-47b9-bc0d-c464e37c2d1a' class='xr-var-data-in' type='checkbox'><label for='data-04e5e43c-aeab-47b9-bc0d-c464e37c2d1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sro</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-5f1ca511-2330-404e-9510-7a0bb05d0bea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f1ca511-2330-404e-9510-7a0bb05d0bea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b7aade0-020f-43ce-9fe4-c16dfbc7729b' class='xr-var-data-in' type='checkbox'><label for='data-8b7aade0-020f-43ce-9fe4-c16dfbc7729b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface runoff</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  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<text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sshf</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-114e0d0d-0d5a-490b-871d-66877dff5f2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-114e0d0d-0d5a-490b-871d-66877dff5f2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-147ebf2f-cc63-4775-8495-3cb9f896d26a' class='xr-var-data-in' type='checkbox'><label for='data-147ebf2f-cc63-4775-8495-3cb9f896d26a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downward_sensible_heat_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-b46557bf-9dea-40f3-9a0a-edbee9ff4ef7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b46557bf-9dea-40f3-9a0a-edbee9ff4ef7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c3d3371-bbf0-49ab-a0d3-08d0f2912d1e' class='xr-var-data-in' type='checkbox'><label for='data-1c3d3371-bbf0-49ab-a0d3-08d0f2912d1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-8f1c36d4-6dfd-460a-afad-0d6d60740542' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f1c36d4-6dfd-460a-afad-0d6d60740542' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a246319d-a3b6-4ec6-bf7c-602de987295e' class='xr-var-data-in' type='checkbox'><label for='data-a246319d-a3b6-4ec6-bf7c-602de987295e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-819e1677-3092-4e29-8ac9-a3a6bde89661' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-819e1677-3092-4e29-8ac9-a3a6bde89661' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1458db5b-4208-4de5-8777-b252f2053557' class='xr-var-data-in' type='checkbox'><label for='data-1458db5b-4208-4de5-8777-b252f2053557' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl1</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-6b938ec3-6efd-4ed9-9c5f-9f1a128c6051' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b938ec3-6efd-4ed9-9c5f-9f1a128c6051' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5c12a76-5d90-4b35-8e2d-820c1fa164c7' class='xr-var-data-in' type='checkbox'><label for='data-d5c12a76-5d90-4b35-8e2d-820c1fa164c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_1</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-267b930f-33c5-4646-b0fa-48668d7c52ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-267b930f-33c5-4646-b0fa-48668d7c52ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27e297cd-8303-4760-afa5-5d51410fea0c' class='xr-var-data-in' type='checkbox'><label for='data-27e297cd-8303-4760-afa5-5d51410fea0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_2</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line 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font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e5183074-9688-4a60-b75e-b8d96234dc71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5183074-9688-4a60-b75e-b8d96234dc71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35ff5bb7-a575-4be7-aad1-413575bbd180' class='xr-var-data-in' type='checkbox'><label for='data-35ff5bb7-a575-4be7-aad1-413575bbd180' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_3</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stl4</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-e7544bb1-dfc9-45b3-8429-4157a74daccf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7544bb1-dfc9-45b3-8429-4157a74daccf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79cc9068-dc2d-4636-bd8e-aa512ef11633' class='xr-var-data-in' type='checkbox'><label for='data-79cc9068-dc2d-4636-bd8e-aa512ef11633' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>soil temperature layer 4</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>soil_temperature_layer_4</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>str</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-53352f66-b897-41e9-bc37-d7ff23de385b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53352f66-b897-41e9-bc37-d7ff23de385b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-385e5315-6e95-48eb-b9d3-089ad81eb391' class='xr-var-data-in' type='checkbox'><label for='data-385e5315-6e95-48eb-b9d3-089ad81eb391' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-eb98a574-520a-40f1-bc4e-de3c37291683' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb98a574-520a-40f1-bc4e-de3c37291683' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f2b56fd-0b23-4fce-a135-27dc089ada6c' class='xr-var-data-in' type='checkbox'><label for='data-2f2b56fd-0b23-4fce-a135-27dc089ada6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>strd</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-818e3656-f743-4423-8b9c-00e80509bec6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-818e3656-f743-4423-8b9c-00e80509bec6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7cd45bc-15c5-4138-856c-c1b4afa201dc' class='xr-var-data-in' type='checkbox'><label for='data-f7cd45bc-15c5-4138-856c-c1b4afa201dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>surface downward thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl1</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-026393c4-0ebb-4cbd-a7ed-256c325eb65a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-026393c4-0ebb-4cbd-a7ed-256c325eb65a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95e1d580-0a91-4d69-b00e-6d76820ef19e' class='xr-var-data-in' type='checkbox'><label for='data-95e1d580-0a91-4d69-b00e-6d76820ef19e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_1</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl2</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-c59378fa-b6cf-4b46-8541-ce780b77ba9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c59378fa-b6cf-4b46-8541-ce780b77ba9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f2c65e9-452b-4edf-bb7a-9a6aa2ce276e' class='xr-var-data-in' type='checkbox'><label for='data-5f2c65e9-452b-4edf-bb7a-9a6aa2ce276e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 2</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_2</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl3</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a7a44a9b-08bc-486b-900e-e619b0c6bfad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7a44a9b-08bc-486b-900e-e619b0c6bfad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db12561b-d4f9-457c-ae06-523e09305fdb' class='xr-var-data-in' type='checkbox'><label for='data-db12561b-d4f9-457c-ae06-523e09305fdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 3</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_3</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swvl4</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-ba856725-29ab-4d91-852a-ca464c824b87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba856725-29ab-4d91-852a-ca464c824b87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eabfdad-ff1a-4bee-9000-de19139981aa' class='xr-var-data-in' type='checkbox'><label for='data-3eabfdad-ff1a-4bee-9000-de19139981aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>volumetric soil water layer 4</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>volume_fraction_of_water_in_soil_layer_4</dd><dt><span>units :</span></dt><dd>m3 m-3</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tcc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-12320443-fd2a-4af5-bfc6-f15e586e2a67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12320443-fd2a-4af5-bfc6-f15e586e2a67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7418ec53-9da4-4a85-841e-0a966f57a24b' class='xr-var-data-in' type='checkbox'><label for='data-7418ec53-9da4-4a85-841e-0a966f57a24b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tciw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-03c6967c-70be-48c2-8b83-01ee3a5cb14b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03c6967c-70be-48c2-8b83-01ee3a5cb14b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-370bccd2-de53-46b4-8686-fa1d06f263fc' class='xr-var-data-in' type='checkbox'><label for='data-370bccd2-de53-46b4-8686-fa1d06f263fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column ice water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_ice_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" 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Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tclw</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-0f5975b8-be06-4155-b08a-4d20ef3637a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f5975b8-be06-4155-b08a-4d20ef3637a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7277af39-8c56-429a-b843-448fcfa6aeeb' class='xr-var-data-in' type='checkbox'><label for='data-7277af39-8c56-429a-b843-448fcfa6aeeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column liquid water</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_liquid_water</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-e20cd6c8-45f1-4c05-9436-fd2511eb4c6d' class='xr-var-data-in' type='checkbox'><label for='data-e20cd6c8-45f1-4c05-9436-fd2511eb4c6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total column water vapor</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>atmosphere_mass_content_of_water_vapor</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-99e4eeac-a6f1-49c3-a509-71607cc2fe32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99e4eeac-a6f1-49c3-a509-71607cc2fe32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b0821e4-5571-4475-8dbf-7ec6d6897296' class='xr-var-data-in' type='checkbox'><label for='data-4b0821e4-5571-4475-8dbf-7ec6d6897296' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"34\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"34\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"34\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"34\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"34\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"34\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"34\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"34\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"34\" y2=\"54\" />\n  <line x1=\"0\" y1=\"63\" x2=\"34\" y2=\"63\" />\n  <line x1=\"0\" y1=\"69\" x2=\"34\" y2=\"69\" />\n  <line x1=\"0\" y1=\"75\" x2=\"34\" y2=\"75\" />\n  <line x1=\"0\" y1=\"81\" x2=\"34\" y2=\"81\" />\n  <line x1=\"0\" y1=\"87\" x2=\"34\" y2=\"87\" />\n  <line x1=\"0\" y1=\"93\" x2=\"34\" y2=\"93\" />\n  <line x1=\"0\" y1=\"99\" x2=\"34\" y2=\"99\" />\n  <line x1=\"0\" y1=\"105\" x2=\"34\" y2=\"105\" />\n  <line x1=\"0\" y1=\"111\" x2=\"34\" y2=\"111\" />\n  <line x1=\"0\" y1=\"120\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 34.50101617379934,0.0 34.50101617379934,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"17.250508\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"54.501016\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,54.501016,60.000000)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_instant_bounds</span></div><div class='xr-var-dims'>(time_counter, axis_nbounds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-d3a50cf8-246d-4368-b0af-ffb10a4daf7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3a50cf8-246d-4368-b0af-ffb10a4daf7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e82765a7-1fd7-443c-86c0-1616ab979ba6' class='xr-var-data-in' type='checkbox'><label for='data-e82765a7-1fd7-443c-86c0-1616ab979ba6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 640 B </td>\n                        <td> 16 B </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 2) </td>\n                        <td> (1, 2) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 40 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"84\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"34\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"6\" x2=\"34\" y2=\"6\" />\n  <line x1=\"0\" y1=\"12\" x2=\"34\" y2=\"12\" />\n  <line x1=\"0\" y1=\"18\" x2=\"34\" y2=\"18\" />\n  <line x1=\"0\" y1=\"24\" x2=\"34\" y2=\"24\" />\n  <line x1=\"0\" y1=\"30\" x2=\"34\" y2=\"30\" />\n  <line x1=\"0\" y1=\"36\" x2=\"34\" y2=\"36\" />\n  <line x1=\"0\" y1=\"42\" x2=\"34\" y2=\"42\" />\n  <line x1=\"0\" y1=\"48\" x2=\"34\" y2=\"48\" />\n  <line x1=\"0\" y1=\"54\" x2=\"34\" y2=\"54\" />\n  <line x1=\"0\" y1=\"63\" x2=\"34\" y2=\"63\" />\n  <line x1=\"0\" y1=\"69\" x2=\"34\" y2=\"69\" />\n  <line x1=\"0\" y1=\"75\" x2=\"34\" y2=\"75\" />\n  <line x1=\"0\" y1=\"81\" x2=\"34\" y2=\"81\" />\n  <line x1=\"0\" y1=\"87\" x2=\"34\" y2=\"87\" />\n  <line x1=\"0\" y1=\"93\" x2=\"34\" y2=\"93\" />\n  <line x1=\"0\" y1=\"99\" x2=\"34\" y2=\"99\" />\n  <line x1=\"0\" y1=\"105\" x2=\"34\" y2=\"105\" />\n  <line x1=\"0\" y1=\"111\" x2=\"34\" y2=\"111\" />\n  <line x1=\"0\" y1=\"120\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 34.50101617379934,0.0 34.50101617379934,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"17.250508\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n  <text x=\"54.501016\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,54.501016,60.000000)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tisr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-942bf4ef-4452-44cc-8137-e5b331e0558c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-942bf4ef-4452-44cc-8137-e5b331e0558c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-868e83cc-93b9-490a-9556-3d9c7d822709' class='xr-var-data-in' type='checkbox'><label for='data-868e83cc-93b9-490a-9556-3d9c7d822709' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>toa incident solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_incoming_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-34b2d553-e549-44df-b1f5-44bb92a144b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34b2d553-e549-44df-b1f5-44bb92a144b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b757ca5-9535-41c9-bea1-b60de7caaf30' class='xr-var-data-in' type='checkbox'><label for='data-5b757ca5-9535-41c9-bea1-b60de7caaf30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>total precipitation (rain+snow)</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_precipitation_amount</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsn</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4c0cb41b-e1ff-48a4-a9e0-7acfb930ad68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c0cb41b-e1ff-48a4-a9e0-7acfb930ad68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-127f9ce1-818f-4eba-a80c-f7e7276f61be' class='xr-var-data-in' type='checkbox'><label for='data-127f9ce1-818f-4eba-a80c-f7e7276f61be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>temperature of snow layer</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>temperature_in_surface_snow</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-ebdd66a8-5f19-4211-909f-853d64ba3d4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebdd66a8-5f19-4211-909f-853d64ba3d4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd9a6303-08ce-47ce-a798-f4a3ab820b09' class='xr-var-data-in' type='checkbox'><label for='data-dd9a6303-08ce-47ce-a798-f4a3ab820b09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tsrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4ee3570f-1375-46ce-8e09-12ff4cceb5c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ee3570f-1375-46ce-8e09-12ff4cceb5c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81b11628-1336-4d27-949a-997802529550' class='xr-var-data-in' type='checkbox'><label for='data-81b11628-1336-4d27-949a-997802529550' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky solar radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_shortwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttr</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-a171ebb9-3c27-4155-bff5-5456800d7f92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a171ebb9-3c27-4155-bff5-5456800d7f92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-248f1218-3948-4c0d-97dc-cca6c4536990' class='xr-var-data-in' type='checkbox'><label for='data-248f1218-3948-4c0d-97dc-cca6c4536990' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"20\" x2=\"120\" y2=\"20\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttrc</span></div><div class='xr-var-dims'>(time_counter, cell)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 412480), meta=np.ndarray&gt;</div><input id='attrs-4bfd705f-6583-409d-b7f0-85bd40c51aec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bfd705f-6583-409d-b7f0-85bd40c51aec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20f26542-ba1e-46e4-bb48-6dd6b0e4ccb3' class='xr-var-data-in' type='checkbox'><label for='data-20f26542-ba1e-46e4-bb48-6dd6b0e4ccb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>interval_operation :</span></dt><dd>3 h</dd><dt><span>interval_write :</span></dt><dd>3 h</dd><dt><span>long_name :</span></dt><dd>top net clear sky thermal radiation</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>standard_name :</span></dt><dd>toa_net_downward_longwave_flux_assuming_clear_sky</dd><dt><span>units :</span></dt><dd>J m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.97 GiB </td>\n                        <td> 3.15 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (40, 6599680) </td>\n                        <td> (1, 412480) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 640 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" 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/>\n  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >6599680</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">40</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a35cb2a6-4e55-4af7-852a-55c4b9daf67c' class='xr-section-summary-in' type='checkbox'  ><label for='section-a35cb2a6-4e55-4af7-852a-55c4b9daf67c' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time_counter</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0e8f63d1-f826-4286-9792-ac242471765d' class='xr-index-data-in' type='checkbox'/><label for='index-0e8f63d1-f826-4286-9792-ac242471765d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2009-10-15 15:00:00&#x27;, &#x27;2009-10-15 18:00:00&#x27;,\n               &#x27;2009-10-15 21:00:00&#x27;, &#x27;2009-10-16 00:00:00&#x27;,\n               &#x27;2009-10-16 03:00:00&#x27;, &#x27;2009-10-16 06:00:00&#x27;,\n               &#x27;2009-10-16 09:00:00&#x27;, &#x27;2009-10-16 12:00:00&#x27;,\n               &#x27;2009-10-16 15:00:00&#x27;, &#x27;2009-10-16 18:00:00&#x27;,\n               &#x27;2009-10-16 21:00:00&#x27;, &#x27;2009-10-17 00:00:00&#x27;,\n               &#x27;2009-10-17 03:00:00&#x27;, &#x27;2009-10-17 06:00:00&#x27;,\n               &#x27;2009-10-17 09:00:00&#x27;, &#x27;2009-10-17 12:00:00&#x27;,\n               &#x27;2009-10-17 15:00:00&#x27;, &#x27;2009-10-17 18:00:00&#x27;,\n               &#x27;2009-10-17 21:00:00&#x27;, &#x27;2009-10-18 00:00:00&#x27;,\n               &#x27;2009-10-18 03:00:00&#x27;, &#x27;2009-10-18 06:00:00&#x27;,\n               &#x27;2009-10-18 09:00:00&#x27;, &#x27;2009-10-18 12:00:00&#x27;,\n               &#x27;2009-10-18 15:00:00&#x27;, &#x27;2009-10-18 18:00:00&#x27;,\n               &#x27;2009-10-18 21:00:00&#x27;, &#x27;2009-10-19 00:00:00&#x27;,\n               &#x27;2009-10-19 03:00:00&#x27;, &#x27;2009-10-19 06:00:00&#x27;,\n               &#x27;2009-10-19 09:00:00&#x27;, &#x27;2009-10-19 12:00:00&#x27;,\n               &#x27;2009-10-19 15:00:00&#x27;, &#x27;2009-10-19 18:00:00&#x27;,\n               &#x27;2009-10-19 21:00:00&#x27;, &#x27;2009-10-20 00:00:00&#x27;,\n               &#x27;2009-10-20 03:00:00&#x27;, &#x27;2009-10-20 06:00:00&#x27;,\n               &#x27;2009-10-20 09:00:00&#x27;, &#x27;2009-10-20 12:00:00&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_counter&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-efd6dfe5-3122-4e56-9b00-6d34d7bf4e5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-efd6dfe5-3122-4e56-9b00-6d34d7bf4e5b' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>name :</span></dt><dd>HighResMIP_3h_reduced_sfc</dd><dt><span>timeStamp :</span></dt><dd>2024-Apr-05 10:55:20 GMT</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>uuid :</span></dt><dd>dbac5691-165e-4465-bd75-e403aa400071</dd></dl></div></li></ul></div></div>"
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 7
+      "outputs": [],
+      "execution_count": null
     },
     {
       "id": "32f314be-e6dc-4a81-b12c-c740fcc043d9",
@@ -241,17 +202,8 @@
       "metadata": {
         "trusted": true
       },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 700x600 with 2 Axes>",
-            "image/png": 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"
-          },
-          "metadata": {}
-        }
-      ],
-      "execution_count": 8
+      "outputs": [],
+      "execution_count": null
     },
     {
       "id": "e909c9f3-42f8-46d6-8d04-8f0a15106545",
diff --git a/04-example-datasets/03-NextGEMS.ipynb b/04-example-datasets/03-NextGEMS.ipynb
index d00b7f3..35937c0 100644
--- a/04-example-datasets/03-NextGEMS.ipynb
+++ b/04-example-datasets/03-NextGEMS.ipynb
@@ -1,1436 +1,247 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
-   "metadata": {},
-   "source": [
-    "# NextGEMS data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
-   "metadata": {},
-   "source": [
-    "> [!NOTE]\n",
-    "> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n",
-    "\n",
-    "> [!CAUTION]\n",
-    "> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "463bf2c0-ac3f-4786-9687-362393802dce",
-   "metadata": {},
-   "source": [
-    "This notebook is a comprehensive guide to accessing a Zarr dataset for an ICON simulation within the 4th model cycle phase of the NextGEMS project. It includes details about the available parameters and steps, along with instructions for using the `load_NextGEMS_data` function to access these data from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/EW3_ICON_ngc4008_90d_Compression_test.zarr/). The Zarr data is not downloaded locally; instead, it is accessed remotely using `zarr`.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "## Experiment Details and Available Data Fields\n",
-    "\n",
-    "The dataset comprises output data from an ICON run within the 4th model cycle phase of the NextGEMS project (NGC4008). This simulation spans a total of 30 years and uses an ssp370-like scenario run from 2020.\n",
-    "The simulation was performed using a horizontal grid spacing of approximately 100 km (R2B8/8). \n",
-    "A global, coupled simulation covering a time period of 1000 years, starting in 1300, was run. 48-hourly output data was generated throughout the simulation.\n",
-    "However, only a subset of the data is provided: three months of data for four selected variables at three model levels.\n",
-    "The data is stored on a HEALPix grid with lossless compression, and the total size of the available dataset is approximately 9 GB.\n",
-    "\n",
-    "The dataset is contained in a Zarr directory.\n",
-    "\n",
-    "The following 2D parameters are available:\n",
-    "```python\n",
-    "[\"hus\", \"pr\" \"ua\", \"va\"]\n",
-    "```\n",
-    "The following model levels are available:\n",
-    "```python\n",
-    "[89, 65, 55]\n",
-    "```\n",
-    "\n",
-    "\n",
-    "The available time steps begin at 2020-01-02 00:00:00 UTC, increasing in 2-day steps until 2020-03-31 00:00:00 UTC.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
-   "metadata": {},
-   "source": [
-    "## Using the `load_NextGEMS_data` Function\n",
-    "\n",
-    "To load the Zarr directory, you can use the following function:\n",
-    "\n",
-    "```python\n",
-    "load_NextGEMS_data()\n",
-    "```\n",
-    "\n",
-    "This function is straightforward and does not require any parameters.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# URL of S3 bucket\n",
-    "BASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
-   "metadata": {},
-   "source": [
-    "First, the package `zarr` (modern dataset format that is specifically designed for chunked access) and its dependencies need to be imported for the remote access."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import aiohttp\n",
-    "import dask\n",
-    "import fsspec\n",
-    "import zarr"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b7b8524c-1fc2-4fd1-94dc-5c9233adae6b",
-   "metadata": {},
-   "source": [
-    "The `load_NextGEMS_data` function simplifies remote access to Zarr datasets stored on the S3 bucket. It loads the data efficiently as an xarray dataset.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "0b393415-87ca-4dc7-8c72-4cdd101d6621",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "import requests\n",
-    "import xarray as xr\n",
-    "\n",
-    "\n",
-    "def load_NextGEMS_data():\n",
-    "\n",
-    "    url = f\"{BASE_URL}/EW3_ICON_ngc4008_90d_Compression_test.zarr\"\n",
-    "\n",
-    "    print(f\"Loading dataset {url}\")\n",
-    "\n",
-    "    return xr.open_dataset( url, engine=\"zarr\", chunks=dict(),)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "62adb1fd-1fd1-4c59-a0c3-66710f4a31d1",
-   "metadata": {},
-   "source": [
-    "## Example Usage\n",
-    "\n",
-    "To load the Zarr directory, you can use the following example. This will load the whole dataset. "
-   ]
+  "metadata": {
+    "kernelspec": {
+      "name": "python",
+      "display_name": "Python (Pyodide)",
+      "language": "python"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "python",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8"
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "510bad61-a98c-495b-a9fa-365887b9f1b6",
-   "metadata": {},
-   "outputs": [
+  "nbformat_minor": 5,
+  "nbformat": 4,
+  "cells": [
+    {
+      "id": "503b82f6-82f7-4f6a-8127-7144c4c08052",
+      "cell_type": "markdown",
+      "source": "# NextGEMS data",
+      "metadata": {}
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/EW3_ICON_ngc4008_90d_Compression_test.zarr\n"
-     ]
+      "id": "33d6bacd-2341-478e-92ac-22d0d00d96c6",
+      "cell_type": "markdown",
+      "source": "> [!NOTE]\n> The online laboratory has only been tested in recent Firefox and Chrome browsers. Some features may not (yet) be supported in Safari browsers.\n\n> [!CAUTION]\n> Any changes you make to this notebook will be lost once the page is closed or refreshed. Please download any files you would like to keep.",
+      "metadata": {}
     },
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:     (time: 90, level_full: 3, cell: 3145728)\n",
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-       "    pr          (time, cell) float32 dask.array&lt;chunksize=(3, 196608), meta=np.ndarray&gt;\n",
-       "    ua          (time, level_full, cell) float32 dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;\n",
-       "    va          (time, level_full, cell) float32 dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-25de3706-ddf7-472c-bc28-9067e9c191b4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-25de3706-ddf7-472c-bc28-9067e9c191b4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 90</li><li><span class='xr-has-index'>level_full</span>: 3</li><li><span>cell</span>: 3145728</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2e239176-5d5d-4919-8edc-248fc538e858' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2e239176-5d5d-4919-8edc-248fc538e858' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level_full</span></div><div class='xr-var-dims'>(level_full)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.0 65.0 55.0</div><input id='attrs-b14e4269-2e46-47f1-9b20-577a6d8bde92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b14e4269-2e46-47f1-9b20-577a6d8bde92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eb1c4f3-f4d7-4a35-b814-1dcb82cf22d7' class='xr-var-data-in' type='checkbox'><label for='data-5eb1c4f3-f4d7-4a35-b814-1dcb82cf22d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>generalized_height</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array([89., 65., 55.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-02 ... 2020-03-31</div><input id='attrs-b82b151e-42a0-4141-953c-4d03c95b569e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b82b151e-42a0-4141-953c-4d03c95b569e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff28a181-c830-423b-b12c-77e05a171322' class='xr-var-data-in' type='checkbox'><label for='data-ff28a181-c830-423b-b12c-77e05a171322' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-02T00:00:00.000000000&#x27;, &#x27;2020-01-03T00:00:00.000000000&#x27;,\n",
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-       "       &#x27;2020-03-20T00:00:00.000000000&#x27;, &#x27;2020-03-21T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-03-22T00:00:00.000000000&#x27;, &#x27;2020-03-23T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-03-24T00:00:00.000000000&#x27;, &#x27;2020-03-25T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-03-26T00:00:00.000000000&#x27;, &#x27;2020-03-27T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2020-03-28T00:00:00.000000000&#x27;, &#x27;2020-03-29T00:00:00.000000000&#x27;,\n",
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-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6cee192-1009-4940-9a74-36a694ed3498' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6cee192-1009-4940-9a74-36a694ed3498' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>hus</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-00584207-7b42-4528-810b-6e02d5a1283a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00584207-7b42-4528-810b-6e02d5a1283a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59dd54d4-398b-4e29-9e76-d195640c3b01' class='xr-var-data-in' type='checkbox'><label for='data-59dd54d4-398b-4e29-9e76-d195640c3b01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 3.16 GiB </td>\n",
-       "                        <td> 2.25 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (90, 3, 3145728) </td>\n",
-       "                        <td> (3, 1, 196608) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1440 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 196608), meta=np.ndarray&gt;</div><input id='attrs-42d80b1b-b4d6-4944-84a9-731e1b51acd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42d80b1b-b4d6-4944-84a9-731e1b51acd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4f51ba8-fa39-4239-9d10-c25f415d0495' class='xr-var-data-in' type='checkbox'><label for='data-a4f51ba8-fa39-4239-9d10-c25f415d0495' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>precipitation flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>vgrid :</span></dt><dd>surface</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 1.05 GiB </td>\n",
-       "                        <td> 2.25 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (90, 3145728) </td>\n",
-       "                        <td> (3, 196608) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 480 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
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-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 3.16 GiB </td>\n",
-       "                        <td> 2.25 MiB </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (90, 3, 3145728) </td>\n",
-       "                        <td> (3, 1, 196608) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1440 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "    <tr>\n",
-       "        <td>\n",
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-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
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-       "                        <td> 2.25 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (90, 3, 3145728) </td>\n",
-       "                        <td> (3, 1, 196608) </td>\n",
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-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1440 chunks in 2 graph layers </td>\n",
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-       "                        <th> Data type </th>\n",
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-       "               &#x27;2020-03-22&#x27;, &#x27;2020-03-23&#x27;, &#x27;2020-03-24&#x27;, &#x27;2020-03-25&#x27;,\n",
-       "               &#x27;2020-03-26&#x27;, &#x27;2020-03-27&#x27;, &#x27;2020-03-28&#x27;, &#x27;2020-03-29&#x27;,\n",
-       "               &#x27;2020-03-30&#x27;, &#x27;2020-03-31&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48e18508-2e06-4c1e-9f7c-1f9685f854be' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-48e18508-2e06-4c1e-9f7c-1f9685f854be' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      "id": "463bf2c0-ac3f-4786-9687-362393802dce",
+      "cell_type": "markdown",
+      "source": "This notebook is a comprehensive guide to accessing a Zarr dataset for an ICON simulation within the 4th model cycle phase of the NextGEMS project. It includes details about the available parameters and steps, along with instructions for using the `load_NextGEMS_data` function to access these data from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/EW3_ICON_ngc4008_90d_Compression_test.zarr/). The Zarr data are not downloaded locally; instead, it is accessed remotely using `zarr`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset comprises output data from an ICON run within the 4th model cycle phase of the NextGEMS project (NGC4008). This simulation spans a total of 30 years and uses an ssp370-like scenario run from 2020.\nThe simulation was performed using a horizontal grid spacing of approximately 100 km (R2B8/8). \nA global, coupled simulation covering a time period of 1000 years, starting in 1300, was run. 48-hourly output data were generated throughout the simulation.\nHowever, only a subset of the data is provided: three months of data for four selected variables at three model levels.\nThe data are stored on a HEALPix grid with lossless compression, and the total size of the available dataset is approximately 9 GB.\n\nThe dataset is contained in a Zarr directory. The data is available for reuse under the Creative Commons Attribution 4.0 International license.\n\nThe following 2D parameters are available:\n```python\n[\"hus\", \"pr\" \"ua\", \"va\"]\n```\nThe following model levels are available:\n```python\n[89, 65, 55]\n```\n\n\nThe available time steps begin at 2020-01-02 00:00:00 UTC, increasing in 2-day steps until 2020-03-31 00:00:00 UTC.\n\n",
+      "metadata": {}
+    },
+    {
+      "id": "b8d06ffa-2c2b-461f-9ee3-0780131f8717",
+      "cell_type": "markdown",
+      "source": "## Using the `load_NextGEMS_data` Function\n\nTo load the Zarr directory, you can use the following function:\n\n```python\nload_NextGEMS_data()\n```\n\nThis function is straightforward and does not require any parameters.\n",
+      "metadata": {}
+    },
+    {
+      "id": "91c98f90-c7c9-4168-abd4-f85b7e008e8e",
+      "cell_type": "code",
+      "source": "# URL of S3 bucket\nBASE_URL = \"https://object-store.os-api.cci1.ecmwf.int/esiwacebucket\"",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": 1
+    },
+    {
+      "id": "6e583dd2-1735-4d6e-8403-413dcdc25ba6",
+      "cell_type": "markdown",
+      "source": "First, the package `zarr` (modern dataset format that is specifically designed for chunked access) and its dependencies need to be imported for the remote access.",
+      "metadata": {}
+    },
+    {
+      "id": "2161d1e4-abda-4c03-8626-1e7ce91dc13e",
+      "cell_type": "code",
+      "source": "import aiohttp\nimport dask\nimport fsspec\nimport zarr",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 71.9MiB (from 49.9MiB) for this notebook\n",
+          "output_type": "stream"
+        }
       ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:     (time: 90, level_full: 3, cell: 3145728)\n",
-       "Coordinates:\n",
-       "  * level_full  (level_full) float64 89.0 65.0 55.0\n",
-       "  * time        (time) datetime64[ns] 2020-01-02 2020-01-03 ... 2020-03-31\n",
-       "Dimensions without coordinates: cell\n",
-       "Data variables:\n",
-       "    hus         (time, level_full, cell) float32 dask.array<chunksize=(3, 1, 196608), meta=np.ndarray>\n",
-       "    pr          (time, cell) float32 dask.array<chunksize=(3, 196608), meta=np.ndarray>\n",
-       "    ua          (time, level_full, cell) float32 dask.array<chunksize=(3, 1, 196608), meta=np.ndarray>\n",
-       "    va          (time, level_full, cell) float32 dask.array<chunksize=(3, 1, 196608), meta=np.ndarray>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds = load_NextGEMS_data()\n",
-    "ds"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8ca30b3f-8c16-4d2b-a390-0977b318d93f",
-   "metadata": {},
-   "source": [
-    "The `earthkit` package is used for plotting the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "0becd6b7-b9c5-4932-aaca-8d1c78dc04cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import earthkit.data\n",
-    "import earthkit.plots.quickmap"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e307d560-3306-41a7-a2a4-01e3fd7b0a51",
-   "metadata": {},
-   "source": [
-    "The data can be visualized as demonstrated below. For example, the specific humidity on **2020-02-01 at 00:00:00 UTC** at model level 89 is plotted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "d19dfa96-dcb4-4b82-b4d4-04c81307936c",
-   "metadata": {},
-   "outputs": [
+      "execution_count": 2
+    },
     {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 700x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "chart = earthkit.plots.Map(extent=[-180, 180, -90, 90])\n",
-    "chart.plot(ds[\"hus\"].sel(time=\"2020-02-01T00:00:00\"))\n",
-    "chart.title(\"Specific humidity \")\n",
-    "chart.coastlines()\n",
-    "chart.gridlines()\n",
-    "chart.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cb706079-df56-4d5e-978f-f362d96e2d19",
-   "metadata": {},
-   "source": [
-    "Visualizing the precipitation flux on **2020-02-01 at 00:00:00 UTC** is tried below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "7e4cc456-744d-42a2-92bd-006dda5b5e03",
-   "metadata": {},
-   "outputs": [
+      "id": "b7b8524c-1fc2-4fd1-94dc-5c9233adae6b",
+      "cell_type": "markdown",
+      "source": "The `load_NextGEMS_data` function simplifies remote access to Zarr datasets stored on the S3 bucket. It loads the data efficiently as an xarray dataset.\n",
+      "metadata": {}
+    },
     {
-     "ename": "UFuncTypeError",
-     "evalue": "ufunc 'less' did not contain a loop with signature matching types (<class 'numpy.dtypes.TimeDelta64DType'>, <class 'numpy.dtypes.Float64DType'>) -> None",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "File \u001b[0;32m~/Documents/ECMWF/Code/earthkit-plots/src/earthkit/plots/components/subplots.py:396\u001b[0m, in \u001b[0;36mSubplot._extract_plottables\u001b[0;34m(self, method_name, args, data, x, y, z, style, units, every, source_units, extract_domain, **kwargs)\u001b[0m\n\u001b[1;32m    395\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 396\u001b[0m     mappable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod_name\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    397\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    398\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
-      "File \u001b[0;32m~/Documents/ECMWF/Code/earthkit-plots/src/earthkit/plots/styles/__init__.py:429\u001b[0m, in \u001b[0;36mStyle.pcolormesh\u001b[0;34m(self, ax, x, y, values, *args, **kwargs)\u001b[0m\n\u001b[1;32m    428\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mto_pcolormesh_kwargs(values), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs}\n\u001b[0;32m--> 429\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpcolormesh\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
-      "File \u001b[0;32m~/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/cartopy/mpl/geoaxes.py:307\u001b[0m, in \u001b[0;36m_add_transform.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    306\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m transform\n\u001b[0;32m--> 307\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/cartopy/mpl/geoaxes.py:1768\u001b[0m, in \u001b[0;36mGeoAxes.pcolormesh\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1767\u001b[0m args, kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_args(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 1768\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpcolormesh\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1769\u001b[0m \u001b[38;5;66;03m# Wrap the quadrilaterals if necessary\u001b[39;00m\n",
-      "File \u001b[0;32m~/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/matplotlib/__init__.py:1412\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1412\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1414\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[0;32m~/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/matplotlib/axes/_axes.py:6058\u001b[0m, in \u001b[0;36mAxes.pcolormesh\u001b[0;34m(self, alpha, norm, cmap, vmin, vmax, shading, antialiased, *args, **kwargs)\u001b[0m\n\u001b[1;32m   6056\u001b[0m kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124medgecolors\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 6058\u001b[0m X, Y, C, shading \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pcolorargs\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpcolormesh\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   6059\u001b[0m \u001b[43m                                    \u001b[49m\u001b[43mshading\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshading\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6060\u001b[0m coords \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mstack([X, Y], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
-      "File \u001b[0;32m~/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/matplotlib/axes/_axes.py:5568\u001b[0m, in \u001b[0;36mAxes._pcolorargs\u001b[0;34m(self, funcname, shading, *args, **kwargs)\u001b[0m\n\u001b[1;32m   5567\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (Nx, Ny) \u001b[38;5;241m!=\u001b[39m (ncols \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m, nrows \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m-> 5568\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDimensions of C \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m are incompatible with\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   5569\u001b[0m                         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m X (\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m) and/or Y (\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m); see help(\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (\n\u001b[1;32m   5570\u001b[0m                             C\u001b[38;5;241m.\u001b[39mshape, Nx, Ny, funcname))\n\u001b[1;32m   5571\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:    \u001b[38;5;66;03m# ['nearest', 'gouraud']:\u001b[39;00m\n",
-      "\u001b[0;31mTypeError\u001b[0m: Dimensions of C (90, 3145728) are incompatible with X (90) and/or Y (3145728); see help(pcolormesh)",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mDTypePromotionError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;31mDTypePromotionError\u001b[0m: The DType <class 'numpy.dtypes.TimeDelta64DType'> could not be promoted by <class 'numpy.dtypes.Float64DType'>. This means that no common DType exists for the given inputs. For example they cannot be stored in a single array unless the dtype is `object`. The full list of DTypes is: (<class 'numpy.dtypes.TimeDelta64DType'>, <class 'numpy.dtypes.Float64DType'>)",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mUFuncTypeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m chart \u001b[38;5;241m=\u001b[39m earthkit\u001b[38;5;241m.\u001b[39mplots\u001b[38;5;241m.\u001b[39mMap(extent\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m180\u001b[39m, \u001b[38;5;241m180\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m90\u001b[39m, \u001b[38;5;241m90\u001b[39m])\n\u001b[0;32m----> 2\u001b[0m \u001b[43mchart\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m chart\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrecipitation flux \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m chart\u001b[38;5;241m.\u001b[39mcoastlines()\n",
-      "File \u001b[0;32m~/Documents/ECMWF/Code/earthkit-plots/src/earthkit/plots/components/subplots.py:597\u001b[0m, in \u001b[0;36mSubplot.plot\u001b[0;34m(self, style, *args, **kwargs)\u001b[0m\n\u001b[1;32m    595\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    596\u001b[0m     method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblock\n\u001b[0;32m--> 597\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/ECMWF/Code/earthkit-plots/src/earthkit/plots/components/subplots.py:227\u001b[0m, in \u001b[0;36mSubplot.plot_3D.<locals>.decorator.<locals>.wrapper\u001b[0;34m(self, data, x, y, z, style, every, **kwargs)\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    219\u001b[0m     data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    225\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    226\u001b[0m ):\n\u001b[0;32m--> 227\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_extract_plottables\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    228\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod_name\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    229\u001b[0m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    230\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    231\u001b[0m \u001b[43m        \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    232\u001b[0m \u001b[43m        \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    233\u001b[0m \u001b[43m        \u001b[49m\u001b[43mz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mz\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    234\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    235\u001b[0m \u001b[43m        \u001b[49m\u001b[43mevery\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevery\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    236\u001b[0m \u001b[43m        \u001b[49m\u001b[43mextract_domain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextract_domain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    237\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    238\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/Documents/ECMWF/Code/earthkit-plots/src/earthkit/plots/components/subplots.py:400\u001b[0m, in \u001b[0;36mSubplot._extract_plottables\u001b[0;34m(self, method_name, args, data, x, y, z, style, units, every, source_units, extract_domain, **kwargs)\u001b[0m\n\u001b[1;32m    396\u001b[0m     mappable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(style, method_name)(\n\u001b[1;32m    397\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39max, x_values, y_values, z_values, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    398\u001b[0m     )\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 400\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mgrids\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_structured\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_values\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    401\u001b[0m         x_values, y_values, z_values \u001b[38;5;241m=\u001b[39m grids\u001b[38;5;241m.\u001b[39minterpolate_unstructured(\n\u001b[1;32m    402\u001b[0m             x_values,\n\u001b[1;32m    403\u001b[0m             y_values,\n\u001b[1;32m    404\u001b[0m             z_values,\n\u001b[1;32m    405\u001b[0m             method\u001b[38;5;241m=\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minterpolation_method\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlinear\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m    406\u001b[0m         )\n\u001b[1;32m    407\u001b[0m         mappable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(style, method_name)(\n\u001b[1;32m    408\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39max, x_values, y_values, z_values, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    409\u001b[0m         )\n",
-      "File \u001b[0;32m~/Documents/ECMWF/Code/earthkit-plots/src/earthkit/plots/geo/grids.py:65\u001b[0m, in \u001b[0;36mis_structured\u001b[0;34m(x, y, tol)\u001b[0m\n\u001b[1;32m     62\u001b[0m x_diff \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdiff(x)\n\u001b[1;32m     63\u001b[0m y_diff \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdiff(y)\n\u001b[0;32m---> 65\u001b[0m x_spacing_consistent \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mall(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mabs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_diff\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mx_diff\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtol\u001b[49m)\n\u001b[1;32m     66\u001b[0m y_spacing_consistent \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mall(np\u001b[38;5;241m.\u001b[39mabs(y_diff \u001b[38;5;241m-\u001b[39m y_diff[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;241m<\u001b[39m tol)\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x_spacing_consistent \u001b[38;5;129;01mand\u001b[39;00m y_spacing_consistent\n",
-      "\u001b[0;31mUFuncTypeError\u001b[0m: ufunc 'less' did not contain a loop with signature matching types (<class 'numpy.dtypes.TimeDelta64DType'>, <class 'numpy.dtypes.Float64DType'>) -> None"
-     ]
+      "id": "0b393415-87ca-4dc7-8c72-4cdd101d6621",
+      "cell_type": "code",
+      "source": "import json\n\nimport requests\nimport xarray as xr\n\n\ndef load_NextGEMS_data():\n\n    url = f\"{BASE_URL}/EW3_ICON_ngc4008_90d_Compression_test.zarr\"\n\n    print(f\"Loading dataset {url}\")\n\n    return xr.open_dataset( url, engine=\"zarr\", chunks=dict(),)",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 149.1MiB (from 71.9MiB) for this notebook\n",
+          "output_type": "stream"
+        }
+      ],
+      "execution_count": 3
     },
     {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 700x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "chart = earthkit.plots.Map(extent=[-180, 180, -90, 90])\n",
-    "chart.plot(ds[\"pr\"])\n",
-    "chart.title(\"Precipitation flux \")\n",
-    "chart.coastlines()\n",
-    "chart.gridlines()\n",
-    "chart.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4521020-47fd-47e9-8254-f83321c920c4",
-   "metadata": {},
-   "source": [
-    "Since tha data is stored on a HEALPix grid `earthkit` cannot be used to plot this data and the `pyicon` library is used here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "962a9a11-d103-4588-ade3-4373c847540c",
-   "metadata": {},
-   "outputs": [
+      "id": "62adb1fd-1fd1-4c59-a0c3-66710f4a31d1",
+      "cell_type": "markdown",
+      "source": "## Example Usage\n\nTo load the Zarr directory, you can use the following example. This will load the whole dataset. ",
+      "metadata": {}
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----Start loading pyicon.\n",
-      "----Start loading pyicon.\n",
-      "----Pyicon was loaded successfully.\n",
-      "----Pyicon was loaded successfully.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pyicon as pyic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "99449502-9fb2-4097-89b0-d63634a0e250",
-   "metadata": {},
-   "source": [
-    "The data can be visualized as demonstrated below. For example, the specific humidity on **2020-02-01 at 00:00:00 UTC** at model level 89 is plotted.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "d792634e-3a22-4e7d-92f8-f81f66eabbde",
-   "metadata": {},
-   "outputs": [
+      "id": "510bad61-a98c-495b-a9fa-365887b9f1b6",
+      "cell_type": "code",
+      "source": "ds = load_NextGEMS_data()\nds",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "Loading dataset https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/EW3_ICON_ngc4008_90d_Compression_test.zarr\n",
+          "output_type": "stream"
+        },
+        {
+          "execution_count": 4,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "<xarray.Dataset> Size: 11GB\nDimensions:     (time: 90, level_full: 3, cell: 3145728)\nCoordinates:\n  * level_full  (level_full) float64 24B 89.0 65.0 55.0\n  * time        (time) datetime64[ns] 720B 2020-01-02 2020-01-03 ... 2020-03-31\nDimensions without coordinates: cell\nData variables:\n    hus         (time, level_full, cell) float32 3GB dask.array<chunksize=(3, 1, 196608), meta=np.ndarray>\n    pr          (time, cell) float32 1GB dask.array<chunksize=(3, 196608), meta=np.ndarray>\n    ua          (time, level_full, cell) float32 3GB dask.array<chunksize=(3, 1, 196608), meta=np.ndarray>\n    va          (time, level_full, cell) float32 3GB dask.array<chunksize=(3, 1, 196608), meta=np.ndarray>",
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.xr-var-data,\n.xr-index-data-in:checked ~ .xr-index-data {\n  display: block;\n}\n\n.xr-var-data > table {\n  float: right;\n}\n\n.xr-var-name span,\n.xr-var-data,\n.xr-index-name div,\n.xr-index-data,\n.xr-attrs {\n  padding-left: 25px !important;\n}\n\n.xr-attrs,\n.xr-var-attrs,\n.xr-var-data,\n.xr-index-data {\n  grid-column: 1 / -1;\n}\n\ndl.xr-attrs {\n  padding: 0;\n  margin: 0;\n  display: grid;\n  grid-template-columns: 125px auto;\n}\n\n.xr-attrs dt,\n.xr-attrs dd {\n  padding: 0;\n  margin: 0;\n  float: left;\n  padding-right: 10px;\n  width: auto;\n}\n\n.xr-attrs dt {\n  font-weight: normal;\n  grid-column: 1;\n}\n\n.xr-attrs dt:hover span {\n  display: inline-block;\n  background: var(--xr-background-color);\n  padding-right: 10px;\n}\n\n.xr-attrs dd {\n  grid-column: 2;\n  white-space: pre-wrap;\n  word-break: break-all;\n}\n\n.xr-icon-database,\n.xr-icon-file-text2,\n.xr-no-icon {\n  display: inline-block;\n  vertical-align: middle;\n  width: 1em;\n  height: 1.5em !important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 11GB\nDimensions:     (time: 90, level_full: 3, cell: 3145728)\nCoordinates:\n  * level_full  (level_full) float64 24B 89.0 65.0 55.0\n  * time        (time) datetime64[ns] 720B 2020-01-02 2020-01-03 ... 2020-03-31\nDimensions without coordinates: cell\nData variables:\n    hus         (time, level_full, cell) float32 3GB dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;\n    pr          (time, cell) float32 1GB dask.array&lt;chunksize=(3, 196608), meta=np.ndarray&gt;\n    ua          (time, level_full, cell) float32 3GB dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;\n    va          (time, level_full, cell) float32 3GB dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f29324c0-129b-403e-9f0e-d45372537a75' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f29324c0-129b-403e-9f0e-d45372537a75' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 90</li><li><span class='xr-has-index'>level_full</span>: 3</li><li><span>cell</span>: 3145728</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-183faa8f-67a3-4c6a-85ab-515135eec829' class='xr-section-summary-in' type='checkbox'  checked><label for='section-183faa8f-67a3-4c6a-85ab-515135eec829' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level_full</span></div><div class='xr-var-dims'>(level_full)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.0 65.0 55.0</div><input id='attrs-b1fef70e-2d27-46f8-9477-822d6f5f8a31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1fef70e-2d27-46f8-9477-822d6f5f8a31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c0a8b44-1645-478f-87c3-664ae8749981' class='xr-var-data-in' type='checkbox'><label for='data-3c0a8b44-1645-478f-87c3-664ae8749981' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>generalized_height</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array([89., 65., 55.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-02 ... 2020-03-31</div><input id='attrs-d436e17d-9e1e-450c-82e3-58a35d37e56c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d436e17d-9e1e-450c-82e3-58a35d37e56c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da9b07fd-81f1-4c19-9ca4-0fc319136acf' class='xr-var-data-in' type='checkbox'><label for='data-da9b07fd-81f1-4c19-9ca4-0fc319136acf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-02T00:00:00.000000000&#x27;, &#x27;2020-01-03T00:00:00.000000000&#x27;,\n       &#x27;2020-01-04T00:00:00.000000000&#x27;, &#x27;2020-01-05T00:00:00.000000000&#x27;,\n       &#x27;2020-01-06T00:00:00.000000000&#x27;, &#x27;2020-01-07T00:00:00.000000000&#x27;,\n       &#x27;2020-01-08T00:00:00.000000000&#x27;, &#x27;2020-01-09T00:00:00.000000000&#x27;,\n       &#x27;2020-01-10T00:00:00.000000000&#x27;, &#x27;2020-01-11T00:00:00.000000000&#x27;,\n       &#x27;2020-01-12T00:00:00.000000000&#x27;, &#x27;2020-01-13T00:00:00.000000000&#x27;,\n       &#x27;2020-01-14T00:00:00.000000000&#x27;, &#x27;2020-01-15T00:00:00.000000000&#x27;,\n       &#x27;2020-01-16T00:00:00.000000000&#x27;, &#x27;2020-01-17T00:00:00.000000000&#x27;,\n       &#x27;2020-01-18T00:00:00.000000000&#x27;, &#x27;2020-01-19T00:00:00.000000000&#x27;,\n       &#x27;2020-01-20T00:00:00.000000000&#x27;, &#x27;2020-01-21T00:00:00.000000000&#x27;,\n       &#x27;2020-01-22T00:00:00.000000000&#x27;, &#x27;2020-01-23T00:00:00.000000000&#x27;,\n       &#x27;2020-01-24T00:00:00.000000000&#x27;, &#x27;2020-01-25T00:00:00.000000000&#x27;,\n       &#x27;2020-01-26T00:00:00.000000000&#x27;, &#x27;2020-01-27T00:00:00.000000000&#x27;,\n       &#x27;2020-01-28T00:00:00.000000000&#x27;, &#x27;2020-01-29T00:00:00.000000000&#x27;,\n       &#x27;2020-01-30T00:00:00.000000000&#x27;, &#x27;2020-01-31T00:00:00.000000000&#x27;,\n       &#x27;2020-02-01T00:00:00.000000000&#x27;, &#x27;2020-02-02T00:00:00.000000000&#x27;,\n       &#x27;2020-02-03T00:00:00.000000000&#x27;, &#x27;2020-02-04T00:00:00.000000000&#x27;,\n       &#x27;2020-02-05T00:00:00.000000000&#x27;, &#x27;2020-02-06T00:00:00.000000000&#x27;,\n       &#x27;2020-02-07T00:00:00.000000000&#x27;, &#x27;2020-02-08T00:00:00.000000000&#x27;,\n       &#x27;2020-02-09T00:00:00.000000000&#x27;, &#x27;2020-02-10T00:00:00.000000000&#x27;,\n       &#x27;2020-02-11T00:00:00.000000000&#x27;, &#x27;2020-02-12T00:00:00.000000000&#x27;,\n       &#x27;2020-02-13T00:00:00.000000000&#x27;, &#x27;2020-02-14T00:00:00.000000000&#x27;,\n       &#x27;2020-02-15T00:00:00.000000000&#x27;, &#x27;2020-02-16T00:00:00.000000000&#x27;,\n       &#x27;2020-02-17T00:00:00.000000000&#x27;, &#x27;2020-02-18T00:00:00.000000000&#x27;,\n       &#x27;2020-02-19T00:00:00.000000000&#x27;, &#x27;2020-02-20T00:00:00.000000000&#x27;,\n       &#x27;2020-02-21T00:00:00.000000000&#x27;, &#x27;2020-02-22T00:00:00.000000000&#x27;,\n       &#x27;2020-02-23T00:00:00.000000000&#x27;, &#x27;2020-02-24T00:00:00.000000000&#x27;,\n       &#x27;2020-02-25T00:00:00.000000000&#x27;, &#x27;2020-02-26T00:00:00.000000000&#x27;,\n       &#x27;2020-02-27T00:00:00.000000000&#x27;, &#x27;2020-02-28T00:00:00.000000000&#x27;,\n       &#x27;2020-02-29T00:00:00.000000000&#x27;, &#x27;2020-03-01T00:00:00.000000000&#x27;,\n       &#x27;2020-03-02T00:00:00.000000000&#x27;, &#x27;2020-03-03T00:00:00.000000000&#x27;,\n       &#x27;2020-03-04T00:00:00.000000000&#x27;, &#x27;2020-03-05T00:00:00.000000000&#x27;,\n       &#x27;2020-03-06T00:00:00.000000000&#x27;, &#x27;2020-03-07T00:00:00.000000000&#x27;,\n       &#x27;2020-03-08T00:00:00.000000000&#x27;, &#x27;2020-03-09T00:00:00.000000000&#x27;,\n       &#x27;2020-03-10T00:00:00.000000000&#x27;, &#x27;2020-03-11T00:00:00.000000000&#x27;,\n       &#x27;2020-03-12T00:00:00.000000000&#x27;, &#x27;2020-03-13T00:00:00.000000000&#x27;,\n       &#x27;2020-03-14T00:00:00.000000000&#x27;, &#x27;2020-03-15T00:00:00.000000000&#x27;,\n       &#x27;2020-03-16T00:00:00.000000000&#x27;, &#x27;2020-03-17T00:00:00.000000000&#x27;,\n       &#x27;2020-03-18T00:00:00.000000000&#x27;, &#x27;2020-03-19T00:00:00.000000000&#x27;,\n       &#x27;2020-03-20T00:00:00.000000000&#x27;, &#x27;2020-03-21T00:00:00.000000000&#x27;,\n       &#x27;2020-03-22T00:00:00.000000000&#x27;, &#x27;2020-03-23T00:00:00.000000000&#x27;,\n       &#x27;2020-03-24T00:00:00.000000000&#x27;, &#x27;2020-03-25T00:00:00.000000000&#x27;,\n       &#x27;2020-03-26T00:00:00.000000000&#x27;, &#x27;2020-03-27T00:00:00.000000000&#x27;,\n       &#x27;2020-03-28T00:00:00.000000000&#x27;, &#x27;2020-03-29T00:00:00.000000000&#x27;,\n       &#x27;2020-03-30T00:00:00.000000000&#x27;, &#x27;2020-03-31T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9cbe790f-c673-40fa-a9d8-79a9b9d9d19b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9cbe790f-c673-40fa-a9d8-79a9b9d9d19b' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>hus</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-beafa4a2-3d69-48c2-aa42-ec7ca0801052' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-beafa4a2-3d69-48c2-aa42-ec7ca0801052' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba0a387b-46d2-455c-81e8-a0eef8049164' class='xr-var-data-in' type='checkbox'><label for='data-ba0a387b-46d2-455c-81e8-a0eef8049164' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Specific humidity</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 3.16 GiB </td>\n                        <td> 2.25 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (90, 3, 3145728) </td>\n                        <td> (3, 1, 196608) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1440 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"8\" x2=\"24\" y2=\"23\" />\n  <line x1=\"10\" y1=\"16\" x2=\"24\" y2=\"31\" />\n  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"26\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"27\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"28\" />\n  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"28\" />\n  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"29\" />\n  <line x1=\"15\" y1=\"5\" 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font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">90</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pr</span></div><div class='xr-var-dims'>(time, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 196608), meta=np.ndarray&gt;</div><input id='attrs-fef8f872-f3b5-46c3-8b04-6b9d04ca7eba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fef8f872-f3b5-46c3-8b04-6b9d04ca7eba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b602d5f0-0467-434e-9357-84aa2ec359b7' class='xr-var-data-in' type='checkbox'><label for='data-b602d5f0-0467-434e-9357-84aa2ec359b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>precipitation flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>vgrid :</span></dt><dd>surface</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.05 GiB </td>\n                        <td> 2.25 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (90, 3145728) </td>\n                        <td> (3, 196608) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 480 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" 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>3145728</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">90</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ua</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-42bde9c9-ee4c-4fe8-a022-58feaa37ed28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42bde9c9-ee4c-4fe8-a022-58feaa37ed28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5fcb84a-beb2-4e59-bc9a-7cceae76575e' class='xr-var-data-in' type='checkbox'><label for='data-e5fcb84a-beb2-4e59-bc9a-7cceae76575e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Zonal wind</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 3.16 GiB </td>\n                        <td> 2.25 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (90, 3, 3145728) </td>\n                        <td> (3, 1, 196608) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1440 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"8\" x2=\"24\" y2=\"23\" />\n  <line x1=\"10\" y1=\"16\" x2=\"24\" y2=\"31\" />\n  <line 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points=\"24.948597949754408,14.948597949754406 144.9485979497544,14.948597949754406 144.9485979497544,40.36121446433689 24.948597949754408,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3145728</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">3</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">90</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>va</span></div><div class='xr-var-dims'>(time, level_full, cell)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 1, 196608), meta=np.ndarray&gt;</div><input id='attrs-f3416e2e-b3f3-4693-931d-df56eb90c85c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3416e2e-b3f3-4693-931d-df56eb90c85c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5aa2daf-ea7d-489c-b503-0d1fdbb11cd8' class='xr-var-data-in' type='checkbox'><label for='data-c5aa2daf-ea7d-489c-b503-0d1fdbb11cd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: mean cell: mean</dd><dt><span>component :</span></dt><dd>atmo</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>Meridional wind</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>vgrid :</span></dt><dd>reference</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 3.16 GiB </td>\n                        <td> 2.25 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (90, 3, 3145728) </td>\n                        <td> (3, 1, 196608) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1440 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"8\" x2=\"24\" y2=\"23\" />\n  <line x1=\"10\" y1=\"16\" x2=\"24\" y2=\"31\" />\n  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"26\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"27\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"28\" />\n  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"28\" />\n  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"29\" />\n  <line x1=\"15\" y1=\"5\" x2=\"15\" 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<line x1=\"99\" y1=\"14\" x2=\"99\" y2=\"40\" />\n  <line x1=\"107\" y1=\"14\" x2=\"107\" y2=\"40\" />\n  <line x1=\"114\" y1=\"14\" x2=\"114\" y2=\"40\" />\n  <line x1=\"122\" y1=\"14\" x2=\"122\" y2=\"40\" />\n  <line x1=\"129\" y1=\"14\" x2=\"129\" y2=\"40\" />\n  <line x1=\"137\" y1=\"14\" x2=\"137\" y2=\"40\" />\n  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"24.948597949754408,14.948597949754406 144.9485979497544,14.948597949754406 144.9485979497544,40.36121446433689 24.948597949754408,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3145728</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">3</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">90</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-88a825b6-9e14-41ec-aed8-b20827659228' class='xr-section-summary-in' type='checkbox'  ><label for='section-88a825b6-9e14-41ec-aed8-b20827659228' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>level_full</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6fda3b71-d794-4a15-beb5-75e1b7c88071' class='xr-index-data-in' type='checkbox'/><label for='index-6fda3b71-d794-4a15-beb5-75e1b7c88071' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([89.0, 65.0, 55.0], dtype=&#x27;float64&#x27;, name=&#x27;level_full&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-13ea4894-ae15-4939-ab9a-e648a2b259d1' class='xr-index-data-in' type='checkbox'/><label for='index-13ea4894-ae15-4939-ab9a-e648a2b259d1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-01-02&#x27;, &#x27;2020-01-03&#x27;, &#x27;2020-01-04&#x27;, &#x27;2020-01-05&#x27;,\n               &#x27;2020-01-06&#x27;, &#x27;2020-01-07&#x27;, &#x27;2020-01-08&#x27;, &#x27;2020-01-09&#x27;,\n               &#x27;2020-01-10&#x27;, &#x27;2020-01-11&#x27;, &#x27;2020-01-12&#x27;, &#x27;2020-01-13&#x27;,\n               &#x27;2020-01-14&#x27;, &#x27;2020-01-15&#x27;, &#x27;2020-01-16&#x27;, &#x27;2020-01-17&#x27;,\n               &#x27;2020-01-18&#x27;, &#x27;2020-01-19&#x27;, &#x27;2020-01-20&#x27;, &#x27;2020-01-21&#x27;,\n               &#x27;2020-01-22&#x27;, &#x27;2020-01-23&#x27;, &#x27;2020-01-24&#x27;, &#x27;2020-01-25&#x27;,\n               &#x27;2020-01-26&#x27;, &#x27;2020-01-27&#x27;, &#x27;2020-01-28&#x27;, &#x27;2020-01-29&#x27;,\n               &#x27;2020-01-30&#x27;, &#x27;2020-01-31&#x27;, &#x27;2020-02-01&#x27;, &#x27;2020-02-02&#x27;,\n               &#x27;2020-02-03&#x27;, &#x27;2020-02-04&#x27;, &#x27;2020-02-05&#x27;, &#x27;2020-02-06&#x27;,\n               &#x27;2020-02-07&#x27;, &#x27;2020-02-08&#x27;, &#x27;2020-02-09&#x27;, &#x27;2020-02-10&#x27;,\n               &#x27;2020-02-11&#x27;, &#x27;2020-02-12&#x27;, &#x27;2020-02-13&#x27;, &#x27;2020-02-14&#x27;,\n               &#x27;2020-02-15&#x27;, &#x27;2020-02-16&#x27;, &#x27;2020-02-17&#x27;, &#x27;2020-02-18&#x27;,\n               &#x27;2020-02-19&#x27;, &#x27;2020-02-20&#x27;, &#x27;2020-02-21&#x27;, &#x27;2020-02-22&#x27;,\n               &#x27;2020-02-23&#x27;, &#x27;2020-02-24&#x27;, &#x27;2020-02-25&#x27;, &#x27;2020-02-26&#x27;,\n               &#x27;2020-02-27&#x27;, &#x27;2020-02-28&#x27;, &#x27;2020-02-29&#x27;, &#x27;2020-03-01&#x27;,\n               &#x27;2020-03-02&#x27;, &#x27;2020-03-03&#x27;, &#x27;2020-03-04&#x27;, &#x27;2020-03-05&#x27;,\n               &#x27;2020-03-06&#x27;, &#x27;2020-03-07&#x27;, &#x27;2020-03-08&#x27;, &#x27;2020-03-09&#x27;,\n               &#x27;2020-03-10&#x27;, &#x27;2020-03-11&#x27;, &#x27;2020-03-12&#x27;, &#x27;2020-03-13&#x27;,\n               &#x27;2020-03-14&#x27;, &#x27;2020-03-15&#x27;, &#x27;2020-03-16&#x27;, &#x27;2020-03-17&#x27;,\n               &#x27;2020-03-18&#x27;, &#x27;2020-03-19&#x27;, &#x27;2020-03-20&#x27;, &#x27;2020-03-21&#x27;,\n               &#x27;2020-03-22&#x27;, &#x27;2020-03-23&#x27;, &#x27;2020-03-24&#x27;, &#x27;2020-03-25&#x27;,\n               &#x27;2020-03-26&#x27;, &#x27;2020-03-27&#x27;, &#x27;2020-03-28&#x27;, &#x27;2020-03-29&#x27;,\n               &#x27;2020-03-30&#x27;, &#x27;2020-03-31&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c34e8a88-ab17-45a7-8ec0-9d19ec93ed9c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c34e8a88-ab17-45a7-8ec0-9d19ec93ed9c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 4
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/ecm5945/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/pyicon/pyicon_tb.py:451: PerformanceWarning: Slicing with an out-of-order index is generating 332 times more chunks\n",
-      "  arr_interp = xr.DataArray(arr.data[pix].reshape(Lon.shape), dims=['lat', 'lon'], coords=dict(lat=lat, lon=lon))\n"
-     ]
+      "id": "f4521020-47fd-47e9-8254-f83321c920c4",
+      "cell_type": "markdown",
+      "source": "Since the data are stored on a HEALPix grid, the `earthkit-plots` library currently doesn't support plotting these data so the `pyicon` library is used here.",
+      "metadata": {}
     },
     {
-     "data": {
-      "text/plain": [
-       "(<GeoAxesSubplot:title={'left':'level_full = 89.0 ','right':'2020-02-01T00:00:00'}>,\n",
-       " [<cartopy.mpl.geocollection.GeoQuadMesh at 0x1486d9e10>,\n",
-       "  <matplotlib.colorbar.Colorbar at 0x148ebf460>])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
+      "id": "962a9a11-d103-4588-ade3-4373c847540c",
+      "cell_type": "code",
+      "source": "import pyicon as pyic",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "----Start loading pyicon.\n----Start loading pyicon.\n----Pyicon was loaded successfully.\n----Pyicon was loaded successfully.\n[pyodide]: Memory usage has grown to 257.8MiB (from 149.1MiB) for this notebook\n",
+          "output_type": "stream"
+        }
+      ],
+      "execution_count": 5
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 751.967x480.314 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "da_hus = ds.hus.isel(time=30, level_full=0)\n",
-    "da_hus.pyic.plot()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7106bc6e-e25e-412c-8aff-5692134155c6",
-   "metadata": {},
-   "source": [
-    "Zonal and Meridional wind can be visualized in the same way.\n",
-    "\n",
-    "\n",
-    "The precipitation flux on **2020-03-31 at 00:00:00 UTC** is plotted as demonstrated below.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "b0371f7d-f7bf-4dd7-be17-830de8e24db9",
-   "metadata": {},
-   "outputs": [
+      "id": "99449502-9fb2-4097-89b0-d63634a0e250",
+      "cell_type": "markdown",
+      "source": "The data can be visualized as demonstrated below. For example, the specific humidity on **2020-02-01 at 00:00:00 UTC** at model level 89 is plotted.\n",
+      "metadata": {}
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/ecm5945/Documents/ECMWF/Code/data_compression/venv/lib/python3.10/site-packages/pyicon/pyicon_tb.py:451: PerformanceWarning: Slicing with an out-of-order index is generating 332 times more chunks\n",
-      "  arr_interp = xr.DataArray(arr.data[pix].reshape(Lon.shape), dims=['lat', 'lon'], coords=dict(lat=lat, lon=lon))\n"
-     ]
+      "id": "d792634e-3a22-4e7d-92f8-f81f66eabbde",
+      "cell_type": "code",
+      "source": "da_hus = ds.hus.isel(time=30, level_full=0)\nda_hus.pyic.plot()\n",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "execution_count": 6,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "(<GeoAxesSubplot:title={'left':'level_full = 89.0 ','right':'2020-02-01T00:00:00'}>,\n [<cartopy.mpl.geocollection.GeoQuadMesh at 0xeb89fc0>,\n  <matplotlib.colorbar.Colorbar at 0xea9eda0>])"
+          },
+          "metadata": {}
+        },
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 445.5MiB (from 257.8MiB) for this notebook\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stderr",
+          "text": "/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_land.zip\n  warnings.warn(f'Downloading: {url}', DownloadWarning)\n/lib/python3.12/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip\n  warnings.warn(f'Downloading: {url}', DownloadWarning)\n",
+          "output_type": "stream"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 751.967x480.314 with 2 Axes>",
+            "image/png": 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LWf/1118Lk8kk+vXrJ+69917xwgsviHvvvVccffTR4mc/+1mnfekKicjxhg0bhCzLYsKECeLpp58Wf/zjH0VeXp4YM2bMQelzPH/+fKEoisjLyxN//vOfxTPPPCNOOeUUAYhf/epXEWVvvPFGcc0114hnn31WPPHEE2Ly5MlCkiTx6quvxtRLmPdzEMuXLxdGozEi05HJZOp2hr0kkkgiiUMBN910kwDE6aefHpOZLTw7W3l5ucjOzhZlZWXiySefFA8++KDIzMwUY8aMifAjfv/99wUghgwZEre+mpqaUFm/3y+mTJkiUlJSxH333SeeeeYZMWrUKJGamio2bdrUrf5//PHHQpIkceyxx4oXXnhB3HjjjUKWZXH11Vf3aj87Q0+eG6+++qqYNWuWuPPOOwUgZs6cKWbNmiVmzZoVNzeCEJ2TYyECvtBBr+Xnn39eTJ06Veh0OjF//vyIct39rYQI5EOIFwP9sbe1P/CTJMdCBEjnSSedJNLT04XJZBJlZWXiyiuvFD/88ENMHcEsOampqcLlcsVtZ+XKleLss88W2dnZwmg0iv79+4vzzz9ffPnll132pTMkIsdCCPH666+LQYMGCYPBIMaPHy/mzp170CYBESKQLeeUU04RBQUFQq/Xi6FDh4oHHnggIvtRsH/jxo0TVqtVpKamiuOOO0589dVXceuMR46FEGLBggVi6tSpwmQyidzcXHH99ddHRASSSCKJJA51BAlLok841q1bJ0488URhsVhERkaGuOSSSyLIrhBC3HvvvZ3W9/XXX0eUb2pqEldddZXIzs4WFotFzJgxQyxbtqxH+/DBBx+I8ePHC6PRKEpKSsQf/vCHmFHfnuxnZ+juc6Oz9qKPQRBdkWOXyyV+97vfiYKCAmE0GsURRxwhPv/887hlu/NbhffzUGtrf0ASoguxaBJJJJFEEkkkkUQSSfxE8OMz300iiSSSSCKJJJJIIol9hJ+cW8XBAofDgcPh6LRMbm5uhEdjEkkkkUQSSSSRRBL7Fods5Njj8fCnP/1pv6cc7C7++te/hpJsJPpUVFQc6G4m0QMc7OdcEocmkuddEvsbyXMuiQOB/Xne9Vhz3Nrayj333MMHH3xAXV0dEyZM4IknnuCII44AAn639957Ly+++CItLS0cddRRPPfccyG/WoA9e/Zw/vnns2XLFv70pz+FUgdDIMXw7t27Wbx4MVOmTAktv/nmm1m1alXcFIXxYLfbSU9Px2azRfjrHSzYsWNHXJ/icEybNi2UOjGJgx8H+zmXxKGJ5HmXxP5G8pxL4kBgf553PY4c/+pXv+KLL77gtddeY+3atZx44okcf/zxoSwojzzyCE8++ST/+Mc/WLJkCVarlZNOOimUAhDg97//Paeeeirz5s3jiSeeYNu2bRFtmEwm7rjjjr3ctQOLZ555ptP1gwYN4vjjj+/0szfEuKv29yUOZNsHQ/sHEsnf/afX9oHGgd73n/LvfqDbP5BI/u4/vbb3K3pibeF0OoWiKOKTTz6JWD5x4kRx9913C03TREFBQYThc0tLizAajeLNN98MLRsxYoRoa2sTQghx2223ibfffju0rn///uLGG28UBoMhwsz7pptu6tQGJRo2m00Awmaz9WQX+wwjRow4IO0eDO3/VPf9QJ9zQiR/959i2wf6vEv+7gcOyXvdT6/tA93+T+Ve16MJeX6/H1VVYyKaZrOZhQsXsnPnTmpqaiLSKKenpzN58mQWL17MhRdeCMDAgQP56KOP+NnPfsbXX38dk35y4MCBXHfdddx5552cfPLJEaknE8Hj8UToUOx2e8Tf/Q1VVQ9Y2we6/Z/qvh/ocw6Sv/tPcd8P9HmX/N1/evt+oM85SP7uP8V9T3TeGY1GjEZj3zbWUzZ95JFHihkzZojKykrh9/vFa6+9JmRZFkOHDhWLFi0SgKiqqorY5rzzzotIu7x8+XKRm5srFEURv/nNbyLK9u/fXzz++OOirq5OpKamhrKidRU5jmdOLklKp8bgyU/yk/wkP8lP8pP8JD/Jz4/jYzAYYpZ1J313T9FjK7fXXnuNX/7ylxQXF6MoChMnTuSiiy5i+fLl3a5j4sSJVFZW0traSlZWVtwyubm5/O53v+OPf/wjF1xwQZd13nnnndx6662h77PfX8E1v5jJuKkPo+jM3e5bEh2QFOlAd2H/oxujFPu2/QN7zKVeti80sVfb9xV6e84KVfyot9+bNg8UgufMAcMBb187oM0f6N//QKO351/wHnfAz99eYm/731fb9waq38Xq7+6goqIiYkJen0eN6YXPcVlZGfPnz6etrQ273U5hYSEXXHABgwYNoqCgAIDa2loKCwtD29TW1jJ+/PiIevR6fUJiHMStt97Ks88+y7PPPttlv6LD6maLFQBZNiHLfe/4cKBJwMGEg5pE72Oy29t9jz5/4t1o+voc68vf6YCc/8r+f3EJ389ED4N9+Vt2t83ebHdAHu5q7wlhb/u7NyTwQLQZgz4k0RH92k9ZDg5aErm3t5MfuxHuj3D/hRw4l9LS0va5W0WvLw+r1YrVaqW5uZm5c+fyyCOPMHDgQAoKCvjyyy9DZNhut7NkyRJ+/etf97iNlJQU7rnnHv70pz9xxhln9Lar+wQ9ueAPZSJ90BDjPibBfUV6+2K7/d2XhOglOe1NP/bFebU/XzaEKg6ea6MX6Kz/vSV+QhOg7/k5FIqqx6uvG5D0UQs6IejRdYYfg57sd3eia92uL/re1kuy3NeR4oOJ9B6qUfAf8z3kx44ek+O5c+cihGDYsGFs27aN2267jeHDh/OLX/wCSZK4+eabuf/++xkyZAgDBw7knnvuoaioiLPOOqtXHbzmmmt4/PHH+c9//sPkyZN7VceBRnduIj9WAn1AScBeEuK97XdPfrP92VZc7GXUdW/a78m+7+1+7rNzsYfnmrSPoiqJIr99Tvx1+/7B3BNCE/cemoAkxqs3Yvt2gh63/UT3alWLe4y7urcHj2GP9rWzsvuBGO8L0nuoktd9jQN53H7qxLzH5Nhms3HnnXeyZ88esrKyOOecc3jggQfQ6wOv57fffjttbW1cc801tLS0MG3aND7//PNee/bq9XpmzZrFxRdf3Kvtfyz4MRLo/XLx9FFEuDd93VcRT0mW9p7Y9ILs9tX5s7+OZW/b2ic4wPrQIESCbiRaHoEeXkvdjsz28jrp6rftul6ly3Y0X+SBkRQpZln0bxufXMtxy0ih9XGOVViEOvqaj4eQbr+X0eqE9Xajjt4Q4iThPbTR2e970NyX9yF6nCHvx4LX317GZRdMYsK0Jw6JCXk/GWLcB2S4uwS1L+vrTb0x2Ivo7v6K7PZVm91uQ5EibtLh36PX7S2iJxWGf49+melqAmK8bePVG/49Gt3Ztqfo9XUbvC6DRLIPZUx9dS12WU9n11eU1CL8NxGqiPyNOiHT0cQ75rcNayd6XUw7CdqI14dubdNZ2W6Q4yQZTqIz7A/CrPpdrFx4037JkLefJPlJ7C325RBqd9DnJ34vHq77ImLZ3Tq7fcz7QL6wr5wf+vo33Bf9jCEMnXzvSbQrWv6T6EEvNNGrSWvh5LWr/7vqQ7z10WXjbRs+hJ9QL9yLCKEkS6Cqkfugqt3bthvnXNyo976YSKupic/VqOtWCgtKR2iWVY3wiLXm05Dan6JCEyj6wDrh6zg+4cc8fFclIolyMJoe8btHRZND66JfVqLQl9HinwQp7uuRoQPtenQAEB3E+LHjkCXHeTnWA92FfYb9aZvVZyd5D24WfR2p3fvh2zD0gWNCl4T9AESB9wZB0rSvJuj0JFLc6QSozvoXjOgpcgSx6W5dohv/E/Xi06eyBUXu9otDTyO1PXkh6ekLQLz2gFiy0n7/2NsgQcJ7p6p179oOKyPJEopeiTw+wXNHL4eiyFLYvshhy0X4+UA3IsHtBDrhi10yUhyJAymFitf2T4gw7yuiLO3Hx9shS47rGtoOdBd+9Njrk3ofRIf7Ug6xV8Ow3Wl/P0X4D4a39L4ixp09kGOip71tszM7sQTr+iLK1lkkuLt1Jvqt45KrHpy/PRlB6Y1V3b4+R+PV3+Prr7sEubN29B1RZTnsP83jjykv1LCRhXBfLE0NrQ8ieMy70oF297zq7OXsR0mKD5I5AZ0iwQvfoYw+H63cj6fmIUuOD3XsS+LVqxN6H0SG++KB292o294i+LA5EJIXza8FxqYlGVnXU1eFA0OsE+psFYngNAgpTpjA3dpAS+0a2lp2o6m+0EcIP0LzITQNkJBkBSQFCRlJVpAkGUlSkOTAX6TI5bKkw5iSg9GUgyk1D4MlG1l0RCujf99Ey2S9HHkeRJ9bqoYUHHoPKxckNrI+tk1ZHzsZLFheCiNjaBqSrr1uv4qkUzr+RkWso/seT5IR3k4w4hnTl3hykTj197R8r+UfidCNa7wvroXwOiSjLrQfil4JRZWDkePwHoWiyQnkN6HjE/xt4oxudJfUxju2SUK8lwiXuchy9/q2D/T7Bxuiz6vQfesgCOh0hSQ5/hFgfxKYLk/aPibB+zUSHI0+TCoRiux0NSNdiAjSJ4RGU+UPOO2VeFyN+D2tqP42NJ+znfSpCKEFPpqKrOgwmHMQqgfV78LvdeLzOtE0FZBQFB2yzoCi6JEVHbJiQJb1SIoOSdaHPrKiR9Gb0ektKAYLelMK5tQCzGmFmKy5SJ39zntroRf2O/k8rThtFTiaymlr2o2zuZy2pnL8Pnc7cZUDhFYO/O/zOJEkmeKSUqwmIwaTHr1Bj0GvR683odPpUFUVTdNQVRVVVfH7PYH/NRVV7Viueju+Oz1uKrc0hxFzGZM1E2NqHgZLLub0fIzmXEzpOVjSSjCnZwfIil6JjTa3n1fhhDJIMBMN6wfPbUWnoPVBFF6WJTRNIMsSql9F0Smo/khCJYWkClqo7URlo7fZW4he2LD1BYFLGHmPemkILovuQ2f9TFRvCO0vMsEjqLUfg/CXD0mWQFbaiXCABIcIhRzHaSMOeqrF/9Ei3rnYE8K8L0hpsM5DmPD2FsFr78dAjCFJjn8U2B8a426dsF1c8H1NXvuaXMdgL7J1JYKm+RGqD1X14PPY8Xla8bqbaW3cSmvjRpytteh0BvTGFKyZQ/F5mrHVbyIlJY3cvFyyizJJSSnAarViMBhQFAVFUZBlGVmW8Xq91NXVYTKZSElJwWKxYLVaMRqN+P1+vF5v6K/P54v5G/x4PB7anM202Z04nG202m2hhzXA4Wf+DUt6MRAg9JrqQ/W5UP0uVJ+7XUsrABEa6jIY0zCl5ASOg+qjrbkce/022loq0BvTsKQWYckowmjOR5aNNJQvZMvSfwCg6HSUFJcyfOQABgw4CqvViqZpIZIb/D83N5fx48eTmpra7d/E6XSyefNm9Hp9iBh7PB5cLhdutxtZltHr9Xg8Hurq6qiqqqKmpobq6mpaqjcCG2PqPPH2TwHQhUXqJUUKfde1R3B17ZFWpX25pgl0Ohm5/dzWEpATv19Dp5NjiLKW4CVM00SoLk3TkGU59Htqmg5ZlgB9zDbhJDpQd/CR0PWjId72nf2N3ofIdmMhVBFRPvYYdJDL7koKOnW7SPDwDpc/BNrtWeQrpJmPjtbrglH/wIuIpLWfE0EC3O6tHP7iHdrPsEhlPPlFT/rVIwQjowcrEkVtD+Y+/8jxYyG7PUWSHP+IsK+G7Ts9uRPcVPYVce3z6HA30NkwcHfKVu+YR9WWj9BULz6vM+52RcUlHDN9Iv3798fr9dLU1MTqNWtxCzd3PPggY8aM6fP9iobT6WTjxo1UVlZit9ux2WzYbDZqa+toc7RGkOOti19E9bbidbXg87oQ3TLSBWt6EYrOhKOlHE31I8sKRcUl2OvtVLQ0h8rpDWZ8Xlfou+r3k5WVwXXXXYfFYum7nQZeeeUV5syZs9f16AwWisccQ8nYY1Foxeey0+ay42mz4XXZ8DoD/3vabGiqB4M5FaMlBYM5FZM1DXNGDgPGzwjIX4LXVYI7sMHYvT7FEuEoItkJwRRa/HU9gaYKZEWK+BuOztYF+w+EjocWRWzC+6ZF9VeocmQZXew2e4Mg+e6e40YP9OSKHCC+QYmEFhxpCKtPFQEdcg+9kvcaPZEDRONAEdCkhGG/4FAlwYmQJMc/MuwLS7eEGqAeEuN96b26NzPTe1K2s23abOVUbPoAS1oJQlMRqBiMGexe/1aozKhRozjxxBNJSUkhPT099DGbD6zXdk1NDVdffXXM8rTc4UiSkZSckbQ2bkP1Bcj9qMFZ5OQMJTMzE6vVitlsDn1MpoB8ATp0wZIkUVVVxfLly/H7/QwZckIoS6bBYACgra2NyspK9uzZg81mo6Kigvnz5+P1egFYuXIln3/+OWeffXaf7vuVV16J0WjE4XBE7IPFYsFsNuPz+WhpaaGpqYmGhgZq6+pxOl0UFxWSk5PNvHnzAPB7nTTsWEH5is9jXhYURUdqWhppaWmkp6dhNBpwtDZjr3JQuac8VM6amcc5972NLBvi9jWaHIZDjnM9Bpd1/I2qT45fXwTpTECgEy0Lv0aCEeHov5F9TLwuXHmraQJZCbNJU0WCkfPIF4HovwrxCWpPSbNGZOPhZDlYfzxSHKEHjqM5h1g/5Ijtw/yOEzqFRHstdyXninOPj4m291TDm+D54Pe14W6rwudtweu14ffZMVmKSM0YhsGY2bM29qIfSfQtOrOPPBSRJMc/YnSWPKAn6JF1Wi9mhe/L1Mk9jaRE24J11jehCoQQtNStomrbx7Q27wCguWYFeXkFKDodlbtqkWQdZmsBmuph/fr17Nq1m6FDhzBixAgGDRqEoij4/f4OrWv7x2g0YjabQ38HDx7co33pCrt372bRokXs3r2brVu3x6zX6U3Y6zcBkJaWQVFBLq2tNm644QYmTZrU4/YGDBjA1KlTE663Wq0MHTqUoUOHhpbdeOONof/3lbG7xWLhqquu6tW2K1asCJHjCRMmMmBAfwoLC8nOzm4nwumkpaVhsViQJIm6ujqWLl3Krl27sLc6aGysj+xLeg6SlPicjSbA4WQ5/P94RLm7iCaJQYmHnOBaiB/x7ZBE+DxOnLYmPI5m3K3NeJx2LBl5pOf3R1P9VG9agt/jwpSegzU9G2NKNpaMXPSmSLtNObr+qP6EJBqKEkGcw8lyiCiHbRe85uOR8zWfPE79juV4nTaE5sdozSQltz9CCPTGFGzVm3C3NgIgKTomnvtnMotGxyfFmgiR39DIUth3oQpEUMvdPvwfmogXLY1QtY5t2v+GHZyItrsbtY6fLrvn0rJQPe1e16rqxmHfRmvrZtrsm3G2VYa0+3q9npSUNGr2BI6hxZpPauZYcouOxmTJ77LP3UK41COeI0QiKUii7Q4k2T7YZSvtOJRJcRCHXIa8Z555hmeeeQab3UVV5a5DJkNeT9CncoaoC7W3lkn7+2Lq6obbWUKIUNRGaGxb+RRNNasYOXIURx89HZ1Ox/Tp00ND/zabjS+++IJF3y2mrrYOu72l131+5pln6NevX6+3D2LLli28/fY7LFnyPQZjCpa0fvj9XhzN20Jlhg0bzmGHTWTIkCGUlZWRmbkPIjqHMB5++GEWLlwYd52i05NR0J+M4jIyi8rIKi4js7gMa2YekpQ4297eoLOIc1fa3oiyqkBVNTxtNhyN1TiaasgqGYY1swBNE7RUbaexfD2a6sdWs4Odyz7rsk5F0WEwGnE5I+01rRm5mNJyMGfkc+TFfwyR13iT4xLtV7DPHcu0iP30+yOJZDQ+e/CULvsfjpEn3UDh8OMjlkVHjBP1WfNpIRIWER0OJ7jtpDi4PBRh1uLvRwxx7gx97O4gVIGzbQ+b1j4YWtavXz/OPvtsBg4cSG5uLikpKUiShM1mY+3ataxevZoFCxfR5mglPWskOQUzSc8ajSR1kxB2tg9dJEbpFn4ExBQ4eMh8GPZnhryhQ4eiKArXX389119//T5p65Ajx0Ecaumj9xbdJczdkVfEDM/tw0h1d9DVw8HnsSPJehSdKa49WMxs+PYbj+rzsXnlQzhb93D00dM5/PDDSU1NRQiBzWbjqKOOwmiMFId6vV7q6+upq6ujpqaGN998i+bmZghLBzFx4kQURaG8Yg9Gg4FBgwYyadIkpk+fnngfhWDFihWoqoperw9NqrPb7axcuZK6+gZsNjuOVjterwdLSgEFA08lK28SklDYufHfNFTHkrlXX301SYx7gVmzZrF06dKIZSWjj6TsiBPpN3Y6elPf6qbjoStCHPG9GwSqfM0i1s55AUdTDT5Phx5c0RtIzx+E5vfQUrMTSZLR6fUITcXv98fUM3HiRKxWKw0NDZjNZqZMmUJqaip2u53q6mqqq6upqKigqqoqtM0Fj3wdcR+JF+UN36d4ExKj9zM6opzo5UDzq7TW72DpG3ej+j3IkozBmkH2wIlk9RuDwZKJMSUTgzUbWTHESCpC5DYsShxcHtHXKGIcb7vwbcLvR/FkHOFtRB0MkOX4AYAuiGN3RtPC5Riq6mHP7nfxuOpQ1VZUfzM33ngDhx12GCkpKXHr8Hg8LFy4kI8//oTt27eRlXc4A0dc3T6/wYZOn4LBmJmYMO+NI8WPebLej8gneV8R5f2ZPvqQJcdvvLOMS89PkuN46JUMohOC3JeT5KLlDvFu/n6fi9amzUiyDqM5B6MlB1mOVQgJTeByVLHm27tDy4ymVAymbPSmXIzmXIyWPCwpxVhTByBJEn6fg+b61QHvW8WAwZhJS91abE0raLPviaj/hhtu4MQTT0y4Lw6Hg3fffZf33nsvYvmll17KBRdc0KPjsnDhQh5++OGY5ZIkkZoxBKO5AEVnRadLQae3YjDm4PU04XU34HE34vPU4/M24nI2hrYtLS3lqaeeQgnTeSbRPWiaxuuvv84777xD0fDDmXz+70jNLQ6tj6+vDa7r+qHWGfHtettYPXGi9eHYs/57FvzzTjIy0rniiisYMGAAmZmZzJ07l/r6emRZZty4cUyZMiXk/LF582YWL17M6tVr2LNnDz5fQD8uKzokSUZVfYQ798uKDr3BhM5gxJyRT/8Jx9F/wnGYUgMvaJ0dt+h9iCbL0QQ5UpISO7EvXnvxygm1g1yLziK/0RIKTcSNFHe2TcT/CdqKczA6O1Q9Rm9lDqrqYde2l7A1r0eSJIqLSykrG0hWVhbZ2dlkZ2eTlZWFJEm0tbXhcrl4+Z//QhUl+H1NOOy7gcA9TVEMWFIHYk0pw5o2iJS0wSg6U5/popPYf+hLopwkx32AL77awInHjWLi0U8iy6Y+r3+/zBzeD0hEbLsiyHsbPe4tdm94i+odn8csHzj6CvL6HROzvLV5KxsWPxizPAhJCiSdyMwdS/8RV9BSt5Jdm16PKKMoBtKyRlM86GycrTvZseFlAC6++GKysrKwWq1YrVYkScLtduN2u1m1ahXffDMfVe2IrJlMZgYOHMidd/6+x9FaTdN47bXXePfddwEoKTub3KIZyIqB+soF2BpX4/M24HY2omkdbVqtqRQUFFBYmE9BQUHok5+fT35+ftxIehJdY+nSpcyaNYvxP7uGUcddErJq6y6564wg9xUxhuhIatf3q+3L5rJs9sMYjUZOOOF4Tj/9dAoKCrrc7oMPPuCt2W/jbHMEFkgS5pRMisccg89lp3j0UZSMno6sxL7EJprEF253F64/Dp9AGG5tl+ilIBFJjm4vkTOGUEVIniHiWKeFE90YwhwV9Y3WJ8eVVhCQaHQ26S+so3H3Z2/RE3u8iO2EwOOup82xA0frdnyeOvw+Gx5PC6rqjSkf9DLPL8jnissvIy0tjfLyctavX8+yZT/gdgdGMNIyhjFkzK17FzVOotuIZ2fYV3V1a5sE91FNdbF8/o37hRwfshPy6hoDs+6FJkAOS9nZQ0LbGenry4liBwqJPJTjDq91MlmgN8c1XNsbSPIgoaoevK5mhFAxW4tC5b3uZrb88CRt9l1x69Mbs2OHDmWZlLTBZOSOpaV+DQBGczqa34ev3ZXBYDDi8bhprl9Dc/1vGT15Fnq9BSQ/QwYPZuPGjaiql+b6FciyAZ0hjYJ+J+Nx1fLOux+225HF7rvRlBlBjAHcbhcDBvSPkWJ0hpqaGl5++Z9s2bKVpqYGAFLTB5KSMQRFMaP6XZRv/U+ofHFxMUcccUTIKSInJweTKb6cJInew+12AzBk6s8RIpbABdHdiXPdJcSdEVxZlkJEM165RLrjcB/igYedSN7A8Wxb/CFz533Mxx9/zMBBZeRkZzF69GjOPPPMiH3auXMnS5cu5Y033mDAYSdROHwKzVVb2fjVG7ham2jZuhC9Tsd3K+eFtjnznvcwp2dHuFiEJgV24nYRvl8RWuIu7j2Rv0FnbiAdxyf4fzBqHJ20pKvUy+HEuCv5RYLOIBFGrPdDxDgc8Tyfu+UjLUmYzHmYzHlk507p2FYIVNWFz9sCSCg6M9UVH9NYvxghNJqbmnjkkUfRtPjJZ7JypxyUOtu+wv6WGva0D92yM0zQZk8y4nXFqbojD+srHLLkOAhJlnpvMbYX0dDOsisdjEiYtjTBSR3u+tAbuBy1NNespKV+Ba1NW4k3gDF+xt8wWrMAaKlfm5AYA6SmDYqdtNI+m3rImBtiyns9LThbd+N07MHvb6PNthOTpQCTOZfhE/9AQ81CKqq2oTdm43UHSGlj7feh7QeOuJLBY/4PITRU1Y3qc7YfQz2a5qHNvoudG18CoF+//pSXB4YM58yZQ1VVFffff3/CfWlubub1119nxYqVNDTUI8s6cguPZeCwUqypAzGac0PHXVaM9B96CQ7bDtzOCior91BZWZmwboDf/va3HHPMMZ2WSaJzZGUFzsuvnruFiWfdQH7ZmIgbdwdJjfTxDV/WFXpsPRaPEMfzHo4i0OHkU5YlLBm5jD7pVwybcSnlq79ixQd/Zcf2QLR88uTJFBUFXlrXr1/PnXfeiazT03/iiRx29u+QZJmWmp2h+pqbmoh+L9PCIoidyRo6ykdGhcNfAiBAd0UYwe+I+sYee117lrpEnstBUqxpAtWnRUano6K7EcvCo8DtjhTRThPRLhShZWGIyIInyyDU0DMsWm6xv9HVs6DTbSUJnc6CTtehxTdZisjOPRJFZ0HRmfF6GnE7q/H7WvB4WiK237X13+za+m/GHP4AekMGUqJDcBCS5r4mvt0ZtT4QbhJ702a3JvXvBZfrDQ5ZWcUb7/zApecfwcSjn+xUc9zZSdYXP8SPgRhHI95+x5z4vbgJaZqPpuql1FV8icO2C51Oz4QJE5g4cQJ+v5/du3fj9XpZv349TU0tjD7qASQJDKYsZFmHz2PHYdtBm20nimLCbC1EoNFSvw6vuwGDMZPc4hlYU/v3oE9+nI4KHC070FQPmubD7aymeODPaahZQE353JhtdHozlpT+EPL41ZGRPZasvMlU7fqI2j0dUbKBAwfyt7/9Db1ej81mY926dfTr14+MjAxcLheqqpKZmYnJ1CH9WbJkSQx5Hn34/RjNuYl2IvTv1vVPYm+JzegWjvvvv59x48Z1eWySiMSaNWt47LHHMJpM1NXV4ff5QuvOmfUR5tQMIL4lWnfdIhIhgnj38iHUWdQlUZIQv09j98ov+OHdh4AAyRk4qAyLxcKeigpkay4z/+/pCLmE0DRaG/bQ1lxNW1MNmt9LemEZGQWDMKd1T04khUeVExDoeBP0oqUVXb2IhCcf0bQw+USUzjiRZVo44Q26UcSzZ4suG1yvenyovjb8vjacjj047Dtwte7C77Hj97Xh87ah6K0MHHEVaRkjuj2xridI5F/bk4hfd9oIl2n4fQ78qhMJGVV1s2vbC7hdDQwdOoxhw4bSv39/+vfvz6effso333wTUVdG1mjMln54PI3tI2LBPgYIuCV1ANa0QRiMWQdsxKynx21/kj6Iz0t6Orre29H4RPV0B6rfxYpv94+s4idFjnv7I/b2xN2fxLgzg+7odXszxNFdyzcg4gZua9zA5hWPAQHf2cMPP5xBgwZRW1vLunUb2LOnvMMb05CCJOvxupuDrWIyp6M3ZqE3ZJOSMYSs3IkArFp0GyAYN24clZVVtDq89Bt6GarfjdA0dHor6TmRVkEO2w62rX0SCVBVL6rqQ1F0mM0WXG4nqt+PLOvIyp9KQ/W3CY/PkCFDyMvLo7XVwZo1q5EVIzn506ir+jKmrMlkRq/X43a78IWRqiAslpT2iStZ1NXVU13dEf1NTR9K2cj/i3zJS/Bw9LgbqK9ZgK1xGW53MxkZWRx22AQKCgpiUkmHp2VWVRVZljEYDBgMBsxmM/3792fYsGEUFRUlZRnAk08+yRdffBH63n/C8RSNPBKDJY2i4UdEHKPuao8PFkTrbcPJZlAy4rI3UrtlGbXbVyJUFcVgYsi080jL7bAfjOdNnKitcCmKHBUV6uz4RVvTJXKuiFgWHT1vj6gHybPfr0XIKOKR4gitsU8NRXI1jz/GUq0jiYeKq7UaR8su2pp34bTtwu+xofpc+HxOVH+sDtdisYTSvIeWWUsYMf7umLI9xb7WjrbattLWugNN86JpPjTVi0DDmjIQSZJptW3F1bYdZ1tNxHapqWncf/8sBg0aFNlfIWhpaaGtrQ2Hw8GGDRv417/+hdWaSklJSaAMAkQgjX1zczMNDXUAGI1ppKSNIK/4eCzWkv0WVe5UnhDnvBZCw960EZezGp+nBdXvQpKUgB5bVpBlA0ZzLiZLPiZLPjp9fBeQnvYnkYVpt+qMHmXuAaHem/wMSXLcB+iLyHE4ejRxLVh3gqGz/YXukuLwcj25sHvyduywbWPD0ocit5cULCl5WNIGk5IxGKM5j5rdc2mpX8XkyVOYOfMYrFYrdXV11NXVUV9fT1V1DVs2b0bTNNIyh+B0VOP3tZKXX8Cvr7uWF198maqqSFeJwWN+TWY7mQZorlvNtnVPh76HR3cBXC4XV1xxJek5MygccBoNVd/iaN2Jx1WLolgxmLKwN63GaJB59dVX0Ol0XHvtdVRXB6ypjKYcDIYskGQkKegEIfD7nWiqC01zI4QfTVNDQ8ySrCAhIysmzJZisvImk5JWhsGUE0lMuxkxEkLDYduKrXkdDvsm/P5WZFmHLOuRJH2gX+034ED6BRnQEMKH0HyoqguXMyAnsVpTGDZsGJMnT+LUU0/tVvuHKqqrq1m+fDnz5n1JZW0Dp97xDnpjYnVad1+sE5HBzpwVelJntzyO40Rig8t7ovVLFDmH7j0cw+URwchx9OS78PLx6uvM2i1iv9rLxUgowohxtFZY87VLI8IIcrAP3rYm7I1bsDdsxtmygzZbecCxA8jPL2Tw4EFYLBYaGxtpamqiubkZm80W53gpmMw56A25WFMHklMwHb0+da+zlO1twg0hNFzOSlrtW2izb0NV7WiaJyAtU934vE7MZismkynwom00omkqVZV7EEJQUlLKmDGjGTlyJNnZ2WiahhCCfv36haRKXaGuro6cnJyEev6Wlha2bNnCpk2bmDfvK5pbmhhz2P0YjO319yFJ7gst7Z7t71O9ew56vYGMjExSUlICiaI0FVXVcLtctLQ0hcobDFaM1nyMpgJKBv8ct7MOZ+tudIZUdPpU9MY0DMYMdPqUxHZ4CTyh9/r86AXXOVgjx4e85rgrdHdooLOJa5AgOUacZfuTKPdmSKwzohyTvKAHF5I1pYzDZ76A32vH53OgN6Sh01uRdR02YptX/B1b4zoMBgOq6mfOnM/bIyh+amtrsNsjHyJlA1JYuzYwm7mutoann36ak046ie3bt2O1WmlqamLVqlX4PI6I/ltS+5GWNRwE2Js3sXPnTl599VXS09NxOp1UVlbicjkpzRjBmsW3ofpd6HR6Ro4cybZt22ls2ciAoVeya8srfPjhh0yaNIknnvg7Xq+XTZs2sXDhQpqamvF6fbjdroBtkdtFVkYGxcUDyM/PD6UwDsop3G43TqeTH35Yzs6dG7Ck9Cc7v31CSzcIcexvIZGSOpSU1KFxy3cHfr8Tp2MXbY5drFr1BStWLMfj8UToTn9qyM/P5+NPPqGqXdftbKnFmlWYWOoQpoXtDGrUtRW3qkSTwOLMF+hqu2h9bsLEFUFdctT+JZqAmKgd6NgvRSd3Svp1+vgP9BBJbu+v6o9/XQQjwvGkIhAgwuHLwqPE0LUDRQwpVjWaq9ex5YcnUf2BiZp5eQVMHDucwYNnMmjQIPR6PWvXrmXx4iVs27alfZRMwmzJJj1zFEZjPkZTHkZzHkZjblyf375I39tTBwpN81Ff8w0tTcsQwofP24rP50Sv1zNs2HAKCsoiUsvn5uYyffr0GOJqt9sDtpOpqT3qbzzk5eXFXS6EoLa2lq1bt7JlyxbWrFlHS0sTFksBekNG+E4F/sYhyZ1ZiMZtcy+lJ15PC9W755Camsq5557LUUcdRWZmJjU1NVRXV+PxeJAkCa/XS01NDTU1NVRVVbFt2zba7BXkFB3FpuWPdt6IJDPhmMfQGyLJpKRIoCgRkhdJps8Jc1fo6v51oJCMHPeh1KKri2RvI8k9PYHitbO/LFn8vjYaqheBJKGpXjQ1EF3QNC+KzozekI7JnIusmANDSLKC21mLx1mL39+G32tHknVIsg6h+WmuXx7ThsFgxOv1hL4rigFFZ0RRjMiKEVkxkZ1/FDmF0xIeA7ezhj0738HrqcPvdaDTW1AUM+nZh5OZewTrlt3Vsf+ShCzLqKrK0NG30lCzkKaGjkQQVmsKubl5ZGVlkJaWhtkckFIEoyM+nw+Xy0Wb00mbw4nT6UQIQVZ2JlaLhaqqGmpqqvF43OTmT6Pf4EsS9nt/w+OuZ8/ud2i1b0ZTfVx55ZWcffbZPym5hcPhYPny5bz00ku0tLQAcMIt/yYlK/CiEE4IOoue9hTdue67JLdxoshdJdqIFy2OR5ITrY9HjMOXh9JBJ+hHNIHX6WVkWcIfFuWNln8E/o8kxuEa5WCEGCKjwxBfRxxBmNsz4Gk+DeFT0XwamqrR2riVbauexee1ce6553L66aeTlZVFRUUFc+fOZfHiJdTV1aDojKSmjSA9Ywxmaykmcx6ybIjZ92h0RWh7S866up943I1s3/wEXk8TRx99NJmZmVitVkaOHMmwYcMwGLru+76Gqqp88sknfL9kCbt27sbhsANgNudgtgwkNX046ZljAlHULubN9HTCYU+Oe6JrWAhBc90PNNUtxda0Hk31hexFO4NOb6Zs9PWkpg+mtnIe9qaNOGzbUP2emLKKzsy4ox9Fp++YDBlyI4kn84gjIwqhl8R5b0fog0hGjvcx+iJ6m8jdARJfNF1FknsjcO/sxhlPF7Q3/oWdTdyI/m5v2sSeHe+GluflFWA2mTCbTbS2ttJY1Yzb7YzbjjW1lIzsw8nOPxKDOQPNryE0D05HOQZjOpJswGDKx2wtwmQuwmwpQm9Ij4i0CKHidTfh97fR2rwds7UYRYm1UDOYssnMPQJXWyUmSyFGfSaq6sLvd+Jo2cqEI5/C7aymsvy/uBy78fsDfd6yLqCfliQZgyEVWTGDbKHVWUSL3YHqr0DTXAFvTyHQhIoQGiZTPorOiqJLBykLv7+Npm3NyIoDo6kQS0oOaZkpmM1FNNYuQ1GM6A2ZmC3FB5SIGk25lA37P1TVQ03l57zyyivU19dz9dVX/2SSiMyePZsPP/wwYlnNpsUMn3F+n7bTq/TvibaJox0OkchOyG+0HCFI/P2+SKut6Aih3xfQrstKJPFV/VrH/8E+hUkm4nY9KrqsqR37oNPLoX3y+7Ww/Yz1NA7XEqtxnCTi2atFp20OlgsSY6EKdqx5hbrybwDIzs7luOPO54ILLkCWZd5++23+85830emspKaPoWzY2aSmD0OW9XH3NR4iEi11Qtx6E710OatxtVXg9TTj9TQjNK3dc1gBSUYIP05HOW5XQFp15plnMnjw4B61sT+wcOFCXnrppYhlGVkTUBQTPl8TJksRXm8zbY5d+Hw2QEVWTCiKCVlvRlFM6PQpGE05cesXQqO54QckWUGnT0OnT+kItvkFOn1Kl79pokCaUAWSJJGVfwRZ+Ueg+t3YGteiqh5M1jz8Xidtrbvx+xwIoWJNG0hq+pDAiKtiCD3PCkpPoqD0JJDB627C520NyPeESkrG4BApTpSbIBFRjuuq0n6vD3Gd4OUf5f/dnWMQUW/U94MhkvyTI8d9KWvokUdwGMKHbuJeON08MeJ5UQbrjVu+i3p7E5mMEfW3f8/InkBW7iSa6pdy0UUXcfHFF0eWEwK73Y7H48Hv9+P3+/F4PMydO5e5c+fS1lpB5a4PSMscQ2r6EErLLsFgzEKIwMMqkggHbjKa6qWlcTVuVy311d/g97VGtNl/8GXk5E8Nfbe3bGLX1pfxeR1kZeVQV9kY88auqS7sLWtwt23npJNOoKysjNTU1IAmTFWpqalh9uzZuJw2rNYSikrPoLFhEU11i0IPlnAUFv+MlqYfcDq24/V29E+WdShKGrbmlXGPs8mURWrGOLJzjwxMLjlAUBQjxf3OxGjO4bPP3qSurp7bb78twm3jUIGqqjz66KNs2LCBUaNG0dLSIesZeMTPmHjmDejiRM/25Y29pzriYHKSoNWZpomYyWgR9UVlgw63pIuO5gbqi1zeUTa8jlhiHVzWmb+xFLWvwYCEDjkiEYgII8bhuuFoGYXqV+OS4hgSEEaIg8siJBWqwNGyO0SM8/LyePHFF5FlmS1btvDUU8+we/cu8otOoLDfaQnJU0/tMFW/G5+3Bb0hHVkJeJf3OGosywgh2LTmITTVi9lsJTs7G51OF7qn+f0qBr2OzGIrEsPweDxYLPs+HXpvMG3aNNLS0rDZbNTU1FBRUcHOnbvR6RRcLheb10VmFZVlhXheyoX9foY1bRCKzoAsGwJzMxQ97rYadmx8KaZ8EDqdibSssWTmTiA9e1xMptaEpDDOs1bRmcjKPwKAjcsfwGHbFbG+oWoBAKWDz6OgX0dm1vBzwGjOxmjO7jIqHpcYK3LE8ugJqKH/NQH6qAmqmoQUXBZHBtijAN5BQIzhECbHednxL+bezrLsDL2JIoev62zYpjPCujfD7HGtXBIZvvfCNqi6/BOa6pdyyimncO6558a2JUmkp6fjcDhYtGgRCxcuZO3atahqx0Wo1+txOTZhb15L5a73saYU4nI1oNOZyMg6nNT0oTTVL6GleS1p6cMQSNib12M2W1D9rpg2TeZ8IGAjtH3j0zhay6E9gcftt/+OQYMG0dzczPLly3nhhRcAaGvdjdNRwXHHHcu1114LBGZEv/LKK6xfvzEwXKoYKe5/Drl509m59QXaHFs5+ujpHHnkkeTm5tLY2MisWbMwmQvZs/ttysoGcsQRp1NSUkJpaSk5OTl88MEHvPvue0iSjBAaJSUlyLLCnj0VaJqG292Eu+Zr6mu+ZsLkpxNPtNhPyMk9Cr0ugxUrX+Kmm27ht7+9haFDe69vPtjQ2NjIypUrWbRoERCIUGX3H82Qo35Oam4pZVNORx9FjOPJBKIlDNFOC+Hruovo1MnRk9YSvbRHbKNIocs6njVavP5FT4wL/p9ofzqWxbt/dCzT6SLP5Wh5SkT9cbTGgeixGhMpDv4fjBZrvihLNlULTa4LIkgCgoQ4eA8O6ox9TjdbV/wdW+NGMjKzmHbUVC655BKcTievvvoqn3/+ORZrMcPG3N6lpWR3ia0QGuU7XqexdilCBPqnKAaMxnR0hgwKS88kJa0Mn9fGjk3PAhIDh12HwZgRW5mmIQE6xcC5F5zLRRdd1K0+HKxQFIUJEybEXVdfX8/s2bOZPn06xcXFZGRkoChKSN7mcrlwOp188MGHfPPNJ5228/zzz6OqKna7HafTGRrF27ZtGwsXLmL7+ufJyjucgSOuDry0hF17PY2Gttl3obWPZMhK+4uVCOi/AVoa16EzpKHTWdHpLeiNqSj6wP/xngudEeMgKQ4uDyfLcrj239TuD+7TQpNqtTDtfuglUxYEJndHyjOitcydkeVEBFlSJKT4OWL2CX7ymmPou2hybxwtIvqRwF1iX2hNu7PPMe12kyRXV3xGVfnHXH755Zx33nlxy/j9fj7//HNef+M/tDlaAQmTycTEiRM44ogjGDVqFAUFBSxevJi//OUvDBs2nCFDBlNYWEhdXR1ffz0fu72FwsJiZs6cwdKly9i2bSsA48dP4I9/vIeNGzfyzjvvsGrVKgpLTqOo/8+AgM54/cr7IvrzyiuvkJ2dDYDH42HOnDmsX7+eJUuWYLEOwtm2g5NPPpnU1FQ+/XQOPr9ERubhmMwFpGeOQW9Ip6riE+qq5/LnP98X4yP83Xff8fjjf+fII6dwww03hJwxwrFt2zZuueUWAHT6FPy+wERCqzUFr9cTsnayppSQljGOzOzDQ4R/fyN4blZXzKWq/ENOOOEEbrzxxgPSl75GTU0NV199dczycaddx4iZF0aQxWh0lfK4J4jevqsUytGkPGYCbTixjdLnRhNuiP+CHy8DX5BEdqVhDi8bXX88P+PAMrlLnTOAP0iA23XFgWWRKZ9DD/ToSXZhUotA+Ti2bKqGrW4L9eWLqKv4mosvvpjzzz8/JCm68cabqKiopKD0dPIKjwlzqdl71FZ9yZ6d73LllVcyZMgQbDZbyOniyy+/QiOXIWN+S5t9J5tWBxyBDMZMivqdjsO+HZdzN0L1oLU75Oh0ZpxttQA88cQTMfZpPzUERzG9Xi9erxePxxPx12KxMGrUqE7rePzxx/nqq68YMfEurGkDgA4+oKlquy1b1zzA1rSBLasex2pNYfLkSeh0OlpbW7HZW2m1t9LqaKXN4cDni7UABAm9wYLeYEXRp6A3ZlEy+BzMaZHp36NJcDQ5jogqyx39Fn41EJGOIzUKIeraggQa5m4Q5XhIao73As888wzPPPMMLbZA5LB82ztYUkqxpvbDktov7hBXdzLOdAfx9MOQmPS62moAgdGc0z6MEzWUsQ8nYPUky06oHwnsX8Lh9bRQVf4xAJs3b+a9995j9OjRDB06NPS27fF4ePTRR1myZAnW1AFAK0VFhfzmN78hOzsbVVVDs3SnTp3Kxx9/HNPOL37xCyorK9sjrDLnnnsu99zzR9avX8fu3eXIsszYsWMZO3Yszz77LP/7Yh65hdPRKWkYjfkMH/17WppX0WpbS5ujkl/+8iqOOWYGp556KmlpaRQWFtLU1IQQAou1FJeznDlz5gSOiySTmT2J7NxpmMz5od9a9beRkZHJmDFjQlKPIKZOncoRRxwRlxQHUVBQQFFRMQDDhwf8hYN+0AB79uxh06ZNrFmzhsWLv6R6z6ekZ44jI2scXk8TmuYjr+AY9Ib0Tn/XvUE4UWppXEVV+X9JS0sjKyuLN954g2nTptG/f+cRs4MZTU1N1NbWcvbZZ7NlyxbWrVsXWqfolJgoZxAx/r5RcoHOrNq6EzlOVCbc2cHv0/C3a3tlWYqI18a3O4s//BmdQpmwbVViCbIsS6g+jeigTnwZhkB4Y9tV2l1rwuuUwx7i4eQ5+lgESXAwYuwP0zdrcZwlQkk62iUU8UhxdDRZ82mUr/8PrS07yMrKYtKkSRER7pSUFEyWErJzJ/cJMRaqoK1tOzV75mBr3sBZZ53FOeecE1EmMDG0GbPFzNa1j2Fv2YzRaMLjceP1NLN722uUlPTjsPGjSElJQa/Xo9PpaGho4IsvvkBVVW666Sbef//9Tu9LhzqCo5i9hc/nY/Hi75EkGb+/LbRcCMHuza/TWPMdKemDSM0Yhd6QhttZg9tVA0JgshZishQiSzpaGteERjyzsrK4/vrrE0569Hg8tLa2RnwcDgd2ux2Hw0FrayurV69h/ff3kltyLJkFE0jNKEOSOzTDAbeKWGIcToojSLRebr8u5FAdQhPIRsKuIyXy+grX8us7tgnXLofrlsMRzX/czlq8noC07YgjjkBRFK6//nquv/76Hv5i3cMhGzl+5Y2F/OLS6RHLsvOnMGjUVRHLgiH8fWmxJlBxOipwtlYgKwa87kZa6pfS1lrVXkLCYs0lv/+pgEAIgSzp0RvSSc0cvtcTsbryFe3uvndXamFv2oyjdQttju20OXah+j0cddRRyLLMtm07qKmpQghBRvZ4zJZCmuq/w+O2xdTz2GOPMWTIkG71DQJ2Qe+//z6nnXYaubkd2eQcDgdXXvkLzNaRFBSdiNlaEjH85PE00tK4kvqaLyJ0wEZTBoqSjrNtN+PHj+e8886jtbWVHTt2MOfzubQ5HJQOuJisnIDlmt22ie2bnwptf+6553LFFVd0u/89gcfj4bnnnuPLLwMJR1JS01D9fnw+QW7BcVhTBmAyF6A3ZO71+ZNo5MPRuoOqXe/Rat9JUJ4CoNPpuPjiiznxxBP36qFzIPD6668ze/ZsCguLuPzyyzjyyCNZuXIlr7/+BpU1dZz1x3fQGTsfiQoiqO1N5OCQyKe1K0Tfr+KRXH+YK0O4XhfA51EjtguPHAejrpGex5H1S+GpsNtlCNGSrHijXs6mWspXfoi3rQVJ1iHr9KheF15nA962RiRJIbVgBBnFI0kvGkFqfr9Q5r3gS0m8CLUsS/j9HSQ3PGIcIaUIyijatceBMmER5DC5RTQ0n0bl1k/Zs+Xd0LyE4uISbr/9NiwWC0uXLuXFF19EkhSyco8gt2BG+4t/9xHsk6q62bn1eewtmynt15+LL7qQqVOnRpwvTqeTCy64oH3/DWial3HjxnHzzTfzy1/+MtTH9957Ly7B0jSNNWvW0NLSkkwl3weoqKjg6aefYcOG9aSk9SclfTh+v5OG6gWcddZZVFVVsWrVGrxeN9nZufTvXwpAefmeUNKSAQMHkWK10tzcgs/n5aGH/hLxHOsp3G43r7/+Ol999TWtrXb0BitpOaNJyx6BpEgBBynNi9A8qH43qupBqF50phT0pnQMljTS8oeSnj80Ro8MYZNWw144gbgjMcHv4deY6onVSMQLJKp+D5uWPoSjZRd5+WXU1W5PJgHZG/zr9YX88rIAOTYYU1F0qfQfehmpmYNjyGBfk2Of105T7VI87nrczira7DtRVW/IosVgNHHklMlMnz4di8USSizw3Xffxdi4GE1ZmFP6IUkKqt+OrJgwmvMxWQowmnIDeekVE7JiRK9PRZIVNM2P39ca4+DQFxCqQFO9eD1NGI3ZCE1l9443URQjmdkTSU3v0J1qmh8h/DTVL6Ouai56YxYmczGWlFLM1mLM1lJkWYfmc7Py+5u63Yf77ruPiRMndl0wDJ988gn//vdruN1OjMZ0rGkjSE0bRlr68FCk1e9z4/XW4fPaMRhzMZnzKN/xBmkpNTzxxN8jSKbX6+WFF15g7ty55BYcS3HpzwGJbZuexNG6BYCjjjqKMWPGkJmZSUFBAQUFBT2a2OJwOLBYLAlJlM/no7a2lpycHEymgAvIa6+9xrx5X4aG3XS69qQiuUeRmX14zISReOjpJB9b8zq2bXiWcIIcxG233cbRRx/do/oOFJYuXcr9998fMynTmpJCmyMgcZl+5X2Ujj0m7vaJortd2boFf9/eTEQJSCW0iHqiHRuiJRhBeYXX449YDh3JMETU7PPoIEIMQQ/3Dlb9tDXsxutuAk0GoaOpYjlVmz4nxWqh/4ABAf9yrw+z2UReXi45OTm4XC7Wr9/Azp07QxNv9aZUpv7iefSmlPbIlhw6dkp71DwYkQ6feCfJMlp7xDeoF+4svXM0MY4bafdpaJofd2sdrtYKdm14BX+7r3E8jJv0aLeymUUTAq+vkXXL72Hq1KnccccdCa//yspK7r77HvxaPjpDGo2133Heeedx7LHHsmnTJjweD6eeeupPym7xQELTNBYtWsSSJUtYtWo1NlsLV199NWeccQZAKCtp9ORlt9uNw+EgJye+Y0Zf9Gvbtm0sW7aMpUuXsWPHdgAMRhMmY8Br32Q2YzabMBoMtLY6sNlttNrt+HxesvtPYMxpd6LoA+4Y0ZP2wkdmJFmK0O1D5L1BqH7QOoJykiyhOn0diXWizAwCI1k+Vn1zC36fk5IBk9mza0mSHO8NXp+9lMsunMy4KY/H9fcLR7wIargerttefkJQuet96qu/RpahIL+Q4uIiRo4cwciRIykrKwvZIsUbxtq9ezeZmYEMOXa7nTlz5tDY2Eh9fT2qqpKZmYnD4aCiopL6+tqYiJEs69AbrHjcdkCg6IxYrCWYLCUYTbkYTNmB7G2mHHS6SJIWcUJqKi5nNbKsR6dPRVVd2BrXYm9eh7ttD25XIFuPJaUYRWfFYdtKXl4BtbXVpGeNorDkNNocu6mrmovJXEDZiBuwNa/D1rQOWTGg16eRmjEMi7U0RN6DcozGuu+6PM5nnXUWV111VZflouH3+9mwYUPgJrHsB6oqA5n0cvOPoqT/xWiaj+amFdRUfojf7yIldTA+bytCa+Tkk0/ihBNOoF+/jlS5TU1NocjwwMFXk545FiRBY/132Jp+wOtpxOu1R8yO1un07QlAjFitVlJTU8nOzqa4uBiz2YzL5aK1tZXa2lqWLl1Kfn4BRx45hYyMDGbOnElmZmaX+6lpGnV1dVRUVFBRUcHq1WtYsWI5KakDGDTsNzG/fWfoDlHesfllUsx1/OUvD6KqKnV1dfzwww/Mnj2b0tJSbrnllh6NABwonH766Z2u7zfhOCad+zt0RnPcEadocptoEl48fW2wnC6K8Ona5QZSnG3CJ6bFe8EP9/sNJ8iqX2uPLGsxkgQIRF5FGIkMf7iFP/xQ5FDUyFa9idqdC3A2bqe1qRxNjUyRbjCaOPecsznrrLMwmzuPvC9atIiHHurIppk/9Bj0lhQKhs0gLX8IiiF4TGJJY3Qyj3DrNeFXYyflQQwhDt8+/NkQPMbB9T6PDU9bHarPg6Z5Uf3egKe734vRnEda5giET22PfrmprfoSTfOi16WjN7R/9BlxE37s3PYyHudmXn75RVJSEhPsJ598ksXfbyQlYwy1ez7nvvvuSzhBLYn9ByEELpfroHT58Pl8KIrS5ciVpmm89dZbvPnmm0y69DEyiobhdbXiaqlB9bkQmorQVHRGCwZLFsaUTGRFj6apqN7AeoMlPSB18rpZ98nDNJSvwGhOxWDOQGdKQ29KR29KQ2gC1e1G0/xIkozRmI85tRizuRCDORvN78HnsgMyaxbfkSTHe4P/vPsDl5zXMSEv+sYX4SEZZ2ZpIreJRETZ1VZFQ81C6qq+5LzzzuPss8/u9Ka2t/D5fNTX1+N0OkMzb2tra7Hb7eTk5JCRkUFlZSXbt29nx45d1NXV4fV2RDkMxlRS00eRnjWW1IxhKDoL9paNNFTPx2Hfgt8X6fYgywojRoxgxIjhFBcXk5aWxocf/hdVVTnttFOZPn06ixcv5pVXXqW6uhJJkigpKaGiYk+7fY6fwsIiQKKxsQGv14PBkII5ZSAmUyASbjBms3PzS/j9bej1gYx0FouFfv36MXDgQEaMGEFGRkanF7XP52PPnj1UVVXR1taGqqoUFBRQUlJCTk4gFfPs2bN5/fXXQ9tkZh9BVvYktm95BoDBgwczaNAg1q/fQGXlHiRJCUS4NS8nn3wyl112GampqXz44Ye8/PLLYcc0nWGj76KpYSlWa39S0gYjhEAVbXjdDVSVf0Jb62bMZgs+nxev1xdlTSeh11vavTStpKaPxtVWgcdVgcfTgqp6ufLKK2O0h93Bpk2buPvuP2AwFlA2/Oa4ns/dQTyyvGfX+9RWfkFRUTH9+/ejoqKSysqKUAT2lltu4dhjj+1Ve/sTDoeD9evXs3TpUhYtWkRbW0A/qOgMDJp0KhPO/E239KSJSHIQ0ZPUEkacgxrb0IQ1ub0/MtG64OgZ8eER5ejEHj5vwNnB71Px+7UQMQ5Fj8PcHYLkEiLviQGS6aexYgWVGz7GVreZnNw8xowexeDBgxk8ODB5NpDh0kd6enq374eqqjJ37lxee+11HI5W0jMykYCWlmasGcWk5Y/AYMnAkJKJwZyOwZpBam4ZOkOAdAdn2aseNdKRQtNQ3WpcX+Pw/QsRYE2LzaIWx+YtevvwckFs3/gcLU1rgMALst/f8fKg0xmxWAdgSSkjI3MsZmtpwJ2ifjHFxSWccMLxCa/5O+74PdW1RlqaVnHUUVO54ooryMvL67VkJ4kkwrFkyRLuv/9+APRGMz5PrAtUOBSdAdXfMVkwd9DhDJp6Ce7WJlb/dxbFxcUcc8wx2Gw2bDYbzc0ttNjsyLKEXq/HYNDj9fqo3LMHt9vVXqcRS2oJ6dljycgZx7rv/5Qkx3uDf721kF9eNB2TtYjcoqnklsxEkTq0V/FC+OHoLFqsqV7Kt71Ka8vGQHIHTUVVvVgsKZxyyklcccUVB91QVnBGbm1tLbW1tezYsYMlS5ZSUVEOEJJzDBxYxlFHHcnIkSMBsNlsKIrC2LFjsVqtXbajqirLly+nqKgIi8XCJ598QlZWFqWlpYwdOxZJkvD7/WzatImVK1eyffsOyssraGioC5GpAAER7ZPa5JB90amnnsrw4cNDFjxOp5O2trbQ38rKaqqrKxNmF9LrDaSnZ+BytYWIT9mw32Cx9sfnbWHz+ocRItLoNdwXs6jkTKr2/JdzzjmHK6+8EiEELS0tNDQ0sGjRIt577z10+oCNnBCCjKwxlA66OGSptHPzP8lMb+Hvf38sVL/T6WTz5s3MnTuXRYsWUTbs16Rnjonpu615Lds3PwfASSedxKBBgxg1ahT9+vXr9FxrbW3l2WefY+XKVbS1taIoBoaNvrNPXC5C2jPhx9a0llbbJjyeOiR07dIeG35vIy+//FK3It77G6qq0tzczM6dO/nhhx9YuuwHGurr0OmN5A2eSMmYoykeeRTGlPg34Xja184m2CVKZxwpseggxBDpzCAr4ZPSEksxoqUPISeHsOixz6uGLNCCet3oVMqhoc5gNrigDEP1Ul++iKqNn9Jmq2L48BGcc87ZMZPU9hYOh4Pq6urQhNTVq1fzzTffsGt3ObYWGzZ7C6rf335sdGQUjCJ7wOHklE3GnJYXV06huf20NuzE5ajFnFqA0ZSNorcE7n/hE/LCfF3D9dNeTwutLVtpbd5Cm20TzrYaivqfQV7Rsfh89sDLrtBwO2uwN2/A2bYda8pQUtKHsHNzwC/3b3/7W2jCb2NjIzt37mTDxo2sXbsOl7MNkykTWZcGQuDzteLzNjN+/ARuuOE3MamTf/Wrq7HZ/bhdjQRlTZ25BCWRRE/R0tLCxo0b2bNnD7m5uZSUlGC1WgNOMrJMW1tb6FwORsvNZjNtbW28+977VFdVklk6muY9G0BoPPjgg4wZE/uMC4cQgoaGBioqKigvL2fTpk388MNyCvtNZNfW75LkeG/w7/cWc+W5UyM0vBm54ynodzwpaUMik0gk0BsnIsiVu96npmIuEDCAP+644xg0aBCHHXbYj27Wb01NDdu3b6elpYWSkpIQgd3f8Hq9VFZWUlNTQ11dHc3NzUBgQkkAUvtvqSFJCjq9EZ3OjCTpAQVFsdJqD2h9DQYD+fmF5Ofn0txiY/u2regNqZgt/VFVF5rqQ9GZKel3LmZLIPWvEBpCaLTaNrJj6z8i+qbTWUjPHA+SRGPdIv76178ybNiwiDKrV6/mD3/4A8ccM5Mrr7yC77//nn/84x/0K7uE3IJpNDeuYsemF7j00ktCE2mCcDgc/P73d1JRsYdhY27HklIasV6oAqezih2bnkNVHQhE+7C1oKCgkGnTjuK8886LO4TX1NTEn/50Hzt37gDAYi0iNX00JnMhRlMuJnNBj2QW0QjNbFYC19nuLa/RUL0QgNLSfvzqV1f1WB/el3C73Xz33Xds2bIFTdNwuVzU1dVTW1dHc1NT6MUnJbOA/GFTKBwxmfzBE9BF6evC0RmZ7QydJfDoSarmeCQ5GtGSq3gT9ALuFmpIrxuUU4QmhrVHXlWXL0AM3Xaqt35B7dYv8LrtTJ48mXPOOYfhw4d3ue/7AkIInE4nDQ0NrF69miVLl7J+3XpU1U9KZimZxRPJ6T+ZlIyBIeKrefysnX8fjpYdoXoUnRGTOZvCQWeSlXdYoG5VoKkaHlcdDttWWlu24mzdhstZDxByhQjVoehRo6QkRUXFDB8+jO+/X4LT2YYkSZx88sn86le/ijtJzu/3s379er7//nsWLVpMc3Mjer0FVVXRNA833XQTxx9/fMQ2O3fu5Lnn/sHGjRswmnLwuBt46KGHurQeSyKJ/QFVVZk/fz7z5n1Jv36ljBs3jilTpvSKY/h8Pr5ZVsmTf7khSY73BrM/XcmFP5vIm2++ybJly5g7dy719Q3U1dVituSSlT+VnOKpGAxZoW3CIwSd5Rf3uOqpr/kGV1s5DtsOdDqFn/3sNC6++GKMxt4NVycRi3//+9+8+25HCmqzJQ9N86GqnoC2T+uI8prMubhdgQfXkCFDeeyxv4XWVVRU8O9/v8qKFSswmy14vR7cbheKzojZnI/f78DjsSHCtMH3338/WVlZrFmzhn/8I0CWMzKyGDlyBHfe+fuYvgYj80F3hvnz5/PXv/6Vwn6ngxDU7JnD1KlTuP3222MITV1dHTfccBOqpmfo6NswGDNj6t66/q9INDFl8iT+97//ATBw6FXYmzdia17OwIH9efTRRxKmcm5qamLVqlWsWLGC1avX0tIS0I3Lso78olMpKDohZPPTEwTJMUD1ns+o2vlfrr76ambMmHFAnSqEEHz++ee8/PI/8XjcpGSVIOv0KAYTxpRcrFkFWDPzsWbmk5pbSlpeCZLUQTqlEPmM1PrGWLbF8f2NtywaXSUKCSfC8RDugpFIvyzLEv7wiXJaRwKNjtTLHZHjcK1xeARV82nU7VjM1kXPIqFxwgnHc+aZZ1JUVJRw/w4UnE4nK1asYNmyZSxZuow2Ryu5pVMZdNhVKDojqBptzdWs/uYO0tPTueqqq3jssY6RnGHjb0OSFWrLv8DZug2Px4YkSZT2G8DYMaMYNWpUiHj+8Y9/pKmpmZTUVI6ePo2xY8eiKAqKopCZmRmK8jocDubPn8+IESO67SkshGDbtm0sXryYNWvWcuyxMznllFNiSEVjYyN/+ctD7NpVTX7pzyjf9gZHHjmVu+66s4+OaBJJHDz4foOdB+64JOlzvDeQ2p9Lsiwzc+ZMZs6ciRCC9evXM2/ePBYsnEvlzv8iyXoyc8cFHB90ZpT22f2ZuRNASHEn5BnNuZQMDAxb+dVW6iq/5sP/fsyiRYs57riZHHnkkQwYMGA/7/GhhwsvvJBRo0Zht9upq6vD4XBgMpkwGo2BGbYmE3l5eeh0Ol555VW2bm3i2GOPjcnIV1payh/+cHfou6qqbN++nbVr11JZWUlGRgbZ2dmYTCZUVcVgMDBq1Ch0Oh1ZWVn84x//4PDDD+fee+9N2Ndor8ypU6dyxhln8NFHHyFJEueffz4XXnhh3EhfW1sbhx8+kW+//Raf347BnB1ZQAj8Pjs6ncaqVQHdYnrmYDJzJpCZcxi7twm2bl3MypUrOfzww+P2Lysri2OPPTak/XU6ndTW1vL111/z4YcfYmteTnrm4Sg6AzpdKplZE7pFloUqkNBwtu2haud/GT16NKeffvoBlRW5XC6efuYZvp0/n+KxJzFk2oWk5gaIXKRfrhxDMKU4UeHQ9yhLtniJMyLKRzlShEdzw0lreCQ4iHD/4ujtgymgo/sQPulPUzsItt8fSZDDE4AEt1PjBAGEJlA9PnYuf4uKtR9y1FHT+L//+/U+fyglQlDG5HK5yM/Pj/siaLFYmDZtGtOmTUNVVb766iuefPJJ0nNHk1t6VGAUxh6Qkk2aNCnkya3oLBjNubidNdTtmUt6msJxp5/AqFGjGD58eFy99FNPPRWzLB5SUlI47bTTerSvkiQxZMiQTieyLl26lMcf/zs+n0xe8cns2TGbvLx8Tjnl5B61lUQSScTikI0cz1u0iROmjeDNN9+Ma4dVV1cXcjwoLS3FaDTT5mzD2ebEZmvGbM0jv/RksvImI4nIdwghBF5PI232nbQ5duJy7KDVtjOizAknnMCvf/3rH53M4scKIQSqqqLT9f37XlVVFQUFBb3SU27cuBG9Xk///v158MG/YDabGDduHB6Ph/r6epYvX0lFxW70hhSy84+iqP9Zce33Wlu20NzwA7JixGwuJDPnMBCC3dtfo6n+B0455RSuvvrqhOfbtm3b2Lp1K83NzRQUFHDMMceE9mfbtm384x8vsGdPBS6XC01TGTH2DyHJSWcI6Y5VL9s3P4+9eQPPP//8fo8q+nw+Vq1axXfffcfixUvw+HyMOvkmSsbNDBHieFKEzsgxQqVy/Xc4W2px2RsxpWYy5MjT0JtS8fs1fC47DbvWk5bfn5TsItoayylf9Q01W5YjSRKaEIyceT6lY4+OOykvWk8c3o/uoDNiDgECHvIADpNUaGrkZL3gx+dRI5JmeF0ONn3xGI0VK7niiis4++yzD+hLz6OPPsq3334LgKLoKCktZeCA/vTr14/+/QN/g9Hauro6du7cySeffMqaNatJzRqG3pCKJOkwWQpwtGzD1riOW265hf9+9DE7tm8LtWM0mrj//lkHTC7SFYQQvPTSS3z00UcAyIoBTfWSl5fPM888HWMVlkQShwoWr23hwbsuS8oq9gavvreYK86dCsDQoUP529/+FrJRgw6bkvc/+IB7//jHCIH4tm3b+Oc//8XatWswmrMYPuFu9IZUWpu20lS/jFbbOtzOBgDy8goYOXI4K1euxGaLTGRx6aWXxuhLk/jpQVVVnnvuOeb+73+YTDm4XfXIih6TKROTtR/ZBVNIzRiZ0IM4kfa9Ytub1FV/06WXsKZpXHTRJbhcbRgMqXg8doYMGcr11/8fZWVloXIrVqzgiSeewuM1M3TU77tFhCS9EiLI29Y9g9uxmccee4zS0tIutuwdVFWlurqaiooKdu3axY4dO6irb6Cqqgq3y0lKVjF5w6cx4PBTSM0OjxbHpiEOQpalCBkFgOr3sujVP7Fn3SL0BiOZmZk0NTUCMsVjZuJpa6Fu67KQZjk4k9tkNjNxwgQMBgPffPMNAJc+8Q0IOSJCHIwch0/gS+SH3NlLWbhdW0zkONy/OCinCEWT1UgNclh51eOntW4X6z97COGzc8cdt/dKNx70fd22bRu7d5dTXV3D8OFDmT59OuPHj+/xi+y6deu4884OuUB64UgkodLWVIHP6wQClnEIgdfridl+2LBhyLLC9u078HrdDB8+ggcffACdTsfmzZtpa2ujqKiI3NzcffKS3VdwOBxcc821tLbaATj66KOZPn06Y8aM6dak6SSS+LHi+412Hrh9/8gqDlly/NIbX3P1pR32UekZmdhtLaSkpjF0yBBuueXmGE3kiy++xJdffonL5Wwn0gpZBZPpP+QSJFnPqoW34Pe1YTabufbaazn88MNDdbS2trJ06VIkSUKn06FpGhMmTPjRZQhLom/h8/l46KGHWLZsWYSLRnbBNHILj8KaNihEQuPZB/q8dip3fojqdyJEu5OA8KCpLhz23QBdpn91Op1ceullyIqVwtLT0evTqdz9Hi5nNUceeSRHH300PyxfzrwvviAtYzj9Bl6KwZiVsL4gwomxJEu02Xexa+NLeD1N/PKXv+jSN7gncDqdvPTSS3z19dchhwKjORVL5gBMqXmYswooGHEUGUUDURQ5lFEtXpQ4sDy+jCJIGHet+ILv//NARB+uvvpqXC4Xcz6fS2ZmJsfOPIbDDjuMqqoqtm/fzsCBA5kwYULot3j33Xf597//TcHgcUy77G5ScgJkXWufDBcuiwhPjRweRQ5GesPt3DRNC6VbDiLcui2ceIdrjIGQzjgeMYaAjVvlmnls/uo5iosKufvuu3o1CtDY2Mhjjz3OmjWrsaTlYUorwWjJxl67njZbFVOOnMrdvdTFbt68mT/+8V7yRvyMARPPQwhB3bbFtDXuQkIPQgYNdm94E5PJzPHHH8epp54aemFzuVysXLmSsWPH7lO7zX2JoMTE4XBQUlJy0LkjJZHEvsCiVU08dM8VSXK8N/j3W4u48qJpjJh+O20tOxFCxWDMwOtqoXrrHNLSrIwZPYqxY8cyY8YMjEYjN9xwEw0tErmlR6EoFlLSh2AwpIdIi9vZQF3llzRULyAvN5uf/ew0SktLycjIwG6309bWxoABAwLWPBs28NFHH1FSUsLjjz+eHOr6CcLpdPLiiy/y5ZdfY00dSGaGj5NPOpGvvvoau72V+vpaTOYsUjPGkJoxgvScURH+w6rqYevqv4LWzJAhZciyjCRJmEwm1q5di9vt5sILL4zRWMdDVVUV//rXK3z//WJ0OiOpGaORZQNu5x7aHBXodCaK+p9DTv5RkDgzeAQkRQoQZFmKkFfs2vgmDdUL+M9//kNqamqvjl04VFXlhhtvorq6hpLRZ5NRPIyUgv4YU7JQDJERPlmWUPQyBkPA5F6nlzvNgBlvnaKTAY3P/no1LVUdw+3jx49n1qxZ3e53a2sr999/Pxs2bGD0CZdw+Fm/jvEcjmfZBsSQ4XACHZ0cJIgO6YQWIsh+v9ae9EMNbRuybIuKLvs9TjbNe4Gq9V9w3HHHcd111/X6vvXnWbNYvWYDI477LRn5o9F8fpor17Bn3Ye01G2kpKSU5557tld1A/zf//0G1TKMwZN/ydbFL1K9NZBGXaczkpo1DEv6IGp2fo7FbOCFF57/0ZLgJJL4qcJms/Hxxx9TWFiIEILF33+PR81k9Q+fJyfk7Q1MqYHhpYzCYWSXjI94iGQWjqO+fDEr123l22+/5aWX/0lmRiZVVXsoGXYm+f2PjTSCD9ZpyaHfkAvIK57J7k3/5OV/vhKTCSoae/bsSRqy/0TgcDhYsGABDoeDtrY25s37EofDSemgC6ir+pxJR8zkjDPO4IwzzkBVVT799FNefPFFzKa17Ng4H53OStHAs8jKOwydPgWPsxaHvRyzxUJTU0tYwpdAchOA1157nTVr1jJjxtFMmTIl4bBqUVERd999F3v27GHhwoV8+eVX1NbWMmTUzeh0aSiKGYMpcLMRcdJAx0X0eS3LyLKJggHH0VC9gM2bNyecINgTtLW1UVG+m2FT/4/CUceiWPTIejkilXAoEtw+gdbrVZFlDU2TE0oqwh0hgkRVU9ujuorEuFOvY/5LvwuVX7VqFcuWLeOII46I20+3283mzZtZv34969atZ/OWzQgUJp55PcOmnx01GS7Kv7i97ei+QSwxDvwNSMTCs7iF7nFaoM5gdFqN2E6ErNuC31Wfl8rV/2PX0tmoHgc33ngjJ5xwQvd+nOhjqml8//337NyxE4Mlk/S8kdRt/4496z6ktXEnZWWDueqy3yY8ht1FRmYGGzZ8jdO2h5aaDQCceeaZZGRksHr1Gtav/wyExpAho/eqnSSSSOLAYOvWrcyePTv0PT1vCPlD9/550l0cspHjd+au5vyTxzP9F6+hM1gisiSFG9y7HbXUly9C9bvQm9JJyxzJpsUP4/d7kSWF3NJjSM0YjNlSiNEUacCOLPC46vG5He1ZzUy0tmzF1rgWRWemtmIeAK+++upBmQQhib7DihUreOjhR3C7XOgNFhTFhDllENn5R7F17eNAYNb6E088QV1dHZ9//jnffvstQggKCoqoqamKqG/k4X/EklKCy1FLY+13qH4nimJG1plQFBOKbMJsLcZh346tcTl221YArrrqKs4666xQPY2NjVx55ZUA3HXXXUycOBGj0Yjf7+fOO+9m1+56ho++G1mJ9V3tDOH+xiFypg9EM4XQ2LT0L+jlFv785/tCjgC9gcfj4fnnn+fLr75m4s8eISWvH5JeQdbLoUxoktwho4BIHW+876H+Q5j8omO9Tt8hWZBliWXvPcaOJR+HlqWlZ5CTk0N+Xi45OTlomsamzVvYuWM7mqZhtKSSM2AMuYPGMvDwEzGmZIUmBMbrS3ja6PBocngfwssG+q20f+8gz/r2KLrq10LRY59Xxe/T8HoDUhS13e9Y9at4Wu3Ub1tM+fJ3cdpqOfroo7n44ot7JaPw+/189913vPPuu+zauZOs4jHkDZ5J1YbPsNdvY8yYsZx//nmMGzeuTyQAtbW1LF68mHXr1rNy5Uq8Xg/33HMPkyZNAgJyJk3TktaaSSTxI8aGDRt49K9/o9XpY9IFT4HQWPCvy5Oyir3BO/9by/knjWXGdW+iM1pCaVBDHsbRfp7tKUVrd85n+6qX49Y58vA/YE2LfNALobFz40vYG9ehCQ2d3oLX3UJqWjpGg5FTTz2Zn//85wn9Z5P4caO8vJyNGzfy2WefUV3rYsjoW9AbOnTm9uYNbF33BDq9tT0Llxu/34/JkkNG9uGomgdHy1Y01YHHbQMEaZmjGDj8V/GTc2jxNQ+ttq1sWRfwaz3nnHOYPHkyQ4cOBeDnP/95hN556tSpjB8/Hr/fzwsvvMDQ0beSmt5hGdVZdkgI00S3k2OgQ1rRHk32upvZuuIxvO56fvWrq+J6tHYGt9vNnDlzePe992m12yk77CoKhx2LbNQFosbteudgu4pOCWV3k8Ii2uHZ3iKIvCKFygUJsqLvIMqyLHWQZKFhr99DW3MtbnsDbc11uGx1uOz1uG2BzI5phUPJHTCGgqHjyCgYgKJTkJUAYY0mt8H0zOGSiWhSHPQoDo9qB8m1TheQsgTr6dhGDqWWDmbG83pUfB4VtzuQyMPRVEfdtu+p3/wdTZXrEJrKpMmTufyyy3r1EuN0Ovn888/56KOPaWxsILt0HP3Gn4NQYe3/7ic3N4ebb7pxnyal8Pl87Ny5kyFDhiS1t0kkcYihsrKSm266GWN6MSNPup3vX7s2SY57g2eeeYZnnnkGe5uHyvIdHHvTO+iMAZKh+VRUpw/Vo4a0hhEpRlUtkHXJVgmShLOlgqaaH3C17iEtazQlQ89FUjtuvn6/k/Itb9JUu4QLLrgAg8FAbW0tp512GgMHDjxQhyCJ/QCfz8dbb73Fu+++i6YJjKZ0cotOIL/4+ITb2Js30tK4iszcw1H9TrZviNRcjpx4LyZLAS0NK3E5K8nJn4bQVGor/4fbWY7OkI3JXIDJnIfekIHf10qrbQvW1EFkZI/D62mioWYhzY1L8XnbMJksjB03hrFjxtDa2hoxRBWEyZzNyPH3deppHDNRMJi6OIzMhZPO4HpN9VK++W3qKr5i0qRJ3Hbbbd3WsM6aNYtly34gp2QapWPOwpJeEIgUy3IHOQ7TOof3NVxqEOpfOzEWmkDWy6huFcWkRBDpwK4FSKikREZ6DQZdRJQ2KJWKJrSKLrI+nV4ORXnDEe4yESrbXs4fIs+RSTx0ejnUfvjf6DqC+662k2OPy0+bvYW1nz5N5fpvkCWJ0aPHMHXqkUyZMoXs7Chf7R7gD3+4h7Xr1lE0eialE87Ekl6K6vKy+PWrGdCvgPtnzUrOt0giiUMMTU1N/POf/6SuvgFN0/j9HbeTk5PTZ3WvWrWKVatWUd/QSF5uTmjZ8OP/j03znmXo0KEoisL111/P9ddf3yftRuOQ0xwHD9YH8zdy9jEjMVl06E2B2eOapsNn1OH3+FGdvgAhDot8CWQkVcOSVgyA2VJIVkFAGyfCNIBCFaiqh80rHkLzt3DLLbcwc+bM/b6vSRwY2O127v7DPezetZuC0tMoKD0JWe7azzotcwRpmSMAqK34LLTcmlJMXsmpmK1FtDQE0kwDOFt3Y0npT0PtAmbOnEltbR0VFYuormgJbZuTk8eurd+i22UmO286BSUnUTLwXJyO3dhbNrJp8yZ+WPZKRDZBgOzcqWTnT0FvSI8hxvEix4mIcbz0ymgayDKKwcjAMZeRkTea5cuf49VXX+Waa67p8jgBjBo1imU/LKd0xM8xGbMD0V80JDngyiCpAZIrtEgyKskdJBhVAyWwPkiKg/uns+jbs8F1RF/lMDlF4Bpv1+kSSViDEd/ouQRSHNmEpgr8REZ4NTUgo4iOKIe3EepTJ/Zz4QTZ79MwGJWI7YOtqj4vC164Hs3TynXXXsv06dP7JOqyevVqVq9exeSL/kTB8KkB72dPYHTOmllCm8OW9HlPIokfEYKZXmtra6mtrcXlcoUMC4Lw+/389W9/Y+uOcoQASfP0+joXQrB9+3Z2797Njh07WLlqNRXlARemzMIyzFnF1GyrxWWvw5yaRX7ZCDbNg2XLliUn5PUWwQeETqdE6POCkSEvIPk0hE9uH3JV24dfJfBFPsyiZ7NLikR9xXycjmouv/zyfUqMfT4fixYtory8nIsvvvig9t/8KaCtrY177vkjVZV1DB//eywp/XpVT17JyaRkjAQhsFj7BYaDNQ2jKRdJkhFCI7dwJkLzIoTgsssuC72ZO51OWlpa0Ol05OXlUVtby5w5c/j0089oqJ1PyYDz0etTSUkbTG7BDBSdGa+nCberDo+rFmfbHjIyx5GSMjimX51KKsJkFBBLjKMJdBCZ+RMoGXouH3/8JpmZmZx55pkYDJ1rnH0+H8HaAzKojnTukiYhqQKhKUhypFxCBC9dRQ5poGV9ZL8D13ksKY6OxIZHkIPfg5P7ZKVjG0mOzXAXTZAhLNGHEulHHL4OIrPnQYdmOjRpMBS9DkSWo6PZ4d91egHCQ1tzDbfccksoQ2Jf4D9vvklGYRmFI6YiSRIGQ7vmXNMoGn0q6+Y+Qm1t7UGZZjqJJJLogM1m44UXXmDpsmW4Xa6IdTt37uTaawNShrlz5/LJp5/R0tzMuNOuZc1nL3DZZZf22LJWVVWWLFnCe++9z5YtmwGwZuaRPXACh0++mPzBh2FKyYyYqCwrEl6Xo292uBs4ZJlWVmpgKC98Jnjg4aGhb4+w+JRATEcGhCKh+bTQ/wGnCgEEZBfRaaQz8w/D3ryRV199lenTp1NQUNDn+/Dll1/y97//PfR9xIgRHHHEEbjdbv7zn/+wcdNmmpqamDzpCGbMmMHgwYOT2uZ9jIcffoSKihoGj74FS0rvE11IAqzWWGJtthQyfsrfUf1O9IZ0fN5AYplNmzYxbdo0IJAi12Lp0CPn5+dz5ZVXcu6553LvvX9iy5bXQusyssdRNvw6jKYcjKYcyBwJJCbB8dKlh0++Cy0LI38R0oYEkouCQSfgcTfx2muv8+677wf6L0nIksSECeO4+OKLycoKeCvb7Xbeffd9cktmYjBkxo1OC00gPP5Q/ZJeQTHGev9KcuC6lrQoIh9m4aZ6/O2SDSVuhjrVp6GFvIcDL9gRUglNIDpJPx3ughFcHu1O4fXERo07Jt91vGwEHSgg6FkcqNNk7ojcCE2gQUjiYc3IxGCy0NTUFLNve4Oc7Bw2bVpE1br5FI2egRDtfTXrcTtrkCQZh8OBECKuFtjhcGCz2XC73bjdboqLi8nIyOjTPiaRRBKdY9GiRTz77HN4VcHwYy8lLa8US2YhKdmF7Fj6OZ/892mam5tZunQZmhBYMgtIyTax6tN/UFRUzBlnnJGw7j179lBTUxNK+uNwOJg3bx4ff/IpdbU15PQfw6SL7iNn4AT0RlPCLKaa2u70sx9FwIcsOW50uOMuDxLkwOQbXbtrhYyEFniwhQ0Xaz4Noh6WFdvep3rnZxHLbr75Fs4660xOOOEE6uvrcTgcvcoAFYTP5+ODDz7gtdcCJKeoqIhf/OIXHHbYYfj9flauXMkHH3xAWt4wLKmj+N+8b/n4448xmS2MGD6MUaNGkZaWFnowDRw4kMGDBycdM/YS69evZ+XKFQwace1eEeNEk+qCkGU9cvukPr0hnZTUfsyZ83mIHCdCSkoKjz76SIhsPPbYY+wqV+OWlRSJttadVOz4D+lZEygsPTViXRDdnpwX6Hjs+iBplGQGjLqI/P4zaKhcgqb6QYCmevjq64V8/fU3nHrqKaSkpLBp0yb8fo2SYWdEyDeCsojwdkMTan0qWrgGWVM7pFJhxDVIiENWjXS8EKioEfZwcfeRDglEoFzkPishva+GFuZQEW3HFiS14aNSgUl2WkcUPEjuo3TGgd0T+LxqKGodlHmE76PWvr2ik0nJLqKmpibm99kb3HLLzQghWPD2A+Sv+JzDzrsLvSkVWZZIzx+AwZzCb3/7W6ZPn87ZZ59NbW0t1dXVbN++nc2bt1JfXxtRn06n59hjZ3L55ZcnkyclkcQ+hs1m4x/PP8/CBQvoN3Y6R5z3W8ypgQBF8MV82PRzqNrwHavWb6Zo9HRs1dtpqdlFcUkpF/7qV0yZMgWdTkdLSwv/+9//yMrKIj8/n6VLl/L9kqXUVAdcmHJy8zju2Jl8+N+P8Pl8FI6YztTTfk9mcUeK9shMn7GyNYD9OUPukCXHwaMY7WUahCxLoJdRjDrAj0bgMacByAE3i0C0SUEOf5BGpfhNyxqOObWEN998izfeeCO0vLComEcefqhHkRBVVZk7dy6z336HpsZGAEwmM3feeScbN27ktttuZ/uO7aEMYYXDjqdo5LFomkpL5UZsNRspr9/Munfex+91ozdaEELg87QBkJmZzZAhZUycOJETTjihy6HtJCKxdu1aDIYUMrLH966CLkhxBNpvDEIITNZ+rFmzkKamplB0NfFmciiyXFRUxNZtK+KWs7dsYvvGp8nOzqaq/GP0xgxyCqbF9LGDbGqgRCaeSESMo8lk+HJLejH90s+OWN7PdzaVWz7h0znzkCUZWTFQMuR8dPpAApGgM0XMRDtZRmpvVtZ3kGAUOTRZL7qvHWQ70tEielloXdg9Q4qSNARIb3vEVwvIGPw+X4f0gsD9RLRrgkN2bVFZ70JSjuDDIdheHLmGFkV8g1HlwHINmQ6NdZAo63QKqbklVFZG2gVG7KcQrFq1iszMTAYMGJCwXDh0Oh233fY7JAm+/fZbXM3VmEsz8PtVikYdRcGIKZSv/JJFnz3BggULYraXZB0ZhSMxmLLQ/B7qdy/mf//7H5MnTw5ZsiWRRBKxcLvdoQyJZrM5dM83GAwIIVi9ejXzvvwSR2srWVlZ5OXlceaZZ4ZGHIUQ/O53t1FbW8tRl/6BAYedEDG6E7wvCSSmXnYfa+f+k22L/0thYRE33HMPhx9+OLff8XtefPHFkMRMkhVUnxcAc2oWeUMnc8SMazBYs9jx3bvMfvttsktHMf6suzCmBAJ14fdXVRVooeRHEoRYWXufFCl0v98fOHTJcTsiZpOH61faHzp6oxJ6OApZCv0Umi/4cNPaZ8YHrN9Kh/2cosEn0VK3HtDILBiLolgoLDsJp203Rmsedbu/oXr3V9TU1PDJJ59QVVXF2LFjOe6442hoaMBgMLB06VIGDx7MkCEdFlobNmzgueeeQ9GZOOzMR2kqX8nOlW9www03IEkSWaUTGDT5CqxZ/bHmlGBKyw5IQRSZrAFjyRowFlkv42/zBrSZkoQQAretBnv9DhwN29myazvLlj3PW7Pf5oLzz+PEE09MkuRuwu12I4SgsXYxRlMOKelDkHpytfYwGYwQGhXb36KhZiEXXXRRjyP/48aNY86cOdRVf0Na5khMloD0x9VWyc4tLzB27FjuvfePvPjii8yZ8zqSopCde2Rg4wREPu4EvAT7FbdsnOV6YwoDxl/MgPEXx5TpzsQ/IDDK015O0gJpTAIjQe1EWYskyiLO/gWXqe2rtKgkI8EbuQbtSUaiiasainpoWqT2F2jXBwelFXLMvSgie15QZ62TIwh00KYtmjjHk4MEj4fQBHmDRrPy4+dxOp0RkhwIDH2+8OKLrFyxAqPJxN133cWECRPi1hc6VkKwbds2VqxYwYIFCxh53GXkDBgZtU8KpWOPJ6NoGAte6JiImZWVQ25uDpoQVFbuwO7egN5g4KijpnHBBecnnX6SSCIMFRUVvP3221RVVdPc0oLNZsPrSTAyrijo9QY8bhdpef2wZpewc8MumufNQ1VVCgoKsNlsNDQ0UFNTDUDdztUMPPzEiHqC95u67StZ8uaD+F2tXHnFFZx++unodDreeecdNm/ayITTr0NnMCMQ9B9/HC01u9pfeocgyTJ+n4rXqzLh53cy3P4rDOZUFL0poo1Q39vvsRrh987w+8n+TaZ2yJPjoFYlZIOkhB9krd1+qX0iiSxCkZ5g1CdYMkicA7PeU8gpDkQ2gg9vc2oeJksuAOl5o6jd/RW33XYbOr0Jv8/NggULeOWVf9PW1iEoP+mkkyLIcUlJCVlZWTQ1NaMYjRSOPA7FZMCaU0JG6TCMKZlhb1XgdflQjErHUHHQz1WSkHTtD3MBprQCTCl55PabDIDTVkXFug954YUXePvtd5k2bSqjRo0iMzMTRVFQFAWr1Up2dnaSOIdhwIABmM16dm99FYCiAWdGyBF6i3g6XwCPq5766vnk5eVxzDHH9NjDdfz48QwYMJDdO99G2iWTX3oKrrZybI3rKC0t5c47f49Op+Paa69tH7X4d/tEwGmIODeiuDrjbkSMo9eFIsBK2LZx6k5IrsPbCZJcuSNiGiwTmMintkujlI5j7KPd6SJS0hDaNkhWfR0+E7FkPSrxSNjEvXhaYa2TUYPoSYDBv5rqZ/Hr91OzZRmKzoDQVFRV5aRbXiQ1pzBGnxxNkMP3b+Bhx7Ls/ad59NFHOe200ygrK6O8vJxly5bx8SefkJKZx3HXPsDmhR/xyCOP8tJLLybMtgjw2Wef8Y9//ANFp2fQEacw+oQrQ8dI1qSQbERSJFKyS9AbraSnmjnuuOO44IILki4WSSTRBdxuN2+99RYffvhfDKZMrJlD0aeWUZibjsGcjt6cgc5oRfN7EJIHVfOgqS5UzUNm6Sgy+41CUWQ8jlq+fvoXIStPg8lKSlYeJaOmABL9x82ISWOv+r2s+ewlNs5/m9GjRnPLLQ+Rl5eH1+vl0Uf/yoIF3zLmpMsZfcLFSFK7JlgT5JeNDb3AQyAIoNMJ/GhYMvMinnPxggLB+2u8l31N01D9PRh93Usccj7HQXy4YCM/P3okZ9zzEXpT4pu836/i92uoPg3V35EkJOh7rPk0RLvFUjBpSOihE7SKUrWIB5FApblmFSCRUTgGW816fO4WmmpWkZo9hIqNH2AxG3nllX+h0+lYv349X3/9Nd9+uwBNSKRkD0BW9OQPOZqicSeiGBRMVgOyLOFq84Vm2gd1k8Hh42DWP1kvR5yEIfIcTILSvj+O+koqN3+KvW4tLkd9zLHJyy/g6aeexGw2781PccjB5/Px4osvMm/et4ye/CCy1OHjGk10OyOM3YGtcS0V29/E77Xxpz/dy7hx43pch8fj4eWXX2bOnDmUlQ3hpJNOYMaMGRERRE3TeP755/nss88YOPwXZBccGVNPhK9wtI1Zd1ws4q2PIsidEuwuyHl0uRiSrXQeeUjU7w6JhhxKMhLPnSJIUKWwocHI6G6HN3K4rCK6XdUf8FtfNvtB9qydz9ln/5x169axceNGAI685B4sGfnkDhzd0U6U33Jk3wLR8y2LPmb9l2/RVLkjtE5vNDP25MsZddyFtDXW0FixhYWvP8jPzzyDyy+/POGxevAvf2FblY3jf/MEAl3ohUf1a2iqwOv1o6oazeUb0TQvnrZWVn3wAABPP/30XmVNTCKJQxlCCL777jteePElbC02CgaeRlHZqUjoYu6zEfeoYOZQow7Fog/xAlmvoHpbMBj1WFLT0ZuM6HRyh3tO+/2iuWoH6774D611FdjrK/C5nVx++WWcddZZoXvXggULeOSRRygeMYnJ599CSk5JqD/BCcdBsiva51oEiLOG19sx/6WzCeFBxN4/JXzuNj554KxkEpC9wYffbuTnM2LJcfQbkt+vBrJWhaVVDSfIwqeGhmyDqaejrd2if+h4SQgAVL+b3WvfoW7Hl5x55ulMmDCBv/zlIZzONgymNLxuOwaDEbPZhM0WcCk46qrnySjqj2ifmR4+HByR3S9BP4LEPbpscH80X+DjaW3E62hFCA0kFY+zme1rnuOsM0/nyiuvTGaeikJDQwPXXHMtqZnjsaT2w+NqwGDKIiV9MNbU/iHf40TRT4i1CExUXlU9bF/3LKqngiee+Dt5eXkxZboDl8vV6YuOEIKnnnqKefO+ZPCY68nIGRvT12AiDohP/BO6WBBn3zohq/GIclytczjk2Eh0DGlvf5mN135n5Dj4PahLDo/wBtclkjmEz8DuLDV0OJoqt/HFE1dz8803c+yxx8bMCJdkhfMf+iKQia+dHCcixeHkWQhB/a7NtDZUkVk0CI+zjZbqnWxf8jmVG5cBMPaky9n09WyefvopCgsLY/omhOCyy6+g9IhTGX/atRETDlV/IG21o6mGxa/+Hnt9BQBlR12E6lPZs+ID3n57djJynEQScVBVVcVzz/2DVatWkpk3jtJhF2GyJL7fh2cJjSbIskkXmJBrUtAbdCj6wGiXwRgQDATnLARe1v0seOU+dq/6hpkzZ1JUVMSUKVNi5h9UVFTw4F8eonJPBZKscOnj80Bqt8oNys7a7wcQS5D9YZHfaFlFNDrseDvu0/uTHB/6soqo4UwtzhtLcHIePg10Cmr7YKokSyB3zJLXfCDrgsO1YSRVjiTM4ROBALyuFqq3zqN22zx8nlaKiorYuXMnH3/8Cea0/kyY+Us8zgY2fP030tPTqa+vw5SSxcCpF5OSUxIi7oE+yTF6yRCR0BND3gUdThzhM/UjtpUljKnZGEwZoRcCa/oA3K7Tef/999m9u5xf//o68vPze3bwD2Hk5ORw6aWX8K9//QtHyxpyc3Opr/yeyh1uZEVPasZQsgumkpk7EVmOf5l1RiRDkGV0eguDJ/ya1fNv49tvv+Xcc8/tVZ+7GgGQJInrr7+e6uoaNm54gYkznoybIESSE0sfuiLMIcQhxtHloiPwoUlm7aMlEQgS9qhjGtN2WGKQTvsXta47IwDhk/XiEdXo6G5nE+6qNizAaDRx9NFHI0kSTzzxBH6/n4yMDP48axZuXQbGdvu2eG0FEbR1C38QZZUMJatkKOVrvmPes7chhEZObscDePTxF7Jr+TwefuRRHn3k4Rgiu2HDBmwtzRxW1jGKEXpJaCfiP7zzAAY83H///WzevDnkvDN+woQkMU4iiSh4vV7eeecd3nnnXfTGDIZMuJGswoDuX/jiOw4hy7ER2HZJg/CroMoIAnkc/O1+8AFy6o+4Hwmvn2+e/y11O9Zy3XXXcdpppyXsZ2lpKc89+wwffvgh/37tdXQGA4iOJEmBboXJvQhIUwMja5EyingZPuMhUjK2/2K5hzw5jmcHAh2kOfwHkhQJNNEeHQrM/NYgNIs8aNMUiMSGPZBkgRRFWF2tdbjb6nDaqti94j/IMkw7aipbtmxlz54K7C4DxaPOoXDECeiNKaQbhqCq19FSvZ5REy8nf8SRmNqHvaNPoI5Z9bFR5GgECH5AlywR2UehtRMILUCuhSwhyQFLLIDiwWdgshazfsMb3HDDjbz44gtJi6Uw/PznP+f4448nNTUVSZJQVZWdO3eyYcMGvl2wkM3rX8SaWkJR2c9JzRiMTp9A3hNPGhD9guVzIISG3++PKduXUBSFPXv2IMkGWu3bSc0cFtCwy3S8ELY7VySMziasPDKyG/OSFiZJ6YpkxxDksMl5ibbvjsY5XFMcLqEIJ77BbeIlDQl0JdK6LZ4EI7j/WtTyYJQkp98Q1nvcfPrpp5x11lkMGjQIr9fLn/88i927dnH0L2Z1RGqiEomEHDSC7Wrx74Ob5r/LoLJBPPzQQxgMhlB0es3nr1I25RRWf/YKb7/9NpdccgkNDQ3861//QpIkNm/eQu6AERSPmBxhrdSR2ESlsXwD119/PePGjWPcuHEcccQROJ1OSkv3wgIxiSQOQaxYsYJnn/0H9fV1FPQ/mcKyn6EoxvZcC+33tXhzFtrXRQQRZA0hSwhZoHpUZH3gHiPJKj46orCyLAJzA3wqtVuWULt9Nffddx8TJ07stK9ut5t3332XZcuWYU3PRm9Q0NSgpEoNRYmDkOUO1iEDOr0SiiIH13fszsElYjh0yXEPVADhD7XQ/3QQ5KAsIeSZqonASRg++UcNOEO0Ne2mfPV71O/6PqKNkSNHsmDBQvx+HyOPuZWcAVM6JrDoZWSTjtLDTqWfclpoqDTYp/A+RnZcCYsoh1lSBd8I5WiZReBCCr6JBpOdKDoF4VcDEXBFQtJ3lMnKn0hKRhmrvrqFjz/+ODmZJgySJEUM7SiKwuDBgxk8eDBnnHEG27Zt44knn2LrqqcASEnvR1bhNDJyxiArBmTFgM5gRtZFRTLDyJoQgsaaZexe/RqFBfmdGq73FX75y18we/bbbFr2CKmZZQwYdQWWtNKISXqiPX1zd6PCECfKHI+gxiHawYhxXMKb4MUiov4EpDyebCLwXY74G2+yW0fzUoK/kRZE4dsH21N0kdrlcGu3/uOPIbOojJdffpkfli/nj/fcw/bt21m9ehUAS2Y/QnrRUKyZ+aF2Iwi5Ekncg04akX2TyMnOxmg08uqrr7Zvp2Pdl2+Ftvvo448ZMmQI7773HrsqqkjP74+wZDP5vN8iRIeHc6RESCArerxeb2hJ0oEiiSQi0djYyEsvvczChQtIyxzGqMm/xpzWruENJ8PdsAAVqoi4X0ZA1VA9gX99moamBfiF0p7CvmL1l5SUlnbpUAPw3//+l3fffY/8spGMOOZkDAYFf7uUSjboArIqOqLIWlQAJBhF9vvCA5RBwh7cJv7+yrKcMNi5L3DokuMuEH6Qw38MSZHCvSwIRmeDD9XQSSgroYiyp7WRqo1fUL/tW9paqiPaUXRG/p+98w6Tosra+K9uVXWYBEPOSUByEhQwExTjurrmvJ9Z1zWsec2uuuaEillXxay4a06gAioYEEUlqeQgYRhmprur6tb3x61boWcw6+66c55nnulQqbur6r73Pe95T5M2fVm+vkCHAX+gdc/tyJS1SAzcwlZ/ppVMYTdkdVL8XhxEF/u0xl3GfCkDdtjHyJihswUEaRtbRM9dD98WeLUOwhakRVNadtyGRx99lOeee4Hjjz+W7bbb7lu/38aA7t27c9ONN7B8+XI+//xzpk9/h/fee5RFnz8cW8oglSmny4DDlAOKKcCAuuolrFv5EWuXzGTD6vkMHzGC4449tp4N1y8RO+64IzvssAMffvghd9xxJ59MvZCS8jakS9pRUtGZVh23x05XhAWqUeFH1LI5Ht8lSWgIYGsW2TC/HRR/n+K7RPOQGEMtbBPpeAhb2Tnq629TQDi+n4aKRdT/ZNGd9jaOSyjiAFY/9n2frz6cwuwX7yNXvZaK1l1IlzcHFjDro4/46quv6NWrFxMnTmTt2rVceNHFTH/wYkafeBOmtenJakNgXX9Hle278eEbj7N06VJefe112vUeRmW7bnz62qMMHDiQWbM+pramhksvvRQrlWbUcVfTpsfgkBmqrVrLqi8/wc5WUNm2G1a6hCWz32TZZ+9gCIN58+Zt8rgaozH+V8PzPJ5//nkeeOAfSN+ia+8/0qL9CFXXo5niTbHF3xK+tnmM3YLDTBwS6agscZR1s5Cmwboln7P9iCHfq65o4sSJlLdqz76X3Be6VNgpgWUp6WcBQhYZ6hfp6VCdi80ky6zvrUWv/7viN1uQ9+ZHX7L94G7f6VahgXGia1VYrKcK4BpyftDLffPlTD5+7rLENq1UlpZdtqVV921o0qonIOoN0EDowarAsYlliXqdtBKMk2aEvSht+0NSEYrhiQni4+4bXtSxK3xNSnLrqxCmje/61FYvZennz7Bu5Uf84x8PNEosfkSsW7eOxYsXUygUyOfzrF+/nttvv52ugw+j7WY7IWzByoVvMO/dOwHYaqvhjBu3M0OHDv23HK/jOEyePJmFCxeyaNFivvjiCzwP+oy8kGxZsmV6HCB/X9eJxOux8/t7d95rSNJhfrf+WNhJICwa2N6mWOHiiAPi4uWM77GN9Su+4oNnbmHZ5+8xZMgQevbsyVtvT2XpksW02XxLVnzxHgAVFU0444y/MGjQIL744gvOOutsypq3ZfPt96f7iD0SoDt5fPHPFH030snz4Om7cMhBB7BhwwaefPJJug0bS6a8kvnvPI9lpemyxSi6Dh1LZbtuICxWL/yUxZ9MY/ln77Bu2YLEfvSxdurchf79+jJmzBi6d+/e4HfWGI3xvxra8QFACIvmbbai8+aHIyzBxg1f4rk5rFQZJaUdQsBavW4htRu+plWn7SNv/QZkZGbaBFNgpk2ELryLFevFn2vM8eEzV1AhV3PN1Vc1eLyO4/Doo48y/Z13WfT1VwCceP/bpEvKEjIKP3CkkJ6vXGsCrOEGNVPxIj2Iy7CSsjAdDQFkN7eRSZf+rrEg76fEuo35Bl8vdqvQUey153s+liVQEs9Nz+CyTVqTLqmkpFlnmrTqRVmLzlS2H4gw7CQgts0EsNbvqdS0iGmB6g/OxaHZ7TjLHf8McVZZCCXCF8JQDU+8mNNAltAFQzpe6NChG55I32DWq2eTr10HQGmTdvQcfgrrXv6Is84+hxOOP44BAwbQGN8/KisrE808qqqquP3220mVVZBunlUNG/qN4KtZE3HztfzpTyf9Wychtm0zduzY8PmGDRs48aQ/seiLibTbbNdQk6xZC+nI4Ny0vnfBYbHHsBG7KzXkAb1JH+RYZ7xw2RjojTtNFF9bxcf3bXKJYhBcfBz1JA7CiHzWYxNa6fl8+tqDfPTcXbRq1ZrzzjuPrbbaCsMwaN++Pddeey19dtyfLf/wJ6qWL2LetGe4+OKLuemmm9h888255JKLmTRpEjOfuoH2fYZR3qJd4riSxxTcKQxV8b149jTytdU4+TqmTZvOmWeegeM4PPvss+E6pc3SzHt7EnPeeJz2fbdm7aI51FWvo7y8gqFDt2DwAb+jb9++rFy5kkcffYx8YSPD9tiTo48+qtHdpjEaYxMxYsQILrvsMqqqqliyZAkTJ04kn1uDZZexduXMcLnKVoPo2udITKuEz2ZeDkC6pDlNWzY85ur7qGaO68krtJzSUXhEOhI/Y9K0fT++eH3CJt2MXn/9dR57/HF6b7cru+9zPF0GjiRTprqXCmEgA/mm60A6o2o1XFcBYifvYdlRV9C4W0V4n/xWG0ojed/8lqzezx2/OXA8fvx4xo8fT3WdAseWLRJtWxuKhqonw9SrMAJ5hQpDgJd3FQiwBWUtO7H1H+9t0JZLLV+U9m3AO9WyxCYbQeh1ik8ar4HCJTN+zLHBWYHiyM7NDAbqklLV4MN1vPBE1my5l/Mg5+DkVdOS8vJyzEwFJU3a02+bC/n6039w3nnncf311zeyQz8hmjRpQpeuXalaNYtOW+2MaZlkKGH7E+7itesP4p133mHnnXf+dx9mGBUVFRx5xOHcMn48c6Z9TNtuY+g68LCEnWBcfrRJX+SYvCEunUgU3WmtsTAa1A0X+3xCUjecaKgRu8Gatki8rh/r/3EgLKVsEBCD0gyH6zTIOIvEsZrUv/mvXzafbCbLLTffRCYT+WVrSUL7PsN48bpjqVrxNVYqjeu61NXVATBgwAB69uzJ0cccy4f/nMB2R14M1Jdc5TeuZ8F7L7Lo47dYOX+2smuMxdixY2jVqhVHH300W2+9NV9++SVdu3Zl8803J5/P8+qrr/Lc8y+w47Yj2HHHHenRowemGeVuW7VqRf/+/WmMxmiM7w7LshJ+9W3atOGNNyazeMnXHH/88WyxxRbMmzePm28ez5z3LqWsSdQo7JuV06hsN/jbC/AJ7p1hg6Tk+A+AE8gsHY8mbfogPY/PPvssUZC3Zs0aFi1axCuvvEKHXoPZ/c+Xf/vnCnCFKtZWIFkRjEqT7BRcLESCxDNjxEhUPxWw4A38N4P77LBhwzBNkxNPPJETTzzxW4/rx8ZvDhzrL+vZaV/wu617JVOp30OG0JANiQ437yqgGjTZCAdkYWwSHOv344+Lu2wVV+nHHzdk9aQBRUMss57FmST9AaPlROg36Dpe+BpI0lkLOwDJhlBSkL5jz2DulFuort5AWbqAKBFUdt2csmbnMeO543j88cc54IAD6NKlSyNb9CNj7Jgx3HXX3fSoWkirLv0AEGYrWnYdwNtvT/2PAscAo0aNYvvtt+fBBx/k6acn0W3okaH+3hCRPAcImeFNOVqEqb5vYZnjALOhZeJa+2JHCf2aEYBjW2irtaQUIlyu6HrzZcwr+FtkCxoIF4cQRsh2FK8H0H/sQXz5/uu89957oY4/l8vx6adzaNauG7kNq1j91Wdsu+22tGvXjnQ6nZiMZjIZ/njkEVx33XWs+XoOFa07s8VeJ9K0bVf9CXj5llOpXrmIQYMGsefRR2GaJgMGDMC2bVzXpX379uH2+vTpQ58+fcLnJSUl7Lnnnr9KIWhjNMb/YowaNYpRo0YlXmvdujU9evTgkksuZdGi6eyyyy40b96chx9+BNepxU6XhjVCDd0v45aXofVsA/I03/NJp1uTKW3KRx99FILjSZMmcddddwFgpdLsfsoVWDGCIoGjQpVHVKALKJAckHVWUHys5RaFvMpU20FBX2wziSxiMbGh77EzZsxolFX82DCIDYwx8NoQQG4oDVls0SSlH+oUAYTth8xvWOy2yfRvtIx+3pDGsfhxw+lRo579U/wY9UxND9jxoh8p/ZAlThb3JUXzdlppRn1L0HbA1pS2bsM795/Mxm8WMvulixm8598QaYtOg/dnxvtPMm3aNEaNGs2pp55S73gb47tjt91247XX32D2P69j+6OvobSyFUIYlDZrxdJFs/7dh9dgCCHIZrN4noOPg7BVFsJv4Jw1TKNBEKzfCx83dE0UNd34Nou0eGe66DjryyH063E9sGXXlzUl5UmiwUlwsaY43P63aK7jRXifTX6MdCZDt27d2LBhA9OnT+fll19h0eIl7H7m7axZ9DmgdIpPP/00llX/lr3DDjuQyWSYOnUqU6ZModvQsbTsrNimz6c8ydol87nmmmvo2bPnJo+pMRqjMf6zonXr1vTu3YtFi75m0KBBNG3aFCkfpJBbh50uVSRdkRy0OHwvZuEqkzVVxLJ6TdoO4r33ZvLHP/4RgDfffIv2vYex7eFnU9mmPelsGil97IBw02yuJ4NOCiEZksRYUvhIaWB4PhlhUCh4uI5BRgjVgC2Qr+pQmeu4y07EKjckh/sl4zcLjrWVm+dKhBV1aYGY7lgm9S86EjILopNJ/zhS+mCbsdlM0nc47pEabSf5WvE+NwWMi2djiWMoCr2OLuxTny+QlATtarXPYPRZZeLESzwGLARN23Znm2Pu5oPHL6SiTS+l7UTSadCetOs9jo/+eRZ1dbXf8YM0xqbCNE1OO/UU/nr+BTx/1WFssdfxlLdox1cfvMofjzzy3314iaiqqmLOnDk8+uhjLFgwH9POUlO1iIoWCow15B4R/jXgJ6yj2GllU9p7swjENqTvVf8jZthsMINiFIHiZBFfsaNEfPv6WOPXs37sy6hhj2mZkb1iA8utmD+bee+8jGXZXHLpZaxcsRzfh3a9BrPradfRutvmPH3ZEeEx33HHHRxyyCH1GBPDMBg+fDjjb70VgM4Dt8K2YdlnM3h/0gTGjh3bCIwbozH+y2L+/Pm89tprAKxevZry8vLgHb9+Jm0ToNEwDYzAhSdiZAm7/QLgeTRrO4jP3prMnDlzeO6555g79wu23O88fNGMfJ0EnEim2UB9lCkMPOmH5VnCNJC+ui+HOmPTJC0EluVRyHsJb2QgAZQ3hXl+TfuI3yw41j+blTIxLYHmfD1N4QsDYvoXoJ45dUjnk5RXbLJoLvAd1mC4eDn9WnyfxV3AihmyuIBdvx7fb/HMqnhZKT3QfSPc+sV6DRfxCSwreC34X9GiHdscPSEs3POFofyeC1DavDszZ07l008/pVevXgk9YmN8v+jcuTO333Yr999/Py88eh0APTffnF133fXffGSq7fTEiRN5++1prF69MvGe59ThOBsQaavBph5aLxx3i9AZl4aYgPh5HJdF6GiINbZiFogazMalGAk9sZmUVRR3qYvXJ8QZDSEMTNss2n/keFF8/QlDDQ56WwCWKXA9GS7Xud8g9vjLNaxfsYSadavp16EL3bccTWnT5uF6Y445n1fvuBSAF154gZ49ezJmzBh832f9+vWsWrUK3/fJ5XLq+zcMJt9zMSvnf4KTz7F5r14cdthhxT9pYzRGY/yHx0033UymtD2Z0vY8/PAj3Hjj9aTSGdYue5+SzTsk5AebjHBSHtybi2xrQWGQZh0Hkymp5KyzziKVKaP3qJOp6LgV1etzFPJu2I1TZm1cT5ANnDDqYRzpK5AMiFiziQTYNS1EwCKDuot6sQ570msYyxTXdvzS8ZsFx3qGsSmxt34vAUZNBZgbcqfQDHI4YBe/vwn2Nb7v4jBMA8+RiQG3oe2EFiix/XuODAvroGF9sd5m9DgqMIpPAvTzqLNX1MnGdVXzAGkaCGlA4A2rj3/JZ8+ycv4bgMHZZ5+NaVq0bt2aceN2Zs8992wEyj8gSktLOeGEE9hpp51wHIdevXr9IjruefPmsWLFCurq6vB9H8MwyGazNG3alGbNmlFZWUmhUGDdunUsWbKEe++9j7Xr1tOm1xjatzZY+slz9OnThzlz5tC21xhabT4yOk6v6NoJfLyLtfbFWZPiyVpDz4tDA1yrIZtE6hfMxUGxXnZTDLG+nowYA92QZEMYyfXVR97E5BnIGpZiVAwD6VsMGaUmP8X3CE/6rFo4h/efvReADh060KdPH2697TYee/xxNmzYQM3GjYl1mrRsCxuqaGG7jD3oQPr160ePHj0aawEaozH+C2PFihW06LArTVsOYva0aSxfvpwdd9ieyVNep02XsVjpTXvea2LAdzzMslToJBS+XyQBNa003YYdxboVH9F50H5kKiqReYljuDFcYFDZwkB6iiRIx8mCxH3QT9zPTGHiuDKQWPhh5l2zyFoGKqWPp/FGEW6D+pKLXzp+s+C4OBrSLOpTxS8CoRGILFonNtAVw+dNMcugUsHFIBgInSXCbQb794JK0vhyuqJfP9bdbfR6+gRr6PNGn0ufcMle7cXtZhVQjrYTfn5bIDwfF0IGubS5bgerQbrFsmXLuOeee3jllVc58sgjGDp0aOMA/QPil3L/kFLywAMP8OSTT/6g9Zp17M+wAy/GSpUy4+GTGDx4MB9++CEAyz9/lY5Ddqe0sgPCsutpjkOPzSJgHGeHi0Fporuc2LR+VzPE8ef6fxwM6wmh2n9yGR0aDIeNQIqafVimCEGtft0umpAKY9OauIT0gwakUWa0HIBlwlN/P511K5YAsHTZMpYsWcKwXffH9RyabKhi8612oM1mvVn55Vzmv/82X816l6aVlVx44YUNapMbozEa478jCoUCdXW1mGYEgFOpFPvttx+vv/4Gy798gY699gFiQFgD3hgRaJgGXj453hcDYw04m3cYQovOW0DQktqrddR7JeDkDfKWy7pvailvmlEuYGZ9eQUEQFkkn+v7XQIkxzv4WoRZcg2Q9TpxGWjyk/yy8du9g24CizWUBtAp2OSPrNOrcpMDnno/aakWVojG/sf39W1schwE68I6ve04oC8G7Q0B4W/73A29Hp8dFnf5Km7raEsfKS08R9K23/Z4Xh1rFn7AqvnTqGjShK22HMaUKW+yZOkyLrnkErp224xDDzmYIUOGNDLJ/4ZYvXo1r7/+Om9MnsLSJYtp2q4H7XqPxPNcTCvNnFfvSSyfKmlC/3HHUtaiPVZJM1LZ5kjp8/GzV2EKyWGHHcZXX3/NhqoqPM9jxiN/wZcuFS270G7gLnQcFElBErKhGDAu7jQXAdv6hXP6vPy29GExMNbbMi2BISN9se7YpMFzCIoDFiRxLDEgbOr3jE2AcVG/INwMlvV8n/gNSb9OA6DfC1JepmFw+KW38vHkF+g9Ykfa9+hDIVdHtqyCpfPmcOOxezF/5ltky8qpXreGFi1bMmaHbdlpp50agXFjNMZ/ecyZMweA0vIuOLkqQNV7vPHGG3To0J5FX79Eyw7bkSlrCRQDZA+EkoMa0kAEDTS/y/6twYI+x8PLFb+fI5VS43gmbTVIyhWzhyk7kpw6rkQaChwLYeAGOMO0TIRwkcF9WoH25IZ+TZLtN3sXbVamqucbYpagPjhtCCArYKhkFnGJQ/HJ4MZ0zDL2H5L+xmYMQG+KXdYAuTidrGUUxVEsvWjwRI1FHITEw7Lr653jy0vpY9lWQjzvCg/DNOg6dDe6Dt2NdUu/4KsZT/Ov556jfbt2VFdvAOCrr77ikksuYffdd+fYY4+td0yN8cvFZ599xkUXXUxtbU342vpl86j5ZhEl2SxVVeupbNedyg69Wb3wA6q/WUqhtoqKlm1p3WMgvvSp3VDFe0/8jZXzZnDaaafRvXt3Hrj/fnK5HFOmTGH27Nl07dqV++67jw2v3kaTtt2pbN8biKwFiyUJm3ot/h6o67KYoQ3X3YR5vHaW0MtYdgTKLUvUk0pAwFjHwHD8GjAD5rj4mKNJo/pvb6qRCBGw1jpk/Vg0cLPX63Xt3Zeuvfsy9ZmHeeOhW1nx5Tw816FF+y4AdGjfjiFDhjBixAh69uxZ75pujMZojP/OeP/990lnmpIt68Cyr/9JaVk506dP5623puH7HlJ6rF76Jh267x0Rc0U1H6Gk9FuamOlliyMOtn3Hww/wiWt5yBrJmlXQvFUpliWUjExQjzhI1HNJH8sysCwzrL3QIBkIa5vASkgrXEf5w/vSRwqJm28Exz85quocIDmYfZfHcXFo5lRvZ5PLbQKcflsUs8tSJpnm4u2aDXTYiu/vu0BGUmfcAMj2/NDRAgKDbitpFZPYr2lgYYaTAyl9WnTpTYsuvVn04XAWf/AvYCkAfiDbeOWVVzn00EMpKdm0Vqoxfr6YOXMmF198cfi8Y6fOjNt5J3bYYQcqKiqYMmUKN9xwA56TY+BuxyGsDNKtxXNdSppUhpO8eVMfZ+W8GWy99dbsuOOO4fYymQw777xz6MP8zDOTWL9+HQvefpCtDrniW5vaxGNTE7bi2NRkLy6HaqhbXXw9o4GGHvq/bSWlSg0xyDrsuNWiWb/QNXEMcblFTK4kGkhvNfQZX33wNvx8Hdtttx22bbN06VKcLl3Yaaed2GOPPeot3xiN0Rj/vfH111/z6quvU145IOg+6uEU8rz11tu06/57WnccTW3NYjIlrYBkLZKORKe8Bt7/vhEH3vHsso7vwlTF9zPb1GSjEdZmpISJ60UZPTDBLZJ6AgIRyt5+jfjNgmM9SFqW2WCHPFkkd4CkBllHVKQXrpnYT0MyjYaexx/X65YXH2i/YxvqM20aRDTMbkd2VQn7ugbcACCmfY7pmM2iyn0pfexUBCT0TNNzJd2GjaHLFqN55fpDqF2/is5duvD1V1+Rz+eYN29eojtQY/wy8eKLLzJ+/HgA2rXvwCl/Prlegd9LL71MaYv27HLqHQhbdWcTokliO5YtWPPlRwDMnTuXa6+9FlCNKqT02WyzbvTt25eJjzxKly5dGD58fyZMuIPadUspa94BYkUgDWUkdIgG2Nx4843463FmWK8T32a9rIxl1tMQayY3LpmACAin0iZCqJv5psBu/PW472e8IE9XbttxA31fVXQ3dJ0XDzZCGHTo0YdZb77C66+/Qb/+/dh///3p1q0bjdFwqJoJ2SgvaYz/upg/fz5//ev5ICrpsJlihe10EwqFAgClFZ0xU2nKU1FdSrz7XQIEexLfA0xRj1WOLxtniTcFolXn06ho3yl41NY4mLaJZ9fPhENDUlKQhk/aNrFNgeNJXNfH9WRoBafJNmEaeI6XeA7gFn697Nhv9u6hBzk7LbBSSg4gpJ8AhxDZhsRZWSMYMPVsKWRTPR/LNnGDHy2xv02kjRtaJm7PFvf1K2atioF1Q4N/QxZuVqyoKMmaGQhhhXZ28dfjx6HnZnHwUQy23WBmV2y7YlmCdEZ93/3HHc27j/yNc885hxtvuok5n35KVVUVjfHLhed5nHjiiSxdqlj74cNHcPrppyVaE+sYNGggnz36GCsXfERZ8/Y0ad0pIVWYO/VZKlp1ZOCuRzDnjcfA9/l8aRW+9DBTWZZ99i7vvfduYpu/+92e+L7EzSlJjRmbmJoNMMbqL8nmfpv9WlwuEQ8r5kxhFOmWLUskujtphjgOcoWItMDFE95i5lgPEA1dsxoYF1u55esVwTY8AS5+X0qfQy+8kW0++YB5H7zDrMkvcNppp3H00Uez22678VsPx3GQUpJOpxt83/M86urqmDlzJm+99RbvvfceAM2bt+C+++79WY6hurqaKVOm8Pnnn9O7d2+aNWuG53lstdVW2Lb9s+yjMRpj4cKFnHPOedjpNnQfcDJ2pgwAz8uRSqVp0aIFC2bdxuZDz6K0acd6hfpQ340LiJqAmJGtW7y7r45NdTHV62gQLoVqJlZTnQeie69tCYUHvqUwWUfKDuQVpkRKgef7IVB2TAXC3aBfg+lI/KCrr2X/l8gqrrzySs455xz+/Oc/c8MNNwBwxBFHcP/99yeW23nnnXnxxRfD50uWLGG//fZj7ty5XHTRRZx00knhe126dOHrr79m+vTpDB8+PHz9lFNO4aOPPmLy5Mnf69gi5lhEPb8B6YkQKANIIUMAHAfKxSAZvi39m2wm0pAuuSEpRPx5fJlUygw0Osn9xC1P9HqpQBAf1wI3tB8zljIWqWImLVnJ/22hvwvLMhMgJmKbIxCw2bAdmPH43/nwww85/bTTePvtt9l6662/cx+N8eNj0aJFITA+7LDD+MMf/rDJIoYRI0bwj3/8gzfuOAuATgO3o/qbZXQcsC0lFZW88+h1NOvQk93PupsOfUeE6yktmGTqg5fy9Qev0r7PcKx0GV9/+CoXX3wxzTr0oGWX/vgxx/Y42EsA36IujsU1AsUWbMUscrQdI6El1o1whFB64ThwLQ51bAYIPwTI8etPg9xi6V7xZLXYfui7ALDedrIpT/3HwrTpMWQEvbfcmj2OPpknbvgbt99+O77vs/vuuzf4mf7bY+3atZx/wYUs+vorDCG48IIL6NixIy1btsQwDBYvXsyjjz7K1KlTcV233vp9+vT+Sfv3fZ8nn3ySd959j/nz5iF9n7KmHXnzrbdDmdhJJ530H9favTH+O8P3fe66626E1ZQeA0/FtDIglD+859VSKORZtkzd15ct+BfdBx2n1muIMS7etgbDngwBcnGE68etLuPe9LF7XLxnhFNwqanOYzXNNkgWfFtk01aYRXNdGQLlvKMwWT7WzVdKgSEk0vn1+NwfvacZM2YwYcIEBgwYUO+9cePGce+90ay9eNZ/9tlns+uuu3Lrrbey7777Mm7cuIR9VSaT4ayzzmLKlCk/9vBC1zVhmuFACYQ2IqCApCGNBKMcZ0JDGYRIph+SYNvn28CyjjgzG2/xHB6vEKiK96T/cDxSqchBQ6+jtxkxbvUbH6TSFtrOKv5e3B/229jo4kYkDUUIWNLq0nEdiWmVUdGiLcuXL6dVq1bsvffe37mdxvhp0bVrV5566qnvxWh17NiRgYMGMeujjwBYNOtNADZ+swynUAfA2iVz+eDZOxiy5zFAAPKCa2Tbwy5g28MuQEofz3XoOmxnatYup33frUll1K0l1KoFDXbiE0wpZaJpRz3AW3TDNy0RA8tFeuFgkhiXR4gYy2AHBXfx9YoL6HQUF98WM8ebuunH348DcceVSdDrx9gVmVw3rl8OixkDFlpdhyZ7nXw+CJMJEybQqVOnBu/BP0e4rsvMmTPxfZ++ffvW68z3S0Uul+Oqq6/mm3Ub6DFyd+ZN+xcXXXQRALZt89BDD/GPf/yDD2Z9Qu8xR1BS2Y5M0/bMnfwQyz97kwsuuIBhw4b9qH0XCgU++ugjZs6cyQsvvEDzdsPoPOBgWnQZTqa8Es/JIX2Xj1+8gIULF/6Mn7ox/lfDcRxuuukmZs/+mB4DTsK0Mhi2GYLTkooOAJQ26UJN1VeYdra+NQ7fj9wqXt4wjSQgFslOpnEw7Us/JL/0/cp1JPk6l0Jp9HooUwu7VvuBRWdy/0Kougth+NiBVabjSixLJnCSLt4TwsDN/4fLKjZu3MjBBx/MnXfeyWWXXVbv/XQ6TZs2bTa5/gcffMAdd9xBSUkJv//97/nwww8T4PiYY47h9ttv5/nnn//RHcLiDQIMIRCbYHf1wB2CZBGAZBkxOuHAJurrf/Vj15GxwTypUY5ArAxBr5bDFWstpYw0vp4rsQMWWUecTdYg3XUk2VLlzuEHhXThsYXHFOsiFjt2O2U1sGwy7IYzmuH348v64Fnvo7xFW1atWtXwBhrjF4lNAWPHcXjzzTdZtWoVVVVVbNy4kdkff8zw4cNZu3YdCxcuIJVOU1dbC75PWXkFG6s3kCmrwLJMCnlXMbExqUQ4mbRsOvTZCiAhQ9IhTINC3oukSsIAosmadpQwY61O9XtS+qTSZsJpAtT5n+hiFwO/xTpifXOO/LuDbTQAmhu6xhONPRqQX2yqMEX6fgjMNxXS98kXPMUix6/1mN5ZENyrAhZ77BF/Yspj97Bu3bpNbvenxLJlyzjn3PNYu+ab8LVTTjmF4cOHU1pa+ovsExSDdsGFFzJ/4Zf87qybab3ZQHb447ncedRIQJ3DuVyOdu3aMX36dGa/eFe0smGwyy67sMUWW/yofedyOS666GI+/fQT7FQJnQfsR+dBe2NYJr7vs+jjJ9iw4jOkLFBXvYp27dr9HB+5Mf6HY8OGDVx22d/4Yu48Nut/DJVthwBJ1tZ1qjHNNB03349seTuEsENWGZLOFPGIs8rF7LGWYyBUk6Z4hF1NvwNsS8/HcyW5Ood8nWovLXyFYeIgWRj1G6OBumemUmY4+ZfSD+VvecfDMgNgHNMme/n/cOb4xBNPZLfddmPMmDENguPJkyfTqlUrKisrGTVqFJdddhnNmzcP3+/atSvPPvssu+++O2+88QYHHnhgYv2uXbty3HHHcc455zBu3LgGmNn6kc/nyefz4fNcTnWPKi9NgWWHzEs4oMcAnWZG1XtqX54rE0AZqCe7AELZhdWAzVoc7Db0uFj2IKUaSMMTKWWGoCE6uUTIXsdPtvjJpzWXMhDR6+dx7aVtCZXO8GS9gTtuN1UciQE8PHvMGOBJHkuhtoqKDl0a/M0a49cLz/OYMGECL730Ur33unbtyjbbbMO0adOY9fHH+L5PKlNC237bMGSPoyhr3hZf+qTSVngjTrLADZwnRdeEBrjSi/lpBzd3PUHTIDkOgvX5Cwpg65spUK8pB0Qgtth6LSyus43ETTguadKWcWaMLdb7gfrXQ3GGJa57bmj9YrlFaGekvw+MBHMcX1aH3m7d+jUAVFZW1vvuf45YunQpa9d8ww7HXMPkO/4CwA033MCoUaM49dRTf5F9Tp8+nb///e94nsfWh5xNhz5bqPuZ4bP5yF34YtoLAHz88ccsWry4/gZ8n86dOxNv+vJ9w/d9Lrvsb3z22WcM2PlCmrbri7DUvXb9ys+oXr2Ar95/jP79+9OiRTtWNrN+NAhvjMYANQG98MKLWbuuml5bnkFZRfd6QBUBqxdNxjA85r1/Pd0GHkOztkMDF4v6RXbfp7hOL+97PgYS3zMSYLwYGMdBeJycEKYRyjXrahzSWTsxoYcAU+GTEia6IC8hqzMiq854kXIxa+x6SnPsZhTxs2HDhsTnSafTm6xL+LHxg8HxI488wgcffMCMGTMafH/cuHHsvffedO3alQULFnDuueeyyy67MH369LABxKWXXsq4ceM45JBDOP744xk8eHC97fz1r3/l3nvv5aGHHuLQQw/9zuO64oorErZVOmzLxAq0LU4wE1E/gBnTFotEukBpkM1Qnxz32Ssu0NMgNM5Ex81G4o+N2GBabEhSLHGI64DDkykAB3HwG37Ooor9cKAOTr74SRuegGag+yliysJBPAaE9WfzpA9mBEK+jT1zPUnVqqW02W44jfHrh+d5fP7554E/5tusXbuGdr22wEqXsuzT6YwdO4aysjKee+55NmyoonW3vvTcYX/a9x5G6279QASFncKAGGOsI5JLRL+7vjnboj6QjC8Tj/j5XRxxYKy3E+rli5hifY7r19JBFyftxakBcVxP/F26uOLYFAMcMiWbmFDq60ZbFknph9emdrTQ68fZa72uruaWwYR2zcqVwE8Dx1999RU33zKeJYsXs/XWI9lmm20YPHiwGng1oHc2sufZ9/DN15+wYv4sXn/9NQYNGpSw9Pu54p133sHzPLY78mK6bzUWCAp3rAx7nH4lrbr14q0Hr+eaa66hWbvO7HDEX2jTYwCV7bry1UdTefX2i7j99tu544472GOPPTjqqKN+0P4//vhjzFQJlV37YwapPcM0mDvlVmrWLQPguOOOo1OnTj/vB2+M/7n49NNPufTSy/CNMnpteR7Z8tb1gTGQz62hbuMqzjzzTKZOncrUqeMpr+xKtrwjddWLSWWa0X3wSQk5hI44QG4I+Bb7I4fLeBI/yH4LO7l8Q+yvul9JnCCzmBJmyBrr+1jB8YL7s6gHOvU9L5MyKATuRtL3Qz/kkEE2JYW0Oq6OHTsmtnHhhReG0qufK34QOF68eDF//vOfeeWVVxqsfgc44IADwsf9+/dnwIABbLbZZkyePJnRo0cDMGTIEJYuXUp1dTXNmjVrcDstW7bkL3/5CxdccAH777//dx7bOeecw2mnnRY+n7lgGaOH9CabFlhpE8eLBiMN8nSaUko/7NKiJQJhYw8NQoWBIY0Q1PrSD4yrG+6gVww4XUfG2N76nsObKkTSg7ghBKl0EaQu+vU0OEjHun2lbWWZkjy25PEWg+LwdSOpr0Y0DCbChgZF7xWq1pGr2fitEpvG+GVi0qRJPPzwRGprayht0pyuQ3dk9HZ70axjDybfcxklpSUs/PJLvvj8c3puvSvjdj2cJq06NwhQ9bkUb61cLJkIrRFjj/W1FGdMNyU/AN06OnYdxCahyikmAr/FxXVJmZNynoiuAZOUHV076eCxZRlYMUArfZ/44YWFeAQ3cIN6jHB8ef1+3KnCDe4xruvjeFK1oTb8qF207yMMeP+155l0x43ka2tp2bErZU2b4eNjWiladuxCSdPm5Gpq8CQYpsXsyc9RUlpKy5YtN/l9gpLAzZ8/H9u2adeuHU2bNmXx4sXcedfdfDL7Yypad6bXqD/w7jsv8corr3DKKacwevRohg4dyhZDh/LmvRfRrtdQRh93BV22GMPCGa9xyy3jsSyLYcOGbXIc+KGxatUqPvjgQ3pv/we6bzlWOd54QWYr+B233Otw2vceQiGXo32vIWGhqTANNh+5M1232IHZrzzJ+/+8h0mTJvH2229z/fXXf68JhGEYnHfeuVx+xRV8NfMhNh8VAeueOx7NnBevp13rFo3AuDF+UEgpyefzZLNZQGUo3n77ba677npKm2xGjy3+hJUqrQdifd+npupr1q2YBUC/fv3YZptteP/993nuuef5Yu7HCAOqVi/DsAj1vQ2xxQlsUY+ZFhGwLirW08BYscqB7K0oYx33P87VudhpBUpCdyBRTLYFgNqOscfBtlKmIGUKCp7E9XwsU2X7NKlpmRFzvHjx4kQNxM/NGsMPBMfvv/8+q1atYsiQIeFrnufx5ptvcsstt5DP5+u1B+7WrRstWrRg/vz5ITgGpYvcFDDWcdppp3Hrrbdy6623fuexFdPqTSuVrKIsm0KkbQqOh7Qjbz0NjHWXFj3LcV01uFtWNJjHU6+QlFkYMglqdRRXqptWlGYuBpHF29e+rLpFY2hLF7BMcS2PBsCpGCDWwEGnI9IiqVuO09ZS+njBSVv8OYoZrE19tvh74euGwSeTn8O0LPr161fv/cb45WLy5Mncdddd9Bu1N72335NWXXsjhBmysL22GUdd1Wo2OgX2OvtWOg1QzL4Gs3G21nNURsVOW+EErzRrI4SRkAQA4TUV15CFf4GUKb68btkM4OTdxPUlkOg5nSFEPYlG8TUQnbMKGCvGUR2rZQpsU2BZQVZFL2vEiu0MAINi7C6MAGgbyeWLIy7xkL6PqyfHsQ5QrlQSJje2k7WrVnLmvuNYvWwpgwcPpmPPzVi6dCnrFi4HDPL5PDNfepp8Lke8diGTyXLOOWdvEpx++umn3DL+VpYsXpR4PZ3O4LgOFS3aMfyAPzNgzN4snfMuTVu3Yd70V5k8eTKjRo1CBA4R7777Ltdccy3THr2VYfv8mb0vfoZpD17MVVddRTqT5aADD2DcuHE/qbGP67qc99e/4plp+o7eL0zVhvfLsN22oEOvAUoeF3f48HQqNsWgnfenz477MO3hG/hsytOc/OdTGDF8Kw444IDvHG+22morBgwYwPKqJdgxIqJtr+EIcSrvP3ExV199NcOHD2fkyJH1xrrGaIx4zJo1i3vuuZclS5bwxz8eiWVZ/POfz/H111/SvO0wum9xLKadilaIgdPFnz7Bks+eAZRPvZ7gDR06lNWrV1MoFFiwYAGum6eQW0+mtHm4fugskXcTjDE03BBESyqKNcZxNlrfY6OMm64REQn84uRdLEskuprWk5nJyAs+jrFcKclaJmnTpM6VilgQIIRJ2jYDzbECxxUVFb94gfAPAsejR49m9uzZideOPPJIevXqxVlnndXgzWLJkiWsWbOGtm3b/uCDKysr4/zzz+eiiy5izz33/GHrptSXmLJNzIApilwXJGDgumpAyzseQQuWaKBHYAoCSUWsPXRMPqGjQbat2NPVNDaZytUyCSOQaWhQrGdfYSeZWH/y0qz66UozduK4IAZShIGliwPN6PVwmQAYh8dY9LkS7HecfP4OOZ++EBZ8MJXNe/akadOm375CY/ysMenZZ2nXewu2P+Is7HR089UyoK6DR9J18MjEawAUXb9CGBBcO3ryZZmCTNoiap0cnS/xRhgNdZYUsfbj+r3omrAT51uYcYm1Zo+DYUjKKvTx2GY0UUwHXpoaGGtQbMV0c7Yw8Hw/BMDFoQFxnDWOLxd9nAgYg4ElgmI8/Z34PilTIH1ImVHraCE9Vi9bSpu2bbnkkkvqHwAKDBcKBdLpNIahUpi+738rOHvzzTdZV13L6KMvpGPfQfi+R/WqpaxZ9jWGsBm08x8Qps3HrzzOq3f8DUMIdvzjebx+16XceONNHH/8caTTaYYPH85BBx3Ivffei2XbDNj1aLY/+jo2rlnKF289yX333c/DEx9hxx2254ADDkjUlnzfePfdd1mxfDl7nH0PlW07JRxJGpp8h/e5ogyFntRZlsk2h5zGwHEH8v6/HmDyW68xddp07rxjwneC+A4dOjD7hRdZv3gWzToPCnZo0LrXcHqN/iOzPn2DN998k0MPPZTf//73WJa1SavExvjfjMWLF3PLLbcwZ84cKppvRnnLgdx+++0AVLYeQO+tzqBJ674IUzRorVbIrWXpF/9kn332YciQIVxwwQWcd95f+d3v9mT16tXcfvvtNGnRj6ZtR9Ox2WakS5rVA8YJDfH3jIZZ5/qYRT+Oe84LoRqNuK7SBjumDAFyQ24/nvRJ2UUSDc0jCEHWghrHQ/qx4mkEqW9pgPZzxw8Cx+Xl5fVYwNLSUpo3b06/fv3YuHEjF198Mfvssw9t2rRhwYIFnHnmmXTv3v1H+0Eec8wxXH/99Tz88MNstdVW33u9nKcYr7KsjZVJkXI8cnkXx5OkpE/B8bBNcLzIO1jrkuMgUg3O9bWWQMiqQXQSQTIdHH+ugYBVnL6Isb0lWYt4Rb3u0GWZEWOnB/34cQL1pSKuxCm2lZNJFtg0jBAgF6e8G2KN46/XY8CLCqV6DNuOl+/8O1VVVTRp0oTG+HWiSZMmzJ85k/GHbcVRt71KSRPFmPlSIrWWrAiAAOF5Fp9opWPsbtpWHpT6vEyXmdTUuaGOFqJzu+B4iln2k+dKfAKm96dlDvq8iXsUxx0mtN423JclErpiO/hvmSJ0iLAsg5QpsIRB2lSvpWKDQDETHH8eMcwk2N74ssXFelpGEZdcbCqzAlC5WRdG7bEXaxd/VW+ZcFkhEgzxdxWb+b7P6tWrqWjZlq322DeUktGjdzjB0Mf0wT8fUOtISbNOvdjxqAuZcv8VfDF3Ln85/TQ222wz9tprLwDuf+ABls6ejJnKMGiPExm695/pvcMBzH/nOSa/NYmZ73/AueecTY8ePb71+IqjXbt2GIbBN1/PoV3Pvkojbkc1IYXaKj5541kWfzKDVQs/w3MKSCmxMyX0GL4zfXb8PU1adUC6bpQBdCTp8laM2P90+o89iMfO24f33nuPHXbY4VuP5Y9HHsnSpUt57+ELGX7432narhcApinosc3+bDZ8L57725784x//YOLEieyzzz4ccsghP+jzNsZvN6ZOncrV11yD57p07n8A7XvtjmEY5GvXYQiLVKYcoL6MQvohqyvr8vjSo1u3brzyyiuYdilfLanm0ksvBaBN17F0HXiImpQ1UDyni6bNEkUQSkeGtSAhSxw4VYSMsS5QtkUCEIdAODFeFDdZMhMmAa4jkbbKysfHD7VNRbQUdx6V0gczwiIpU+BIH2HECD0D7AZ02b9U/Ky+GKZp8vHHH3P//fezfv162rVrx0477cSll176ozUhtm1z6aWXctBBB/2g9QSRjqUkY2FZSm+sjaXTthlaKLkBIPZ8n5RtqrRdADDTthkWzLhF2l1isodNFfbEC4V0WhcUuCgGmZYpyKZjlft6oA+YrziDpVK3AAZSxgp94q4cfuQfCPW1n14DjEw9yYT06wMIsz4oiK+jv/tOfQbjeR5r165tBMe/ULiuy+TJk3nttddYuXIlpWXlfPVl5L+qGxbEXUsg0vdapqin3417AmvZji6OaFKmmGh9vlVWpMPrxXUljqcmnhDLOIj65x6om2Q2bZEObqpaG28XAfS4X7HrSrzgvBbCIJu2QjZbXyep4EZvCQPTUIyx/oNASy+i68kOHlubuIaLIw56LaGkGBoQ623Erw87YD9jSgBMg/B5NptlzZq1P8plITwmKVmwYAGLFi3irbff5v2ZM9nx0JND9iY+IY/fD7oMGsna5Y+SLW9Kp1696bB5H9p278PL48/j1FNPY/vtt6Nr166MHj2a/v3788orr/DCCy8w5a6z2PrQ8+gyeEe22PMoug3bief+fggvvPDCd4Lj4s/ZqVMnOnbsyPSJ1zDjyZsZtMvBNGnVlmx5E9Yv/5oZk+7Dd1169erFuNE7kM1mMU2T1atX8/obT/HBcw/QofdQOg7chg59t8LOlLB++RI8z8NzPea+9RTA97Kgs22bc885h/MvuIB3HzyXbf94LWWtukULmJFNYrOug3nqqafp1q0bI0eO/O4fqTH+a8L3/R+VEbjxppup7LgF3bc8klRJM/yCuhdmyps16CsMgJTqdVvJHrLlrRGmxdVXXw1Ak1a9GDD2QjZ8M48NK+bTtsdYhG0mLNz0tos75RmmgUAkuuIBITCOa5ANYYTOQRoMF3fwtWJgVzdYM4QRWm2agZwt/Gi+H+KBdOBElE1bCWyjtqXuv3bsXi0Mg5zr4Ug/vMfav2KWxvD9BmiN30B8umQl/Tq24ZGPFlJSrmZrljDIuUrs7XoytDLTliFxr1EpfZyiSnyor7fVJ4ptGqroT6sYYuAiXiinwypKOejtJZYxk4M4UI+N0vrGghNZnjR0vHFLlDhQieuNv61YSh9f8XYbmhTo7+/LWe9y39lHcMstt9C5c+dv3XZj/PCYP39+g7ZaOxxxBna2jM79t6SsuSqG1Dc6PZNX0oNIn1twvLAzUbxJRZw1toKJlmZ3dWhwnHe8MBMhJeTyrpIsEbP3i53v6ZRJacZKFMvpY40/1hM8rbeHaEJaXpJKsMO2UI8T11VMLxw9joBrQ683FLoNq2k46rmhnvu+QGLi+/WBra76Tmj2/YiNlj689M9J/PmwA7nzzjt/dPHqxIkTefjhhwFo3q4Te550Lj232jFRSxDqv4N73LSn7uO1e9QAbGeyHHrVI5S16IgwDZx8gVkvPcpnk5+mavUymjdrxq67jGOPPfZg1qxZPPrYY3z+2WdkypowcNej6TxkJx47eydOPvlkxo4dmzi2devW4TgON998M4uXLGXtmjVUNmtO+3Zt+b//+yOXX34Fq1atTH7XQuBLiWlZ7Lbrruy3334NTrBzuRxTp07ljTcm8+mcT3Edp94y7dp3YO/f78VOO+30vQFPbW0tZ599Dt9U5xmw+yk07zIAIzjn3nnoQpZ/Po12fbZhxRfvID2XCRMmNPoe/wbi66+/5umnn+btqVM5YP/9+cMf/vC913311Ve56aab6LnDUXQYsDvSkcicqwCslmbG7DA1U+x7fsLBx/c8Pn75YmrXLqBly1YsW7aUob+/gdLK9uEyMq+6QtYD27o5iJQNd8/TqbjA39gIQLZhKqCcKU2RSkVNw+Ke9maRjljZbprYaZNUSslXtdWmzoZHWT1DERiWoCSQggIhOE7ZqhgvawkylgglaK6UFDw/xDkbqqrYd2A3qqqq/rM0x/9NodvA6gIYYYD0FdhUzJJijgu6QM/3qcu7AfsV9PG2IvZHg4I4+CzWQKZixXs6xauX0XZqoECvMJS2pjjlqpkozWg1FI5UukaIKkD1gB5niPSxxoExREBFay21dFHPIh1PYhpJsF8/GgbF+nuR0mfxFx9jmuaP0iE2xndHHBiXNWtFty22Zdhef6SyTYfw9ZStihl0d0XNysY9rwEyaYtMOm7NExRJBOeMMJKgFKLfuxCTF3mBM4PephAG+YKHZYvwONJ2ZPyuJ47xNFtcVgEksjc6lLTIIGMJTEP91+yvnlBaIrpuzQZAbxJAN6w5NmKyJNNwEoC4GAzr9/T7arsuvi/Q45OPCO5FEeu89OuvsFOpH32znzx5MhMfeYR+2+3M3n+5goqK8vB7LQSZMqeoRbUnfcqatQLg/PPPZ/yttzHtibsZe8wFeFIVwQzc+UAG7nwg61csYtrEG7n/gQf4/PPPOfbYY9ly2DA+/+wzchurePexa/jm608xhGDu3LnsuOOOWIEV2qpVqzjqqKNIpVKhD/2gPU5k5bwZzJ79Hqecckq9z/PAAw/QpEkT6upUl8ZvY3wzmQyjR49m9OjR5PN5PvvsM1zXpU2bNgghqKuro2vXrj+YkS8pKeHUU0/h5JNPZup9Z7D5jkfSczvlxFTRpivLP5/GmgUz2GyzzRg9asdGR57/8sjlcjzyyCM8/cwzZEqbgV3K/fffz1ZbbUWHDh345ptvcByn3gRISslbb73F1KlTmT59Ou37bkf7AdthZ208W5kASEfiO17E5io9Z9JFImOhK5ALtTVUrfycE044gdGjR3PYYUewcsHrdB9xeCR/yFh4tU6CIfY9H4SJ73oYlqlcLIKIg28za9ezdPM9HyMdXSOl5ekwy1jIe5gBOwyaFIw1cAp86eMNlXT2MZUKMt+B1C0TZMaLsYgQBpaIjjGqDzERhirOs4RP4T9Vc/zfFGZwMyyxBemUSd5L6nMV8Az0d6YfDtRSBj29YwxyXG5Q/DxeZFcoRCAhlTLJpixStgh/6GxRN684EIaIFS5+rl/TukdbqPfyngyX0dppUxih1lNrNOPAIv45itPXxa+nbZH4vMniveT3HWeoNJPYbYvtmPrYnYy/9VbOOvPMhn+oxvjBMXnyZPr06UOvXr1Yunodx97xMhBNjGxLBJIFM6ExLbbccwNrMR2JTEjsvDaFQV3AVOhuR9q30vVkeF64btLgXUolU9Istb5JahcJKf2QjdY6eoBU6CoRvGeKxDXhSJ+0aYTWP5FMIpJIAAH74IcyCw10fUSoCdbbjevldRQzxUA9QCwxEUTXffjY8ML39T51xPXL1dXVPP3wP+jbt++PcnzI5XLcceedDBm1K4dfdAMY6pqtK7g0LUuHmYKaOkdlxxx1n6jLuzRr1wWApk2bkk6nSWVKkr7SjofrSpq26cSup17LJ288y7uP3cgVV1zJggXzSWXLaN93BF/OfAUvX8sWex7Di8/cjmVZHHvssbHvzA+BcUmT5vQcuRuDxh3IfSduA8Auu+zCNttsg2VZtG3bNqzM/6Gd+NLpNIMGDfrB3+GmYu3atQA0adeLph16hfdCw1DfUZcuXbnmmqt/tv01xq8fvu+rWqlLLmX+/AX02uFQMhXN+eDpaxk5ciSPPfYYH340i6r16wAYOHAgu+yyC1tvvTUAd9xxB8899xxN23Zn0F6n02e7PVU75YKLaQs8SyLTPtLxQrYYFEDWgFXLxnxPgV5bNMGyM9TV1ZFKpdhqq2HM/Hh2CGj1elZZKuFIEeqXzaSOOIwAkMZbUwvbVAWtNkjHwwnArutKSrPpQC7hIqWMrDw1cI3Zull2BIAtU4RkjLbR1OSHlD5ukWtRPAuZjXUTTcfu7a5UzHG+ERz/9NBMTco0yFhq9qHBpB4QvcBj1NZVzkKB5JQtEp58EA2acUN+IEz5WrFUrvT9EBhrZksz1g2lbTXTpRnuQqxrnR7E4+BGH3v4fqCNjgPhkMU1jFDzqR/XK6SLMYV63Tgg9jw/ZOLjBXw6NFNYLOGobN+NQbscyOxXH/9JesrGUPHGG28wY8ZM3nrrzfC13591XQJoWpagvCQq3AzlQlJ1KgISgFl3SCw4XphJ0PpdtVwyexJvDFNXcDENI7xOLCvSvwsZtWzW547ebknWDlwlRMjuqpSaiTAgE9yEM5Yg58pwGT2ZdKVPJrhJ2uF7AbA24/ZsbgK4aqBqIDGI2FwAH31+++Fjzw+uSz+tOkmpJs4huNb78REYhmxwX74vgu0Qy2BF1/JTD/+DL+fN5fLLL/9R58ScOXOo3rCB4bvvRzplh9tu1UwBbS0dS6dM0phYpkfB8RDCpryJkpudfvrpAHTdvnVo35cvRP7C+vfute3u2CXNeHX8qZSWllJTs5EmrTux11/vp0nL9qRKyqhes4yXXn6JMWPGsNlmm9GqVSs6dOjAkiVLGHXs1XQaMALTMpGeS7a8EiEd9t577/845tVxHJ577jkyZZUMO/AqUmk7/D56jzqMr99/nlWrVjF9+nRGjBjx7z7cxvgR8cwzz3D/Aw/gOg52uoQdjr2ell378vg5yjxg2rRpZMoqaT9wZ7q03Ix81XpmvTGedeurQnA8ffo7AAw/+BJKK1vjFDyEaVBano48gD2fXE5NsD1H4rleQlIhHamytrbSBvvUgaHOQSkl77//PtlmfRS4FWZCJ2yW2CHghlhjj9j2w/fsQE8cAGytK9bNnrCShdMQ6IrLU+TqlNVmwmveijr4hnVVuumSEUlK9faKtx3+DwgX15PUOF5IZmhSJGsJBYw9Gd73f434zYJjjT+zthl+oZYwKHgSR/pBJSQJCyf9I0SAIKo+11HM5sZZOcvUbar9UEOjBOZJ5ktJKupbREVpYLMIFIOSMSg5hdYqulIBeA1KhKFsVfLSUynxmESkuCivoeI7/b5Oa2sQbMbAfORsUVRkFQNNGqALw6DLoBG8++Sd3HnnnRxzzDGNtkc/MjZu3Mh1110HKD/Wd999l1GH/5lBO+wSpq4yaUulv4KiUteT1NQ5OJ5M/F6WZSSyBhI/9AWOF5A2aFsYTLAAbJR7hW0aWEHKrS7vBTdIo558IhvILLJpM5wspgOtcMYySZmRo4S+Rkptsx67qxldzzcwDR/PV+uo133AT4BVw5Ah4+v7IpQ/eL6egIL0I/mIqpCOiuyia1cvo//HC+/M4LrVy8bdKmJSrMSk1+eb1d+QSqdDZvWHhnax+PStl9hyx9Ehc6OlW6oeITbp1Vpyx6NVxy4M/92hLJrzAf1H/54uW4zCczzstIVp+9RV1+C5Log0IMlVr8FzagCoqakBw6Bjvy3p2KtvmGXb9uDTWLdkHhdddDHXX38dLVq0YMyYMdx33318/eGrdOw7BCtbhpXNMOz3/8ebD1zD2rVr/+PA8fTp05kxYwb9dj2DVNrGjGkvLctkxCGX8d6jl/Hcc883guP/wlixYgV33303bTcfRrctx9G03eY0ad2RXE0e087gOXn6jD6CjlvsTipbjudI1i9TLct79ugebueEE47nlvG38vr44xh1wq2Ut2yHnTLJlqrCZc/1cB2JnTbxXInreLiucmLxPVXEHNcdm2mLhe8+g2nAzjvvzD333ENV1QY6DN0eTKUTTjpPCDytP47LNGKuE1LXfThqX0pfHNV5mHZx9lDd/zW4D2WjQdGdLuxOmAY0YFMbHzvqgu55OpMVl2DEw5U+OVeRmE0CDwdFKqp7V5349fDDbxYca5JSGAZZS4TFeOq3koHMAiBeRa6Ys3SMUrKFEWh8VcSZZ/2eaWh9YcA8m4TWUeHxxABwceW81hbLgLnzfChoY/tQlxwNtnHm1jKNBDDVaXPXiRwE4sB4U64a+n2dUtEsot6fflxcvBe314rPCB1XUnA82vQYxHaHn82/7ruCQqHAn/70p03uvzE2HS+++GL4+N1336WkvAkjfncI5aV2eHMqydhUlqUoeJKaOnXDrMu7yFiNUhy0CtsIdWDZlBVqjLWjiy6UywU3tqxthTe2XN7Fkz6WqX77dFCIoc+x0oyd6HinZRNCQIltYguDEtusV0iXMhU7axoOElPpfIPnGuR6vnIMsAHpW5hGkqmNwK+NaTh4vh0yuAr0ivDaVwA3Aquer7JO+ppvqDhvU9ZsOuOjt1l/meTyAL8/+FDuG38T559/PhMnTqSsrOw7zoQoHMfhttsn0GnzPhx8yplk0xYZWyRSk9L3qRFqwpJ3vHrX/x4n/ZXqmgK2JcJrFhS58Mg5+1G1ehmmncJzHYhbQFo2B109ibLKFoBqEqOaJ5Wx22nX88i5B/DPf/6TI488koEDBwKw4L2XqGjVkW0OOI5saYpFH71FJpsllUrxnxb6dyipVKDdc3RBlYnrejRp2x07naVNm9b/tmNsjB8frVu3ZuTIrXnn3Xdx6mrovdPRWCWtWP7ZVDpvsQudB4+lvGUnhBC4roc0DRZ98CwAr732Gs2aNWPfffdlq622onfv3pxy6mm8+8iljDrhZoSwQmAopU8qbSFMD1eophlpCIGyLX2cvJIvIV3mTrmXr997in33/QPr1q1j0qRJdN/6j7TuPTw8djvIFCrQKylpmqFuQz4Ey6AYX8M08ByJlbVxCm7CF734PqAJMcuKHI002LbTVnhfjzdu0gXemkyJb9PzfbygQFsvq3suxOV/UP8eqzP6da4ka4kQTwkDUr9i9vk3C46tALBmTREwt36oX8m5BnlPgQc9MGYtNWB6wQAp/aggzhZR5ywNnDUzFGeetY5ZhxKRR+/HC4XiFk/xFG14/CJuEaU1ypBzY73Hw/WTs7TQqi02wGvQHJdmAKHmE1TxVtiulVjHPiMq3kMEBYDB/ixLhJohDZi0I0a+zsF1JT23/h1rli7i5ZcfYpdddqF792jm3RjfL373u98xc+ZMPv30UwAOu+hGWrduTmnWpjRjk7IFBUdSVesgpU954HGp7QsLBS+8gZWXpKjLuzQvV1NzDaZqHA8wKaQi3+GCI5G+SctK1f7UDRxcQplFIOfQ4EoXXOgmNfFzXrPEJQE4z8YyOurPwzTyWEYeYQRsSACIpR9YHBouhiFDwBtnhfUy+OAEnSvrpPoedHe6SJYUlzkQft745FdlZzy000VD29ERf62h7FLxvhQzLqhs24HrH3yM4/bejZUrV/LNN9/g+z7t27f/VtD46quvMmHCBHK5HOfe8RCVrduQsUw1MXI8Sm2TEt3AxVQtWTeaMGfmTFavWInnG3QfMhLPrWXBlGf55J23adauI313+B2pknLsbAlVq5cBarIwaswYysvLeeqpp+jYbzgDxx1GSZPmSoMrYp6lpkmTVq3pMngbpk1/h8MOO4w77rgjPO5UJrJC2+vUy3j8ilO44MILmXD77ZQHrkL/CTFw4EBatW7DjIfPoKJlF7qOPJxWPYcmlhFWhvVVVf+mI2yMnxKGYXDqqafw8ssvc+edd/LexItp1rEXyz9/h0wmy7y3HiGVKaXHdgfTfWvlWNFly9+xbvFH1KxbwmOPPcaSJUs4++yzqaio4Kwzz+DMs87i88kTGbzbkeRzDnbKCq3RrEAq5joSp+AFDLJk9fx3qFm7nIp2fZj9z2upWbOUQw89hL333pt58+YB0LLHFpgZM/JBDm4iqWx0LWUr0jh5RWrYscI5K6uAbTqbIR9II8Jx3Y6uWyn9mEtFUC+SNkOdcXlFJiRJIPAqDlhjRZgY5PLqXqnxQAIYm1HmRWMMjU88/JidqBHipoInSQfkX0oUMIRMeNT/0vGbtXJbtXYtrZs3Z9aSlZRXVISDUs71yHs+tY6HK32qCxokR0ytfl5cwa6BcHyZeg0yigZXvXwmsCgBQpYM6ss2IPI/1SJ0PagWPEnBk4lCvIIXMcMFp749neNGVaE6LEuEBVXx1zXYiYPoYk9k2xT10vRSBk4fMSePuppCuK7rSPK1tUy67BA269iGSy65uFFe8SNi+vTpoTb17g8WB+xspPPVGl5LCNKmkShC1breUs0aG4rBTZuCGsdTri3B+SZ9n5rg+hCGQWXGTpyjbiBLAjVZjPTIycyI9AlteeL+labhJ4rcQIFgzRALw0UYDj4mhrp1RsA3CM+3kb6VYIXjx+/FAK+WIcW/i4au4UjGFH2euF9xnGUutleML1fsAa7lDfHt6MeWMPjXxH/wt9NOSny+iooKbrjhBlq0aMH777/PrFmzQs1+XV0dL730EjvvvR/7HXMinXr1xZOSL+d8wqx33mbl0iWsWr6ctatXUrdxI7U1G8lksziFPEtjHtimaeF5LqZl0a1bN+bNnQtA8/ZdOGHCcyyb9wnvv/QkX7z9InUbN4Tr7X/ZgzTr0DMa+IP29TqEMFj6xWzuP+NAdt99d/71r38BcPQtk2jWvitm4K1dkrXIb1jLlYeMZfPum9G1a1fatWvHmDFjyOVyTJo0Cc/zaNWqFWPHjg0dMBYvXsyDDz5I27ZtsSyLAQMGMGDAAH7uWLt2LTNmzGDy5Cl88slsKlp1IV3eCjuToWb1IqpWfUWfvv34+5VX/Oz7boxfL2bMmMETTzzJN2vW8Mcjj+Cbb77hrrvuAqCyXXe2O/ZWNQY6knxtHqeuhg+fOp/q1Qs5++yzQ/3xrbfeyuSp77HHeY+TKbGxg2ycMA2k5+MUXAp5D6fgIT2fDWtXMfmWP+LklStL5y5d+cvpp9GlSxdATYBvvPFGtvm/u6ns0EGN20HRv87wZjI2UqrxXksp9fs6NDDXy3mOduMS2OkYExxjhe2USTprU1qeJpO1SNsmNXXqni0C6V7cgUsXdysCRu23ITvbsKlZLLsV99VPBf0cylMWGUtQYps0SVukRA2m4VBVVU3Ptj0ardx+Skhf/ZCmoVnZYLZlChzphbKGtCmoc71EGjJefBRnjYrtoOKDrH6u96OBcdwCq6HXLKEkFPH967SuWrb4c2mJQySzqPfZY7PDYvG7drPYlJerjnqWdQGTrIGx+tiRkF7vV1+gYVpJdwm0Ugze60+8efe5vPnmm2y//fbfuv/GqB9Dhw6lZ8+eeOmywAVCUF3rhDcdgPKURcoUASCUOJJAWy8oS5lkLDNkcVOmkhrpiZowlF1OreMF57WkbVkmYEsjLbCWEEDSejCaDCqtcPx8VymxgtICB/ZmcdZXa4Q1YwxgxArcDCSun06wxK6fDkGsniR6sWxLBIzrM7pKAqEex90jFFss613n9SfByWyPYodlYrn4hCEOzKN7imrks+WOYzj98mtASpo3b06nzp056eD9uPnmmykrL+etN9+kZbsOQati1Wxpr8P+yF//fi35gsO7b77GtX89m8VfLiCdydCyZUuaN2tGy8pKsi0qyWazVFdX47ouJx1zNF26dKGqqor333+fiooKBg8eTHl5OcceeywrVqxg0Og9EMKgU68BNO/Sm60POp1VC+fw4fMPk6+pol2PPriODD1QtXzGFFEBX6feAxh95Gm8PPE2tUwqTZsu3fGkH7qppG2T0latOfDMy3j+rhtYuf5jVj77LE89/TSWabFy9WqaNm/JmpXLeO7559msWzeqq6v55NNPyVZUMm3aNAA+/fTTXwQcN2vWjJ133pmxY8fy+uuvM2/ePFatWkUut46BW/Smc+dxjXrj30AMGzaMYcOGkcvleOihh3jmmWdo3X0wlR160b7vSFJpCzeQmpkpGyfns3HN1/Tt2zfRMbh379688MIL+NIhnSlR42GgzXWdaDyVno+Uks9euZu0bXHOWRewfPlydtllF2w7YoNffPElmnUcRLayVTC2ClKp5PgOBBrhZC2R1g5LKUNwnkpbCCHJSSeUOGgNvV432RLaCCUWXpEkU9ePaNtkXUStxyFIZqWjYzXqPY66slLvfgka82jSpKgR2y8YvzlwPH78eMaPH4/rqkE2ZdZiGVmkodgnS0BpwHJEbA4hy5ZzJBlbhIOrpvGFEWhh/Nhgb4qQXVPLxGUL0SAYl1ZETQfibJsI9wFawqF00dEAHGejFAgQRn0XC11QBUpvGu9+5gVSCz2bKzhevQG+qFN24mSOALdyqNC2d5qpBsjXOUk2Wvp4rgI5nfpvQ8cBO3LDDTdQXl7OkCFDvuev2higuncNHTqU5158iSYlis0tL7FD6YKWR2gWWE+w0oGWtzJjB5IGF98Hw/BUIZxPAGaV77eerStXCInvW5Ta32DgIQPG1vXTCMMNNb2m4ZASqljLkVkskceV6dDhwTACHbEvkL6NH8ieMKICOw2WlWxCOURoxjgvy9R+ZdSa2ZVusL9iZpiim6v6H7/e1XP9vp/4XyybiNsuFr8eOnj4fqI2AaJMk96vliY5gXA/a5nkXI+KFq3Z9ZA/In2fphmblCk47IQ/cdPfLgageavWXHjjrdTV1dG7X39atG3PjNde5ODRI/lizmdIKRkwYADHXHop/fr1CxnWb4uysjLat2+feO2kk07ir3/9K+VNm1KasZTPuWFgWjZtuvdnl5OvIJ21cV1Jm9blYXbKk36isyeo637MIccyfLc/sGzuJ6xbvYr7zzyE4b87BPBpUllJ5979aNm6DdvssQ8jd9ublG2ydP7n/Ovum/Gkz6H7HkLPLUbw1WezeeLGy1mwdCUIk7q6OroP2pKrn3md/xu++Q+2fPuhIYRgzJgxjBkz5hfdT2P8++Kjjz7i2muvY0P1Bob9/kT6jjkwcl9yJKYlsGyTnHBIZ1vSZvORVG34OtGYxvPUOGeaRliQJ0yDpZ99xMIPp7N26ZdsWLmYQm0VJc3as3LeTE488USGDRtW73h838eTHhh+KFuybIGUSrfsuZJcnYPreliWKgBUsorIFUo/lp4fTmTTWQvLFuRiLHCyFXSsc69p4ORdUoHMLpu2Qull2haUl6bCe6BuFQ2EjZ/UQSQz0nnHC4vBpaEmytoxCTSZEhGIdpBpNAzlEuQHRdTDhg3DNE1OPPFETjzxxB/9u39b/ObAsf6yVqxZQdsWbQEwRQHhCyQeHjYuEUiNNIBKrlAStoT2w2K4ONujBkAZA7RRZX3EQNXXD0etEUXiBNAp3ygV7YeAW7tmyBggj4Nl/bolDHJODKQLA+GrwqtiKzr1fSi9TwIYa6a3AdYZkrKMQiFqLqKXj6d84nIKz/ViM1mD4Qedy7sPXcRll/2NW28d/x9Xpf6fHMuWLeO111+nVfsOgTxBhLKdeMofCECxEUp6ylPKtUX6PkbMeiwlvJCBbZr+JmRrLVEIJQ2+LxBBQVxa1ITMr7I0Ewn5g+8LMuZ6pG9jibrw2JX0IY0wJMLIB8cYbSdkhhE4Xjb0BXZkVnW1lBJwE8yw/pyQfE2HW/RC9B1FryX097Hvsfi9huRPWvIUv4416xyfxIICyvHfxxYGVXkHSwg83yNtCjKWSa3jYRrw1msv0759e4YMGcKLL77ISfvuGe63pLSU2poaBg0axAknnECnTp3YfPPN+alWiQMGDKCktJT1K5cGtQTguqqrVU2dylCUZm0cV5JNm7imgeeLhJtNcefP0rZtaNm6NfddeDKL5nzIojkfJvZ5+aSptO7YKdSqd+nVh5Ouvk3d1ywD1/UxfJ/PZ05XzhlBVK/7hnRG6eDj7F1jNMYPjTVr1vD3v19Fk4492e3cv1LWol3A7ioHidCfN7jZuI6Hlc5i1Khzfe7cuUybNo0pU96kSZvNKBQENdV5SkptZk9+ltfvvIyS0lI6dOjA4M07UFbWi6+/XkS3ESMT3SR936dQKPDJJ5/w0ssvM3/ePAbtdW4ofSjkXdKBztjWdSExq7Z01sJz4x7CMcu34Lp0Ch52yqS0PI0TjNeaMdahm324jiSVUs5HwlB4ojQbFedl0lbYoKkQFPuG2eNA0imEge6jFGaURYRJNKi2rGj/toiwjiN9DMOPupAGhdczZsxolFX82NBepQIPy8jjGwLfdzB8iednAN2e0KfgqR9CyxWAhFxCD4wpUw1wipFLDqoaqMat14oH7jiLHGentDerSilbgC62M0L2LwmY1bGqgVjtQ8T8i6WvLoy4JriYFdZMrz4+S0QVp3FJhXau0FEoRA1GdNe9ePWq9Hxcx8VzZXjBWZYZznazpRlGHHw+j527K9dccw0DBgxg++23b2wv/S0xe/Zsbp8wgUVff02zlq25eMIDtC5Nh6xwsZxAGIT+wJpVBqgNPCSlYWALgRB5xfgaCvCWmGtpSOsrhIuBR8qswZAuvmEhsYPOby7gBgDXBQM830IYDpYRscfSAJN8CKA1S6ylEo7MBsyAIOelwkmiI91QwgFR5ySIZ01UFicXOLPo67Whwrn4esUSp+g6U8tFjjH6utIsdXLC7EovbDgSFd9FBcCu9JFGUp7hSsXW5z2/XmHuUw/ez4fvTOfSSy9l0KBB7LvvvuTzeYQQLFy4kGXLltGlSxcGDx78s2n3fd9nwoQJ1NbU0HPQFpRmbOrybtBIxgqv8UzapLzEDpu76AFPR1y+Fb5mGBzwlwsZuM2O/POO69m4fi2FnJo4ZTO2StGG9854B8bAXSVlJ4Bxn2EjOOeeJ9lYXY1hGPxGy2Ya41eKVatWUVdXi7/8K1Z/9QnN2nUM35NSOT65jkSIAAB6Pk5dFV5dDeeccy6ffDKbbHkzKjv2YZt9TqBlu3KWffEJH/xzAkvnvMeYMWMYNGgQ06dP54svviBfcOi1+eacccZfWLNmDddccy1fzJ2L6xTCc7m8RUf6jvsTmw0bDRDKHJy8F4JlO2WSr3PDAjyAbKmtnKriY39wPaWC9eJ+xcV2rmo/IhyvXVeSzipXI+n7IfMrDNX0zDKVjagm3ApSAe5E47GYBE3ri11Xhoy0rpvReuPoT2f5A0Y+cC76teI3W5C3fM1K2rVow4KVX9CsqRUyXJ5MkZdluNIMC3gc6bMh7ACmBkXth6x/WFsY5D1lLaIHNM3a6fXU/+SArO2pdM9wMwaqNWMGhGlnXWgUT40X6yqlrwoL69yocl6DAiAs0NKWXDo0IJa+kldoUX6xDEIzwp6vnSkI/xcKUSpVFoEQ15Xk6xxydW7oxehLn3Qw28zXOZiWiVNwmTftORa8+y+qVy2hbuN62rZrz0knnvCLaAf/W2PevHk888wzTJs+nd5DhrHvH49l5I6j6NCyeTihihecFbPHymZQbUsYUYtlUFIGS+RJiRoMQ1JhL8cSdRi5ajAtMAQIE6QHpg1uHm0WL0UGHxOTPBIr0TbZldnQag0ibbB6L0g1Bsu7Mh2w1hauTONI9XlqHI9ccG67gQRBX0emoQpWdYFfnSvD8z7neuHn16/FbRPjjHEMu4WTZHVM0TXlSJ8maYucqxwgiov14tvalINF0nXGJ2uZYYc/R/ph4xOAQk01T9x1O/fdfB3bbL11g62Vf6moqanhwIMOol3XHpxx+8M0adlaXc9BirQ2SMOmgqKd8tLISSO8rxQPtCKWyWpAqiL9ZGfEOPEdT7NKHz774D0+f/89Bm27I+03603e8bjrwr8w46VnuOnGG2nbtu0v8K00xv9KrFixgrvuuouZ73/A8Xe/SqasafiePq/12AYw750X+ez1R8lUNKfb0DF0HjKKJpVK3rN07myeuuTIcP1MNkuuLsqiAWRLSjnzjL9w/Q034mLRZau9SWVKqGhWTmW7bmQquyA9H9MSYUe8sHlH0bXlFDxMKwK7qbSpwLypmW4ZvpbO2qH3smWLUAsdl1bYKSuUYQhhkM7apFImpRk77LqqMz1m8FgYRsgeFxwvxBfFE964fZt2sdDMcYltYhrKrEAbGJSnLLJWDl2LsqFqI5u37fqrFOT9ZsHxyrXLaNO8PV+u+pjKJlkMQ+LKFJ6fDgZixVRpEJr3JBsLXoJ9076ldbEfWTtNFFex68fx1/XAXBr4udqx3uFaYA6EjJm2qSp4pSFQiEB6xGBLXxll53UhXAwQCAMKgR7Y8WSCBdZ2XhrcanYnH3xuzQRB0OUuVnxX3B5ab1eDZH1RFPIe+aAbkBW7YKX0KeQ9LFvg5CNNkm17fPzaJD6YNIF8bTUHHnggBx544P+8m8Xnn3/OOeecQ5tOXdh+1z059oxzaFKapdQ2sYWPj0gwpJDUasXPw7hvsMDDMCS2qMMWdZhGnnJzGTh5/I1rlJetZWOYKbBsBZJ9CVYKhIlvptS5iovEQvo2XsxJwvPTeL4CzJZRoNatxMcM7NkUKy0Mh7xXERbX5b1ycl4qtCHTTLFmEEptQVkq0htnLEnOVZ+/zpWJSWO8uY6WOejQrC5EbLArlYvH+pyDMNSEotQ2Q1u0giepyrvhtaxAe7S9hgp6tTyjIY90bQ8JUJYysYSg4DjcefXlPHH3BAqFPHvusQcHHnhg2ODj14qbbrqZKW9OoX23Hhx10ZV07TMYD3Vda0cazfyUZO3w/qHTp4ki3hhT5Ek/UagjfT9WhKOKeuLncLRc8vikr6wFHVfyzbIlnLHbSI488gj22muvX+YLaYz/qaiqquKII45kzBF/YqfDTkhIDB0vkhxGmdLAKSgYD5csmMtL4y9i+byPvzObYaUyuIUcLbr0Y9j+F1BS0RwAO23SrnNT6moK1FRHjYGURFGG8gjLjrTEcSCsw05ZIZAG1YxEF+jZKS2/SOqTtW2b3r5mdg0hsGzlh6+v/1Qq8jhuqPg//t2lU2aCSY47VqSDwvJ4UygFjE1KbZOspbCSKQoIPDZUbaBH256NbhU/JYShqXgXU2iNowUoiQVCsVqYaYShwLEGvdJXFft6sC21zcQgq9mlYkbKEpE+OALZEbOs7atMUcA0nER6WRpmWK3vGSl8kQ7TunEA7koSQB10w4KosxcEF65vIGXUoYyg0tVCoK8DrRfOBcx5OmWqxhFSFQ+ZJuRjlbZpW2CbJo4nEk4VVpBayaYtqgMhvzYNJ2CfMtnodAu1UsJk0M770W/U7/nohQeZOPE2evbsydChSU/R/5UoFAo88cQTPPnkk3Touhn3vfwmTcpKyFpKM2wZ+VAjnDKVZEg3yNCTLVvU4chsuE2t6wVIm9Xqv9iALeoQNavVQtIDQ2BYFqRLFXtsBuBYuup9wHDzGIBnqgK5cB+YFGRpeE4bSPJeOdK3sQPtcc5rGlqvSd8Kj9P101QXnHACKAx9ozQoS+nlFIOQ9zLkXBFOWDVbLAwSMpKcG03A4rUDGUuEoFfvB6BpxqbW8XCksrDbWHBD4A06a6NbXZvhPcCRERhOBwW6qlBX1SWoe4KWW0SFJ5rF932fK047iRefeozf77UXe+yxB82bN/+5T6vvFSef/CdGjdqRq66+mvP2352KymZkSkpp0qIV3QcNY+jY3encewBmMCDqApxUwDK5rvqeP3//HV55+B6WLZxP87btaNe1O6P3O5jOPXoBcS13fWCsf0v9XEc44Q+AyKQ7biRbUsJOO+30q3w3jfHbjyZNmjBu3M688uBtjNn7D7Tu2CW0KItnRbTfr86cVtcUqMn5VK9ZxbK5sxgxYgSnnnoqH374IVdcEdn8te+9BfmNG+i702E069SPFV/MoOPAUYpsEEYIfKX0yZamsCxBbY3CDNJzFMAg5vJgi1AiEXeP0YA3vj3TMlUHPu0qkTZxnRhbbEW2jHod15WhXaP+3FJGdm+u64fdVgHwgmPzCK0ddUMw7WYDUV2CZQpStnZBMshakS+7Ki5P/j6GIRN1LL90/GaZ42/WfU3LZl1YseZtKirKVKFPwBZr6UI8rZtzJdUFNwTE0ldSC52Ojirkk19XKih6gmQ6Vw3EqiVu1opmQKAAiijyedVCc1BFSJo91qy2tnfT8grNeOvQYCFMswc+xFr0rps0aPmDfg8i9le/Frds0xFneYrbSbtu9B5ALu9RrX2OPT+0kgnXcaNCAH3h6mUePvMP9OjYmnPPPfdH/e7/zVFVVcWll13GwgULOfj4kzjh9DMpKy9XXsGBDZrn21hGXgFbw8EWdVhGIZTlmEYe6dthpkQYEu0QkTY3IPwCFOrwc9VQqANhgVsAO63AcUlQfZ2OHAAkFkLmkCKD8Au4lOL66bB4ryBLQy2x51tYokDBK8VHkBI1uH6avFceaoy1y4Vl5MnLMjYWIm3u6toCKVPQPKtcG7Tsw5FZXGmGumLNyG4seKFhfMYyw/M/qQHWE9+k7le7zWhArdhjbckGda6X8IzWoSel8c6RELHWij1Oam4Lngxfi3fH/GTmexy9x1hOOeUURo8e/XOfUj8qPM9j7ty5vP/+++TzedauXcusjz+mav16Wnfqyrl3P07r9h0ANQmPg9snbr+JB6+5jC5du7J5z56sXbuW+fMXUFNby477HES/4dvSqWcv2nfrltAZQ1KbGJequFLdYxxPdfArFFzOHLcFO48ZxeGHH/7v+ZIa4zcZdXV1/Onkk6lo0YprH/snTYIeCfH7RjxDnA8kmGuqcqyvznHv+X9iztsvscsuu9C+fXvuufdefnfiucydOZWdjjkXs7wlNdV58oE8ww3GwtDy1DRo1qosLIST0sfJu4mMrGaMNeCV8eyYEAnZhQbQDY3Den9J+7fkcyDsiqf9iiHqiKplmWlb1GsJrScUYVMQndUTUTFfyjICuVwkp9DESIltYgkPgYcpCqTERmqq19Kp5RaNzPFPCT1wq9lGHkdmQ0ZLhy0khi8D6ykzHCxrHY+MJahIW9Q6HhuDRiEa7Ba85OCnb+rJwVGE7G7GEhiGiykKyKBYyYx1ABOGgxswfdJX7KAUVlA4SFiwA9GgEWeT9TKqKMtA+hKJH1aK6tRm2jaTLWT1VxHX+mEgDcU0Fxwvsp/Sn1VC1CVPpUhMI6ro9QKWujRrJzXPll7fD1NA8S57+v+AnQ/kjXsuZ9myZbRr146rrrqaBQsXMmjgAMaMGUOPHj1+7CnxHx2e53HllVeyavVqHnj+FbYYNoy0GaWUNEubNdeH54+WRthUK+mD44BXQHgeGIaabpm6a5wEz1OgGECYGGXN8AvBTNy0lJQCwEqFWmLDVzZsCAuBi0M5rkwH1jqqcM8wJPiKsS7IUqRv4cismuQZpSGjLTHDx55v4/k2tY7AkUpOURtYC6qbpMT3IeeVhNmQuAY/KlAlsJwLijuC6w30teCH17X6TzDZTcpQCp6kLKW1/mp/Wh+si2LLAxZFFfHKRHv4lCnCegXNRmuQnHNluM/i+8bTjz1CeUUFO+yww08+h36uME2T3r1707t37/A1z/P45JNPuOrqq5l43d84+uKrKC0rV/eBoL3rutUrefj6K/jd737HkUceGbarXbNmDffddx/T/vUELz18DwCDtx9LSVk55U2b0nerbRkwcjuyZWUgfGqqNzD5yUdYtXQJmDb9RmxH90FDkb6J40mmPPUQ1evXNnoMN8bPHtlslnPPOYdzzjmHOy48i7/ddlciQxu3ktSRcz2apC2WpS2OvfJWLthrJFJKXnzpZfpvuxPjDjmKcYccRd7xqNpYCIrpXHwpQ7mhtjy1UxZO3iWTtigvTSk5kyUQwsGyBYW8Gr89XQgrDAxZH9DGZRZawhAfa+M9CCApgYSoHXzodxwDxuG+YuO4riuIF+fq7YaPtZwi1i1P3aNVD4l4A6m4IYIpCmTMDViijryR+8m/8feN3yw4jmQVDqA8U0UgZRAGYdGQ1j1aQvnEaiZJM1S6ECiug8xYRj1AHEVSc1ieEni+T0rkA5YvH7LGmvEDsMxCwKyl8Hxla2UJD+mbCSlFZB8VMdyalbLQ7wtyyEB/TGhLg0lYdZoISQiQQ1u2AERHJ2mgMdYgPba84/ngRRojLbXQNi9AtD1TJLSJutOO7uTXf/SezHjmLu6+5x7O/+tfmTZtKp7nsbaqhueffz7Rkei3FLNnz+aTTz7hH88+z7YjBpGxvkF3gANCICwMN2SOLVGHka+BfA2+W1DSBz/4bj1XySFASSMMAVYKI5VVjwGcPIaVDorvLDBNtY4jEZ6rgLUw8bHAiIrtfIQ2dUFiYRkFlZnxs6FMKGutx5J5vJisQss7at1mCQcKNfkzaJ61w0yNTmjpTIkOM0i9a5CZscwwgxJvLRqxu0ZYlOgF68WfB1lEdc0E17y+fqPmKEkdtwLjIsF8akmAziQpGZaaSJcEHtLxcUMYBlW1OV57YiJ77r5bCCT/U8M0TQYOHMjBBx3EbbfdxrsvPkPXPgPov+0Yth63G9379iOVLUEYghYtWiQ+T/PmzTn99NPxfZ/169fzyCOP8NVXX+OsESz44BtefOgeTMui19ARdOzRm3eef5qaDetp1bo1tbW1PHfPLQB0H7I1Zc1b8dErT9Ola1d69uz57/o6GuM3HJ06dWLAgAF8NfdzWmRVbZDOernSCplkIMhYCWodjzYVaQzpUair4bXXXkf6kkMvuSEEpOWlKdVVzjKoDYiqXJ1LCi2VsBM1P1nDCl0lNFllWZpxlvUYWh31gHKRFjgOWOPbMYTAFBGbnPA8DgBwvKAu2l8AdIWyGIgvE2ej46/FWWPdd0LfHyOgHMkGLVGHbdSRNmt+4q/7/eM3DI7t4L8XNSHwXTzDCgd2n0iLqRll3UFPMcRqVpgyRWI2o0FznAWKImKOLaH0l64ncEUaUzqkrI2A0kJbIo+hOF4APGx1HL6yoBN4WMIKL0TTAJfiqnsj1FRCABokYVpZkV0KfOYdLxTUx8Fw6EWo2z7qi1HGdJvBheJ5kSOGEBFYhthFGdt+WEEb81wuvlD0RedJn2xJCdscfAov3HQOs2bNYsiQISxa77P1YZfzz7/tzcyZM/+jwXEul+Odd95h2bJl+L5PixYtqKmpYf369QwYMIAhQ4bQkB/tokWLsCyL7XfYkoxVBcQmbkY+PFdEYGdjiToMNw9eAd/JRcBYyuB/4DKh9cK2BYYRAWMNogOtMb4EkY5e94Pt+AaCAr5hoToUuRS8UjwsBDIsxjOQ2Eadki0FNnBZay0FT8kzdCGsjwjAcjbozmdQXZCBrt+nIuWGADzexU4YRtA2Pel4oG0PNYOsiuSK2AshQnAan9Tq0BIlLYXSE08th5Cxwj7pq0K62sC9It4GPr6OnsBqV5HoeNVrGwselmVh2XaiK9Z/euy6667079+fzz77jA8//JBXHridSbdfS7+ttqZ567a4rkOzZs0aXNcwDCorKzn++OMTry9fvpyZM2fy7nvvMXXSI4wcMYJ9992X1q1b4zgOV1xxBbZtUyjkWfflJ4wbN45DDjnkF/2cuVyOd999l2HDhiGlpKys7BfdX2P858Rdd93FO++8w/8dfzS2qAulYYaUWIYIs9AqC6zqGzKWYGMBSkrS/O3Rf/HUhFsYd+hRdO49QOl3ieoNVP8BVciaSpvkpVS1OSiAChHbq54KHCFDHa9mfrUc0XOiXgIa7BYzuOF2keFjX8oEUPaD44gzxcKOtNXFEbdctG3VUtoUyXudXi68BwbA2DKjArziLB4E999Y8bhlFLBEAUHhx/2oPyJ+s+DYkGqGYZJHGBnlHmwIfKlgJwRMli+C4VwxxAbRQG0LNSuMt9jVzTzyMTar2N5JW05lLcX4YWVDpk+nw3V62fTrwBD4hsCQinXz0Cy1q34gMx0O0PHWipF2EQpeNICH7G4gv8CSCGFRl/fC9q0QaY21V7HrBq0mtSYz1mtdd7QJI050mdHs1QvaSmsdsusqiYdmh+3YbFR31gpN1gMLuUGjd+ejFyYy4Y472WLIYGbNfhW89fQdfQBTXn6A/fff/9/WPGTNmjXMnj2bb775hm7dulFZWcnGjRvp27cvQggmTpzIU089RavWrQBYtXIVpWVllJaV8/TTT7PlllvSvHlzpkyZwua9enHmGWdQVlZG586dcV2XuZ98yMDBvcPiAxFIgbSzhAheN7wCFGrBCSqa46BWesHsRQbA11O6YsAPZBaGoW+QbiSncAvKlcIQUWogYJ29WLc7W9RhBFp9MwDMQOi4os9vV6bI2NXUes3wpYlPFlcqWzcDSZmtJCNp0wr0yjYFmQpbX8cBb96TgfuDOqyk17CaOJanrFghbKT11ctrqZMGv9rKMWUKPF+tr/cjfeWEEWWGRJi10TKOeOMfVQzrh6xyXKeo5RXKecOkKu+Gk+tOm3Vn4cIvf5Fz9ZeKjh070rFjR3baaSdc1+Xdd9/liSef5MPPP+Wkk05iu+22+0Hba9u2LXvssQd77LFHvfds2+b0009n0qRJ7Lnnnr8KSL355lt47bXX8DwX07SQ0uOyyy5rtJn8H4nu3bsDULNhFRkWk7FUHZDu8JkK2Etdt2SLLDWOT0XaJu/5dOjem7NvvF1Nuj2fmpxDXd5VzK8pVKGbqRrq1OXdUGagI8xQuZLSrBXIFu3QGk2Nn1bUcEcYoWOFBtmQbBCi2WCI7NvswI6t2IJR/5e+H8oyiwGuHtvjBXkatGfTVkJ/HBbimSKhMY7LKCBy+NISOVMUKLHWYhuBs5Jfh+E1yip+egStHPEchFenAJxvIxGJ4jctazCIWteCGQ6uOg2rvVVBva79STXjZAuBI7X/sUHWgpRZE3QM2xCCG9PIR+DG9xUgMS0MYSoxvZEOgZGUJgIXgUnKNFHNPSK2Kg7aM5ZiutV5rtiuvJZbCIEwfETGoCbnhjZuZjCLS3SzQV2UukLXiU0CgHoXspZIaIBrmwLHk6HNjRAGKWHGLhojLBbU64kAUFuhQZhg9NHn8OBZB5HP5yjkNvLKTScx5sS/M3/6v7j22uu46qq//6p2b7lcjltuuYUpU6YAUFqapaYmqpwdOHAAo0eP4eWXXwbgmZceo8fm3XFdF2FmcKXFM0++wClH/x/l5RXsd9jhPP3II1x8ySVceskl9OnTh6aVTXn8oYcYMew0JNFMX7dlVtmPOvAk1FWD9PB9qc4jUOeSF5NWuI6yY0tllcwCwv++tmwTwS3AMJSMwpeRU0UsLGrwhYUXK2KF6LqxRD50hdHNQ6Rv4/hZxXybSgqi15NBxzwnkGq4Mrom44VYOvS1F7Vfj1xc6hfMRCyE1h9nLCvMDulMTco0SJuSvAflKTO4dqKMUFz3pq5xfQw+ec8P2WK1bHT8cYs9paE2KE9Z5Fwv9EsutwX33nAVn334Pscccwz/rWFZFltvvfUvms1ZuHAhEydOZM6cOVx22WW4rstpp53OiBHDOfDAA3/2/c2e/TGe5zJgz3Mw5VoWzXqdu+6+mxuuv77BrE9j/LZi1KhRLFiwgEcemsT4G4+kPFWGIRQpoIv5HZnFw0MpzAQZK0PekzRJW6RNg+qCR8GJ5IUKEAvyjhfT/xqUikhKEfrTuxJTGJRmLTLpoCOd4yn5gh/0DBBe6BbjSR/XimSfhdg+dBQ3A4m/Vswyx33JdU+EevIIQeh5bJmqGM8O/mttMcRs28ykBFWTA1q+ZsXu79L3SYnasOhcZU098Byoq/2Zf+1Nx28WHIenRa4GKkpDLSRE6WpdcKQHbGG4SN8KHFMMLOEHxXci/KI0II4X+ehzq0x4+L6hmDVDKn2xcEK2Ly1qlMRDA5o4YPFcRCooxjMspGGpx1iB7VtFTPsc7V9bRkFUfa/1k5rZ0iekJQxKMxa5oG2k48qQzU3bZuBQEWmS4q0g9ewv7mwBJC4s/brunW6aiknWINk2VaooURSIuoDyMkgHSSXJaNW1N7ucchPvPHoDsJLqtat48YZT6b/Tgbz35G288847v0pBjpSSDz74gH/84wFWrFjGhJuPZ+99tqd5i6bMmrOEDdUea9YVOPnEv3HdddcF34kAWaMubtsC6hCGzR/2HcuoMZ+TyWawU6Xsc+AB7DduHI888ghHHHEEY8eM5f67J3L6ab+jW9dW4TEYeAhc1YjDc5UnsVdQjwt1YbEdvowkElr3kiuAFYBkETDEvq/AsJT4soChm36YltIe622Aeg74Ql8jDnZwLXnhdaMzFkrb74WMsoPnW3i+FQJmbeUWB9gCD9PQ7aiTAFc30dARv4/r89o0ZLhukfwuxiK70XUfyKwEHp5vhxPMlMjjiojZ1nKM+PWuWWP9mdU+VIdC/T5EBXrNMmbIuNcEy1R/s5LjjzyYj2bO4OCDD26QMW2MKPr06QPArFmz2LBhA7Nnz+bLLxfSpMkvU60+EDejXgABAABJREFUZMgQXpsylV5bj6asooSug4bx1GVH8dprryXa/TbGbzOEEAgh6NChJc1KVSMky6zDNwSmn8fz09iiDlemycty5SAkvDDjrO9d63wH142ysK4nw3oeT/pkULU4rmmQdySCZOGbjJFYerw1M2rZOEmVso3QflU36HFciRszH9D7j9dJSJFs0FEcthCJfgj1vyd1rOmUmQDEEDlTxN1s9H7NIlIrMi5Q2ThNYpiGg22oLKSQOYXlNnEsv0T8ZsExXuCvID0FIiw16MU9XyHoUmdIpEwH4DgqBNLpV50W1QyVZqwyliBtVitAbeRD/2LpCyyh0tiqAM8NfuhATF5XFWk/deGTIcBzsMwaPMPCIBtW/wNBKqcUV+pCI+3Z6if8U5WtVdTCWretVZ9JfY6StBmCZidoCOJIWQ/o6ohfrB7RDFTGgHKokTKiylnXkyBVnZdliVB3HM5K48ygaSinC0uEuqn+221Pt0GDeejMg6gsMVm0eDGfvPIIXQZvy1VXX82JJ5zAyJEjKSkp+SlnyiZj1apVXP63y1iw8Eu2GNiFp++7gAEDNoNMORgGg/p3QRoppG8zZtwr1NU5WHYJpmkGzihOmJnwfRfXT9O8WSYAiC6DtxjEmF125sOPPuIIYN999+XVV19hwviHuebqY9VBeA44uRD8hvriumqoqwPLBMuKiu5MU/1BBHI9T7HIGjxrIGzG5BMx5jM8J0Fpln0fw7TxDYGQOXwzhcQGX7HEnm8GnzF+XQWpMVx8QyCJTU5RUqagw3ukufdthCEwA4cZ3xfI0CLOCLtLamArA8Ct1zcNn7SptNkaeHu+HTZA0YWBrp9W+4dQy20J1eraDn+vyINcGNp+TqB9y7WmvybwQ/9k+QYGtW+CMAib/ihphk/K9KlzJJMef5S5s2fx0lOPI12Hyy+/nP79+//o8/N/JUzTZO+99+app57ijECGBFBaWvoda/642GWXXXjuuedY+flbVG67K10GDqXP9rtz55130b9//3+bpKsxfvnwfZ8nnniCyZPf4ID9tlbZOEOA52IIgSHMsJ7GM2wsUYcl04rMEhnKUxZ1gfd6iW3imj65AMzW5RW+qCzPkHc8Co6HKQzqcBNjqher13FcqaSHaSO0MhRCAWQ9ZgJksUJtsDBUvwEtUwRCtypPqqm6GWwbkl3rirXFqZhMw7Ji4LeBrK1lClVwaCqtdNywII5pdTFzxlJ+xroAWhhR59astZ6UqCHNOnA9yNXg56rxazb+hF/3h8VvFxzHTXqF8nlVKRFbCepjs6q4c0UkCo9YKJ0e1eAyJQqhV7GPSYm5NpRQAKFYPyygMqQqntIXmpOPwLEGMEZAseJgW3V4fpqCLA1TOQYSXwh8q5Raxwsq7g1Spqq0L3iRl6u2w9IsliMjzWWkyTTIpAxSfsTWua6Pgwzt3OIXSjhzFUYizQKEM9C4TYz62qOZsC4EzEsvLADUdnN6FkugTwYoL0lRve4b7jxuHIW6GnY/5BDGZLPceeedbDF6HBVNyrnxxht55NHHmHD7bT97tb/jOJx//nmkjAJTHvsT22zdFyMdgHAnB2RAmKGNWUrksEpsPFwEhVAWEaYwDLDI4xsOpp8PrfsWzl9Im9atAWUj1LVrN2bM+Fwxwm4BPAffC1hjrS92C+C66m4iJRQKMZCrXClCgBxngn0/BKRam2xotjhWfAkoUBy6XNjgRxkPQ7oYRjQBVBIKkShwjXfNk7F20gYeRrCcbgaiQ7dP93wDP/h+9MTSNAg7/MWXN4MCRd0IJQK1TuDFLEOAHF9HkjxflHZQyT4kiu3VAF34blBoaCMMM2zXHdf+b92lGdL3WVlToHnW4JlZK9ipTxvW1BUotU1OP+pIXpv0BG3atqVbl84cf9xx/7ZmH/+Nccghh/DUU0+xbNmy8LWpU6fyxhtvsOOOO/6s++rcuTODBw/hw3/dSYsOHWkyZEt2OuZc7v5kJsefcAL9+/fnsEMPDbWpjfHbiddff50HHniAffYayTl/2hXfzWPEAWO2XMk0her6CaquSPdRyFo+wghkC4GTlCv9UGIhpU91rbJz08AzDkB1aH1yVssqhIHpB0DTjFwnrGCbqteW6k1gxwqSZcxmVTcDE0JJL9IpU/U+iMsmaMAWLuhtEAfw+vjTQV2SfqwBdCR5izLX2stYd8PTf0q66mOJHJaRJ21WkzaryRhrFVvs5PGdvBp3ZbI/xC8Zv11wrMMQIStWzCZBpJnUlfN69uJKCKQ5eH5koVZqVVFmrwqLowwkFjWKnQuBtcD0nHDGqXbuKvbONJW3rJ1Wz6WLkS6FlLaPAOEXVNrGT+PKlNLbGISFAK5ZEsorlPwDhGGGxUcZS8srfCzhk4ppMh2Z9FpV6wSpFmGQFmYoo/CCIjz9Hmg9chIsAwkdc9hSmkiQH6ZzwgsuYLqDWavWSYHEDoCdU1ZB09btWfXVXDZs2MCWW27JNttswz9vuYwTr72bsfsdxLUnHMKkSZPYe++9f/q5EovPP/+cZctW8M5Dx7LloI5RoVs8pAfCCphMNbHRYNgMJl+GkAHgknh+OlxVF7GNGTeG228cz8qVK2ndujWjRo3immuu4bPZ8zClw1vT5uB7yhPz//YfHjAXlrpy49kHKaMJoVuAGOgMWWC9nGWH7iR+IOkxTEu9piUXoO6KwopAud6WbwTHr726vQA0KiCsAbLK0pgJCUaoRy7S/Wu5hfpu1P41A+wjVOYl1h1JVzLHiwGDlcLiPgisHINrWjcA8hFhRkcfixXopXURJAS6aNKYOOH2LOFhCbWPGoeg2jqyeKtIW3y53mFUr1asyznUOh4TH3yY1yY9wemnn/4f5Wf83xS2bXP++edz6aWX0nPYthxy6e08edU5jB9/K927d6djx44/6/6OPvoo/v73q/jXtaew//m3sNmgrTj+5seY/sIk5rwxib+ccQZ/PPJIxowZQzabZfbs2bz22mvMm7eAfv36cNRRR5FKpX7WY2qMXzbWrFnDXXfdyaGHjOH+G48ICQffUUVghp1RAE3bYpoSw/dCGaWBpNZtFtjCqhoG3YmzOrhPgSKT4t1ls2krtDiN1+7E7dQ0WBZpg7zjUZK1Y57E6jjDNu5uVAekn2uGWavt0rZNTc4NGebIOSpghc0oE6yPM971Th+fEIqlDtcP2OK0qSxsdY1IJmCItWWuAsYeAmWVZxiSlNhIyqwhLWqUd3+uOgLG+ZoYvvp14jfXIW/8+PGMHz8ez80xd96XVC1/iIpWrfGMLHmvnJxXHrJ2wnDDAdOTqbB7l2LCovS/afj4CLLmesrtFWSs9cpf1rSUY0CxRtMNmDzNvEk30oT6Uv3YEK1jCOU/K0zF+lkppJkl5zUNmiqIEFQYSAqylLyXCZsRQNQ9TzPDCdY3NngXv5cPZrbxIqLi9YCwQ1W8i15D/orhLFUmgXBYdCAjkb8GzeF2ZTTLrQsarxTqannh7ht45+n7ARg7diyLFi9m3rx5XP3M60yf9AhP3HUb/fr355yzz/7ZuuZ88MEHXHjhhXw56SQ692gPpaWqsM1Oq8mMaannhsAzsqFcR/9WxaFcSKzQ9UF3raveWMfWA7ejY4eOnH/++TiOw957780tfz+MP5/7IF6sIPKgvYby4E1HBBuU4AS2NkHrz9Cr2PPU+SdlYOUmI6AbZ9iFFb0uzLAYzzCEKsgTArSThR8dRwjK9TkuTPU9kE7oiuOfVxey6PBibLJex5VmwDi4ISsTZ3k1S6wGomiiEv++NRsdSi4CJlkD67h1omnkQ520zgLFpSEhixyTYjkyG9q4qc8RFevVOJJax6MibeFKnyUb6njsrrt59tYrGdS/H+eee079E60xvnf4vs/Jfz6FVLM2HHf9A+Rra7ju6N9TIiQ33njDz549WrduHZdceinz583jTxOeoX23HgjT5pt1G3n5zr/z0YuPIkyTli1asnLlCtIlTcjXVgE0ToT+C+PKK69k/vzPmPPOlVSWpdW9Uxcmew5YaQwrFRQ5l4Bpk5flYYdQ6VvkvArq3DJyrgy71tY6HgVPxrrYkigkjmSGhMXxQCiLcAPpo2JmBYWClxhb49aoBSfouCeTHXC104Ueh7V8Me+o4nnTMMKGX0KoDnbKjUKEhBcQHkOxX7GuexKGIgwiSQWhPWbaFKrrXUyCqjODIqhRsY06UmaNMiyo3YCfrwkYYw+ESdXGPJU9T6Znz56YpsmJJ57IiSee+IucD7855lh/WVWrP6RpqyFRWpko1SsMFycAyCmzBtdNIzFDqYUt6hJMlLanKrHWkjVWQW2NAhxmWRIYa22nIaL/GhDHiqSMVFY1bXDdAEhb+JatMt7SBReEMEmJGjzfQhhmeLyun8Yy8himpGCUxor0dLWqCAAv4cy1oYgXL8U7++nQmuQwdWwZiIBVhogp9oKLN+FraCqZhNY46RRQ6MGIQcqOZrWmH13oWguVTVnBDLWMvf90HsN23Z+3HruTyVOep0WL5mQzWab983FOvuhyOrZry/WXXMC6det+NnDcokULAN6fs4zOm7VVQNQOdOtuXnlFOnmwUphmUETp24BQBXSxbow+QotVIr2aTGMaLs0q4O/Xn8dh+5/Ec889F7YQrihLc8Dvh7NhYx1lWZuJT7/HilUb1Pmi7qLJAxYCzeSGwFgIMAO2WjPCWnusXSpEDPwG0h7fj7HJEE3y9ON4xX78dzecQDYhcUlj+i4I1TzEDN7TQFaDVCVlUmbvaUG9LpY+AitkqGXYRMc0XOU84+trW/koawCu21NrfbGIMTcGEbgOu/cF4N0IiwvVoOjILLq9vLJK1B3+ojbuQMiKlNgm63MOK6vzTHr4ER696ly23357jj766OJTrDF+YBiGwQH778eVV17J1x9No8ewbTng3Ku5+fh9eP7553/2wsbKykrOPussjjrqKJ6+9jyWLfiCQ869gn47/o6DzryUnQ8+mo+mTmbZ/E8ZcchwXprwt3Ddfz33HOXl5bRp04a2bds2ulz8h8fMmTOZOnUqD088PwLGRQ4/mDa+W8Cw0+G4njJrEIYk55VDUDyfMWuRvpLgadJKR/x5vPOe9H1cw1fFgNqtx4t8iFO2EUoiRDrZdj2endXkEiQt4Wyd4UpDvhB4tBdZtWaDDqAaEGdLG858CMMItcVW0PTIFpEtm5Z1isCJIm2K0NZWs+ya6Ih3C06JGpWFz9UFjHGO0KcfFCEVzFVmzJjR2D76x0axzZfvC0zDpUASaErPpNT+hpzbhJynvmzNKANh0wJb1JE2q6HgKBCcyobAFggKlyI9jB+wx9pPNjwuvXy+Rv3odlptx8njm4Gu01RAxUoJsqYaoH0jaK4QFCmZhkParMaTKQzTJmWqAVwdv0HWMnCkjyXMUPfUkOBeX6jxor3i10Bn7aOZqA7bFOGFl3g9GAzCSlcJpVk7BNV6dgyB/6EdXdTCMEilTHJ5N/SCHDC4H1tvfRufvvsWlx65L3379uW5B+/l8GOOY6tttwfguuuu4/DDD2fIkCH81LACNnZj3lOa3pQdyGIsxR64DojoMxiWQMQK8IAQDKrUkRmcg+oc8YyoJfQ+v9+ayf+3J7fffjuffvopAP37dOKQfUfiS4/7Hn6TylKbw/cZFh2gMALWN8biQjJr4ckI1GpWzU4nJm3K/7iQLNQTZrQ9z1Oe1qLoVqHZ5SAkFlLaRc4UVkL6YBABeA+1HAZhRkTfNHX2RgNo7TSh9fsmLh5qMuLKVPB16DbxEWC2RB6LPNIXiCIJhmbvtc45ZJtlOlEIaBoOrq8bg2jNvp9wppC+soWTPqyuLbB49UZWrKllyqP3MnjwYP7yl798n1OuMb5HjBw5koGDBvHg5Wdz+TNT6LfFULbabT/uvPNOevbsyeabb/6z7k8X/y2Zq67Lqc88zPa/P4DqmgLtu3WjY/fNWLVsKfecfTR1GzcAcPyFlzPhsvO56KKLACgpLWWzbt1Cf+iWLVtSXV3N2rVryeVySCnxPI8NGzawdt061q1dx+DBgzjyyCMbQfWvEGvWrOGmm25kp7FbcMBuA5k7aw63P/g2pxw1ik7tmihiwbTVeG34+PkaNa4LE8NX95WMWU2dZ4YuFinTA0wsUZyYVzar8bb0unjeEsF4bCneTMsY0rbSBpdmrXAMr6lzVTMRbRAQSCJSthWy0Do08xwWzpsu+aAY0JIi0jSLyClDg/K4K4a2fdUFd6mAIbZFpCXWxXa6EVOJbQZ64nxMfqLAbkrUhODYokbV2Tj5qPDcc6CmClIZdQAxieyvEb9ZcIyIVTL7MmKEZCpM81pGnpTYiO8r1wlTFDDwcGUW5SimGCf9w5pStepV+lMX4hqk2A/nB/ZahplRllux4wj/u05SeuFLcPP40lMMsu+DEFjCxbTyWDJPrdsMabhhpzFL5EGAiWo9badE0LrXJu+BJbTXsRkUBygNsh7UdScv9VRXvsZlFbHZp+GDmZxwhIBaErDBEWEZB9OptAhTRhpI66YgmXSscEtrqIILMxs0dSgvSVGSMkmbBl07dQBg+PDhLF+xgtsuv5jx9z3AEy9M4sqLr+TSSy/liiuuoFevXj/qtNHxyiuvUFmRZd9RvdQHijG1vucqgKwHLl9ieAUMQ+ALBdr0DcAItLH4SpsL2q84H6bxheFw14Q/8dWXi3n99bfYbadBDNy8FX6hjl7bXcy8hSu575oDGda3nbpr6h8nFWVFAMjnIolFnB2WQStpYSmgG6SoEFaR/Zur/tsZxSALwCuAIzHMfJCFMSLLN1+G2xG4+IaJQOJK5SdebJ8Y70ZpGi6GkXR00cuB0mqrj2mp4kZDSyZsXD8bbscShYQUwhZ1CemFj4mBCD2YgxUDpjgVSqq0dMMy8hhCJrapQLu65jYW3CA9qp1sVIestXWKLV61tpZl39RQvX49a5cuZLs9dv1B511jfHsYhsEJxx/PCSeeyOM3/I3Dz7ucw867gk/efoWXX375ZwfHpaWlHH/88dx2220AlJQ3oTSjJoWuK1m3/CtuPm4/hO9x7LHHMmHCBFp378tbX62ies0qli+cz5yP3mHOx58w//M5vPLKyziOOtcrmzWlrEy525imSbMWlXTu2pre/TbjkQefpK6ujpNOOuln/TyNkQzHcbjqqr+TSRvcf+MRfDxrPmP3vo7Vq9czZdoXvP/i2UGWOFhBe8NLF6S6/xqGKjIWAfGRMmvAAylKcWXg3WsaKLCs6pesGLsb2auJoLmYBIswU6sYWhHrxknQPAvi43XYFdeMejAASCtoeBTYyplZO/RPdmOZZRFjnzUTDZCyI4ceYSgtddpMMsQQZc/SwbHawsc0CmFTKOVS5GCJAiXmWrWO3AD5WvUd+37SjUm6ChjnaiFTEuCrX08F/NsFxzIwiw4AqYGHRGCKAo6r0rwKYBbCgVuzesJww9SuGhhV846Q6gelO/ViFf1OrHOLEBhGrBNZAD58z1HAI7TYCk5MKxXZZ0kX3zUwbKFYPTsTAK904Fxhhcxa0jpLhPpKy8hjGIoN0xeJKsRT34SezeqLqZgJ053FdPMT0zDQHcDi8glXKpCASIr9g68gMeuU+NHz4GPX69NuEqR7NPOnXs8G/ofCMOjauSO9Bg7m7rvvBuCFpx7n0lbN+du1l/D0i4/y+3F/4PzzL+BPf/rhnbri8cknH7Pzll3IpoK7Yly/G5fRhC4jgADDK4ST2xDoSZfEtMInAJcghKOyBdLltUl/Ze2KlTQtT4H0KBRcVq1WbNSdE9/hsL22iI4FFKNtWUGRnQWplALP+ocwAuCrdfBuLpqFqw8QaY0hArwyAOAaOAuBr90qENF5H5fsxJhlw5BYFHD9VACQzXDgUGDVwzRcTCMfPtdhiTyeryUqhK4fMtAxa8/gOEOv9cJx2YbelzCcqMAuuEY0s60nJ6YoIGLXkv4MPgJXpvF8G0caFDwvYHsMwGd9zgmzLDWOx9oNOao2Ftiw5hseOe9wfLfAmDFjaIyfN9q1a8exxxzDrbfeSrc+/dlq9/0YsP043nz5GVq1asU+++wTZn5+jth5551ZuXIlTz31FLOnTebmvxyL43pYqTSfTp9Ck7JSLrn4Ypo2bcrLr7zKhPNPo+sd97LV0CEM6NGKXccNQxhuIOFzWbo8T3mz9mTSqjA0ZdYQOb6o83fg0OGc/ee/MGbMmJ880W+MhmPevHnceOMNLF++jNefOoMmGYORB19LRUUzCgWfvpu3De5/fkhkJTLBQRdSQyjywzIKFAJiJNLR6joKdc/Q/4HEWKo1u7YwcKVikV18MkHGL95JzpU+VhFRBUrT60iftKmzxnp5AD9mxaY62EoRuUrFa4R0cxHdqETLI/Q+IPJx1zZsDTX4UIXaSTmbJQqkxYYgA6gdKIJi93g2M2iOFkpadJH2r1iT99vN2egJRjDA+5hYwSwmY1VhGVHHLhEDxWZws0qJmgAUO0FDg+BH8331lw88i82gqClbroCvnVHAOV2qUtmpbNCAwcIoaarS2ulSKG2q1vGlWl5owOxGJ4rnqpPHdbC89WQtZRmXMTdgGfmw8EkGgFmDegMZsOI1lFgOZSmfJmmLJmmLbOAtqKpFjdBWJWOZZCwRpEVUOqTENkPP1rSpGzJErLP6zggtWTJ2ZM0S73oDqPcs1SknkxJkUoKStEkmZYaPS9ImTUtUU4bylEWTtE3r0jQVaYumGZuWJTbdWjfj8dfe5M9X3Rxu+97bb+fayy6jPJvnmefvZtc9x3H11Vfz1FNP/ejTZ+2aNXTv1CyiwQFy+aDphq9kMxCwsVYi3WO4eQWSffUHqN/UCezYPAcjX4NwazDqqmDjWvyqVfhVK6nMghHIbNJC8tw9RwEw76vVEStsWZG/cehQEdxEUpkke6xvNHo9zSJ7MaCv/2R0Iwu1Xm7wmbVWOWxPHVs22JdKkalsjGXkyYhqVWAhasK/tNgQXlvhrgKLRd0xTwNjLZ3wAqkDkNAjK31zxNDrEIHeOCVqMCk6ziD0ta3t2gxDhn+6A1bOq6AgSyl4JjnXI+9Jcq5kZU2er6vqWFPjUJVzWVfrsHJtLes25FlfnePZq09H5qq5/vrraNu2bYP7b4yfFrvssgumafLMhBsoL0mxx3Fn0mu7XXn44YlcfsUV/Jx15qZpcuSRR3LCCScAsHLupzirl7L+y8/p1aM7l//tbzRv3hzTNDnjL6djOHkO32k7tujUhuMOPYy1q74MtKkOiBJatu1ISVrbbGapcVpQ5zalxm1JrduM9fmW7H7QMbTt3IUzzzyTyy+/gvXr1/9sn+d/PXK5HA888ABnnPEXKst8ZvzrDEb2a811d7zC4mXr2GefP1BVVcUfdh2UdCjS2TZDRAX2roPhFTCDLm6WkccShbDgzBJe0IlTUBqMj2UpNZ5VpC1KbDXupmId5PT4nLHMcGwttc3QAQMisBwVvYlg3DSpSNvB/tQ6pcFYXhKM5RlLjbVlGYvyEpu0bVKatdT/jIVlGpSk1PsZS20jbQoqMzZlKSs87iZpi4yl9lNqm5SnTJqmBSVWrarPMteTNjeE2fe0uUHd/80azPxaqP4mcOwKPPzdghpraqrU2OS5ysvfTinMVewW9QvHb5Y59mODqALIKmXqGwIEYWGP9O2Aba0LW/U6MotDFssohCDa9tYGYKEQuhRQqFPA1wp+PD3T0WkXO8bSOVWqeUQAMAw7o04GX+JXfxO1+DUUY+wDRjo2d/EFBnWUmSvIG00wkDgyi+unwVdykTibBhFzqW2wbGGRtSKrK8WMRcyxI6PnkJRVSD+atbrB7FQv44SMM4mqV4jb4fmYsVm3tpYxAz9EPXOWvk+prQqb4gbhKVMowb7I4/mVHHHkEey8/dYcuscurFm1khuvHs955x9JiwqXe/9xGZt1a8HVV95JZWXlj/JBra2toyITgE8RaGuFEXSlqwU7gy8ERoFIaqA1u4YIAKRTBD5V6ghgw4ZaHnxkMm+8OZv5C1eyeOlamleWkk5ZbDOkE0ceMIKhAzoxcmBH7rpiP7p1bKbAeSo436QbscT6h3A9sNzgf5E2S7eGDk8OzX4T0f1Sqhd8GZzTMimliBflxVlzQ8D/s/feYZYU5fv3p6q7T5iwObMLuyw5LbDkKEGSgIAEQURFUREEAVFADIAgCJJBEQVFiUYkKbDknFFAlrTEhYUNMzszJ3V31ftHha5zdk0/Bf3yUtc118yc06dPh+qqu+7nfu7HVn7UHeXZpVA2O9vJJJyWP/IMS45d5CHpXK9LG+FxgDmUajgLPRVETSIyz1ZEIkXKzFvJSetf7BIkY1FH6YRY4nXHmS2PDZCpiEyZLPNGpnz555ZPQNU005xGM+PthXUGai1eeux+5s5+nO985zsst9xy/1Kf+6D9a23V1VajTkykUsaOG80XTvoBt623Gb865QieeOIJ1llnnf/o9+24447suOOOf3ebKVOmcN655/CXv/yF559/nhtvvJFP7XsIN996EbJU9v1YioySHKKejQgiFIL+ZkamUlq54uIb7+DOG67lou+dyKFfPoyjv3oUM2bM+I+e07/T6vU6s2fPplKpsOKKK/7H3UL+k63ZbLJgwQJeeOEFfv7zS+nv6+NbX9qaYw7YhKRahizj8b+8yiqrrMzs2bMZM2Y4O261uvmwjAr3nsBhirRpyC6VI2iRSEmkM5SSRCKmEvWhbY5TSZZsHoMZgxrWPcLJLNy86ySQYWXb0NnCkVtFJVFhq3ZKrwEuRQU5ZSLDBasLdHxW0cxtVDgW/rs6E+zc70pcMNyOBAvzRZxEzkXsBCZpMRYtSnLQkEWD/cU1DA0L8hQW9zkRtp173dxjXtP50smOd6O9b8Gxx8ZKQatOVJakdCNFStneOGenBUaW0CXfBpUT5wuJkzHFJJsPQrNmtMR5ClRt1mrFWmo1DIhIyoWnbFB5TOcZ9z3xOpust3whx5CR2VeemRtfHygYSFskRDeHzPah9yxQSoZMcqHqpqW62yb2tmpjTu9KkfQEeN9XrNYYzIMSCW0r6hUPY6GJan+o/LFEgop9rdAvB/3b2taED3tYZdCBYq/Fsj6IRruEt+GS5CYhEqhEZeJqmUlrTuJrxx/N1w/7KkcdeziVxJTujkTGd7/7Wd555VnOO+9cll9++X8JqNRqNQaHaowb3WP0xZmVLLiW5xCrdlmN0/G6hDgItOSpldUYTe9jT8xhy11PYajWbPveXMesv/5a/OrmR/nhFfez27arM1Brsc9Oa7HlhtNxJZ+pDRnw61jtUqkAyWA8s53vsWORvcdx4EABhbzHRUDcvQ0HIRkX5ypjXxTEO1doBSJCC2kG/0CiYKIZxf+u1DM2wuHkTM7Nw0knHEB1oBfMM4qGSDY9KFbIoAqf9AVBQsmGeyacftk/86H/crBgBAeMtXek8BIiTF+uNw2L0T/YZKDWot7MaDVzXvnzwwD/cWD2QVuy7fyRj3DGGT/g2H134eDTL2LU+GXYatfdeOy6y7j6ml/91+5BqVRi5syZzJw5k7XXXptjjjmGDdbdhz/dfR3dvYV8r5aNIlNlL+GppZlZ49qxcvSokez9qc+w3lYf5pgD9ub444/noosuYtKkSf+V8+ps1113Hb/4xS8A2GqrrTjyyCP/y0fU3prNJj++6CKee/553nrrTRoNM97usOE0zj38o0xfbrQZrwfN2LjW9DHceNvdvPLKq3xu3w1IRA4iIDuERJMhomAucFE5IZFkKBJi0SKSGak2TjdIkwvhchuUjpGJIQTqmbAVQf9GPo9u/9vNpaElXCHbgN5SRKo0XYnRHbdyJ6Es5mQ3z5p9S7psPpKbv51MoqhcZ+Zql3hn9ufG2qZPunbjbmxztxxZ4aImImtCY9ATRADaSRWzZlEeOsttAnxuVwZWJugi9+9Re/+CYwcEtfb6lYSmWQEKaSdySaoIMtkleVQlYtCYUCvLnLkbKq1MwtL72mqBPcvWqhdssTQ12WnV+dkvbufAL5zNxRcexs47b0jUGmDMyB4DpvMM3nnbgJZKuV24W6oajbOQBojbqmUiaxJLY5MlUGS6ZBw4HN7RJsM/V6W26n9QMG8OBDgdsnkADWhJggc173g4/dXVxQPmHiCg7bOp0oFumUCvyRLbuhVvJLR5yKwWvBQNIcg96FWY13rkPAAOPXg7PvnJxxnVmyFFE6UTE1ZvLODCUz/BHXc+xW9/+1uOOOKIf6n3xHHEggWD9sHNrL63WmiPm3WIsyJ8K0SbzZnTmqs8pVVvUI6BtMmf7niGHfc/33/PejOmUi5F3Pvwi/T19XHbbbNYd92ZjBo1hjsfeYOx48bx+eN/w423/5WffntXRg6rmu9oYYXdsWGUHRiW0vSjODbH3GgWo2IowwgjKzLQEceFA0UbgEaahQIYw4koWiJ7WGRNIhE4X/g38M9bZ3TD+SCHDheOeQ6T+QxLXFTBMwmzBiDndhhzkZ5INm01vxZKmIlME5Hp8hJ+1M7fPA80zY7JcaxKKzf+xS5CUookWaQ9MG40c7Jc8eIjd/PULVew9dZbf+Ay8B60TTfdlHHjxvG9U0/j1M/uyanX3s2I3gpTV1uLFx686799eACsssoqzJgxg8cee4x6Q5F0dVOJ+9sXkOQsbkEz154FBANiGpliwsSJJBVrDZa+dxXCXNNa09fXx6uvvsorr7zCVVdfTaNeJ01TSpUK+x/9bS456Vi222471lhjjffkmOr1Ok8++SSjRo1i+eWX9xpzrTVz587lrrvu4o7bb2Pum28BcNA+G7HPVisxuTdmhW6BKJfs2CjRcQRDii9/dAYXXvEA/QMNjvrERrYKaRzkBCWF5lgIS5RRvE8RsEt1FWdt6Ug2I4WMyVQVKTJaebctPFZIEN186v6GIrGuyB/SPnrltjWSCwOK3WcM02xyg1zyXOfnMqVp5oqupPg+k9Tnos60geRYCssUt3vRO+mEkatl3lc+lnWEmy9b9TYXL+3Abn3AzDEDA/Z6ukRyVcwv7u/sgwp5/36zd1rnLchLUOsvJn8ZI2yxg3KUoexlUMREaog86jFicZXD0CLzENjP6FYdVIboHokvu+vYwnBF6UBTUmGnnTZgv49vxdTlxrL3vidz973PsPvO67Hrdmuz78yxlKqW+WulBtg4/WjatCEFjW7VDEAGy9YJYjHk8CxIiHRmwIWIEDpGyrwoYiCKqmGuOeauKABiDLuLS7h0WzcHiktBUkAscyS5z/qX5L4SoSvjC3jWxNjRtbxrSJFIWGzndKlCKBJhwHGqqkbXJVp+RVoaVibWA6A0KjJOBrq2mEq1zEG7rMk3L76dPfbY459mj7u6uth88y04+ZcPcNCuM+geHiPc/Yktw4rxo/YMqpAsXDjAL37zIDff8QwPPj6HoaEmUgrSLOeaHx7IbjuuQ1Q293DtNZbl6C9szZ57bEpCxpw585j94lv89cW3ufCye3jx5bcBGDNmNMcddxznnXcOa+31Qw7cfV0+uvWqrLv21MJBw4ya9tiElVtovH2IT+BL7UrGguoQwIa66dC7WymILKMcJuYpAbT8gs10AisvQhXXxe5PaIXQmYlWBAVDTN+SgazCtBA8hM1ZMvr+5P29U99XtLae0ipHypYP80UiRWlpPJFd9UmhkFaK4YCyex7yYDFoJggzkbRyxVAjpd7MfAY4wF/vvoFJEydw8MEH/8M+9kH7z7QVV1yRY4/5OkceeSRznnyQDbbcmtFjR/NI3yKUUv/1Rcpjjz3GX/7yF7501NEMG7Us3cn8otqilfmZCo3SJz3HUtKVKGLRYk5/RCNT7PH5L3HqwQ9xySWXcuyxx1Aul3nmmWe49dZb2Wijjdhwww3ftXP4yU9+wh/+8Ie211ZbbyMGF/dx4LEnMGOzD3HdT87joYceetfBsdaaO++8k5/+9GL6+hb71w844ACeffav/OXPf6beaNLdVWaPrVfhiP13Y8XlRtPdXUEPDMD8hajFDWhliHJq0GxPDyjF8K6Y607fk3n9dSYNL5nxNVEG2FW6jZsUGFlk6DKVNg1ZICWoDClTExFV2Hm3SA4GkJGRblaixX5+BnzlzrDwUK4TuuKmX9gbIK2XAK2JNE5TsXXrSWTnfK+JRINSlBCJVpvPvIuYuYiuqwbsmivMFIm0ICrs/05S4WSqsWh5kCyEMsXS8hbkeZGnQyCPcDkxLQt6vdWVKKKj3oM/B/3e6Y7fv+DYXcS0gc5KZoKOTClcreumIp1t0ons7Y2J1HzIM1PswT4EOm0YZnhgEShttMtJxRiCezKsVYCKVt1qUCPGjx3O5T89DKISrVqdV159m99d/wi/u/4RPgMsvusYesZY4OsATNoyQnQnt0jKBlc47aqtpBdHdWSUIlWVXJeRNtM/dUlG9iFQARuXuxC2/Z1Ip9/MfdgnU8XqEfDhag8+hAMlmbe86/T4dYUVKrLfAxMplA8lOtcCKELl4fc5D0Tj0FGAoJIcMqJ+XaehRlHR73i3EAn23rUgTxk/omrq2btV6T/Z9tprL26//XbufPI1dtpkurkXWSg1EEZaoSTEJeo5zNzhFF6fu4gZa63F9jvswrBhw6jX61x22WXc8eBL7PaR9dh24+VRr55vNellyIzt37RJw5g2ZRQ7bLUGXzlwS55/fi5/emAOh3/zGm677Ta23npb5sx5iZN+dDsn/eh2ttlgGt/41CZ8aNOVzH2t1y3TIRHlivm7UoauquvkgGWWAws6INANU+iLke1RDLudcNplLQsPZMCXSpehblgVSRS2ih5CoiicVkLGVqIM5g5s78wh2XtvmeKwuT7i+nKqqj7iEInMn6MWkS35HpHnRRlvrWXbRNRZCCf0/3ZMXitVvuKUq+yYZYrGUI3+BfPp6+tjwoQJ/7CPfdD+M83JDF55/EG23GZbNt16O645/0z+8Ic/sNtuu/1XjklrzZNPPslJJ53EJltuyVe+dgTdyXyfNK2FRGlb/VGV6LI4xiWJ9yZvsqg5lUqcU4okW+24CyMv/w3f+tz+fOGLB1OtVnjj9deRUrJw4cJ3FRwvWLCAySuszBdOu4jxk5ahUikRl0q+CIQQsMraM3nwoYfYf//937Wy2bVajTPOOJ2HH36EvbZdne8esR3vNBSbfewc7r77bubMmcO3vrQN6604lq3XmUJXjxv7RAG+sgw90II8MRaVFQvAKhUoJcxcZbwZ46Q0Y2izYWzE0oaZcwFvc+mdsLARRlP3QCbS2FraMdXcczMfahv9dK2zoqrWRS6EA9NKJ8Q0yVSZSMRkukxXnEJQyCi2VpVh4amK6PMSMvdaWQx4pjcc98K/y5EjIVJPcHVG/Nw5OQlFLFqF7tixxVlKZ3XgNkJGa4OTWgEb7CUUuTV7LmHCpJjXm+1SxHezvX/BcW4n1/ogdJkkJp3bBKm4jB5caPWaJkztinNod1OdAFzrgh3TCrp6jO/eYD90K7O9jBFJGd3Kin3JyNz8PAda6MYQMMSOW67Iy4+ezqK+IT5/1M/4zXWP8OHDr+a7h2zFNpusaLU2gZOAAx02bO9D28EkLiMzqMa0vCWQe7ByXfYPYPgAOGa3zYsWSYSyBU/ay/WGUoxw1RgydmHTWlKS89tC5DmxT3p0D7GzMSoKRRRewEIrcsokou7D7sYCLCNq9UFkkh5YvKiN7dTOIzFrccS5twGw6qqr/kvdZ/LkyUybuhyHn38X660+ifHlChAUxyiZxQmVHohinnnyVV55bT4nn3wya621lt/PWWedSbVS4pMfW98wDK7FpaKPCmnYWa2g2UBIyUrTx7PiChOI0HzlhN+QZYoZM2Zw+OGHc8kllzDroTnMemgO3/7c5nx2l7VYuLjOtEkjeGfhYvoGm6yyymS6lILBIRgxvGCT40CPDMFKPQNsIqlrwrAfRHbVHhnmGIw9nbnYNtzlylC7wiNSgrCJLAG7rkWgVdOKyFYWdHIKA+FjP3i75qQTUUe1PPO5ov+CsYNr5r0IuhFCURKDhqlhPkpWzGSClXrIFkIXC7YIE+J0GrxMFZq+XCuy3CTheWCsNWmmSJsZM3Y6iDt/fDRnnXU2p5126r/U3z5o/+9taMg4B62w2up0JRHrbbQR+3z+EH52yUW8+eabfO5znyNJkn+wl/9su+yyy/j1r3/NyFGjufiXP2bU8MxHwlwxhGbeS0kOklEltmFoRwY08hFU4z5GVkYSCUNUfHSnHZl8w63cdM2VNAf72efjH+b+ex7ikh9f8a6cQ6vV4k9/+hP33/8Au33uYKautBJKaYQtFhFHRd7IZ448hi/uvDVnn302Rx111H88OS9NU773vVN46YXZ/P6Mvdn1Q6tApcxEEbPKChN49oU5jBrexaG7r82YXrv4DfUJcQR1ZQgDWUenOTrNEY0MkSorF8ugq2p0xQ3MXAxADcpBcn1YEEkrQ1pFpUKapnIiWUfIvG3eldZhSmgJ9n7nqowQLZ//EEnnnNX0c6+pdptYANykzICPukWy5Sv6OktLwBNPYRXRMAKb+6hd4h16XD6GFKk9Ljufajfvp/58DHuc2gRoc8yxbBXa4sxGFbO0jTE2ieqBhaiQRTQT2ucm7D2wWnEDmj+QVfz7zXmw1uuwMIeebtOpo6gAnlp5SzbtsvGdhZpLpLIlg9tWPCMnQH2xeT9tGm2wk1bEZbQKOoMtCOLDLwBxiZGljF9d8GnG3P1XHnx8Dh/+3BxuOH9/dtxh7aKjtFrmoXTuABh2VGM1rbbUNEIgIpOsJGzGfyIbPgQdPiBhAqL533V8twouCiiY7SwolU3P8prtswCcBAUU7EPjgK0fGGTxEAl3PQAtjJTFux8Evrs6KhEpw8BnOvFgXKpGAcrq/SZEYxlKIWMj7s9zmL+Qw3dZg+9e8zgvvvgiK6200j/dfYQQHPeN4/na177GEefdyRUn7xFYqUUG3EaJX6T8dfbrACy77LJ+H319fdxxxx0cedBWzFx9cgE8RSFTQDiRWrOdmc4yhNJ86WPrctI5f2Te/AGefPJJnnzyybbjrA7vYdmPXrDE8Q/vrbLRmstw8pe3YZ21yqZiZKfMImSRIXCpsAsvnWPc7LPi3iiMvEgrc63dc5Hn9h46z2qXmGqt7pxvOAaUggHBnVpigNyyLGFLLJtiEviaSKH8AO+a0tIW8SkjRUamS5TFEFI1qLTMAkrGmliayArgQ4WOHUlVFSkin/ndyjNqaW4dK3KaaW70e0lEvZnRXYlJM0VSjhk9eRoT19qOFx/+NVrrJap0ftDenTZmzBiSUok333jDZtVLvnPyyfQOH8HPzzmdBQsXcuwxx7ynbgq3zprFR/fYge+c8g2WG7uQWLbIVMl4HauqIQciSFWFatRPpkt+fE2ATBvANKxkXqvGMa28m03Xn8nmG8xgdPklUlXlmScfIQvHjf9AU0rx+9//nt/9/vf09/ez0z77c+g3vkMtd1K7Qm7nKqRNX3U1TrjgJxz7uU9y9913c8YZZ/xHC7JceeWVPPP0U9x09t58aN3l0M0GDz37Fh859HIW9g1RqZRZ2F/juHNv4aJjdjDsr9JBYnLk5WRyRBk10IJco1OFth6/olr2NpkajBORl665aJsG6cpJxwVQdh7wKsMUFSghpclZ0tKATqc/zlQJgQGk0tYuMJ7AqbWcbaJsJC1VVWLZQtlKfA4UC+q+uIZrZv9OltgM8jgST2bFmMq8sSXblCyhiYho+vk61zFJVG8jtpbmL+++z7HIUrcMJsozC45zQx666+NBsS702l6OonzkEyjmwlq9+D+OIf1AVvEfaPYiZhlQMgyaqa9ovGADgFZUErOTe55bDXFchIVVam54XDLAuFEzNyvLQDRNh6h0B8U8LBhu1Mx3uwxMlzDVMGBoysThLOwzAH32awvZ0T2MTvvr9ufAhgXu2vrSitiAd6FyRJQgaRFFsS924AojdIZQoADIoXdsWFBBBg9CCKIdyyFUhpQlJJnp7CICbQAvQKRtx9bKgqesjUVEq4KBDAcZacJaQtnPR4l5qJ27h7PJq/Xb11KfdKlbNVtdR6GbDY7be21ufvwNjjvuWHbeeRf23HNPXxL2H7UJEyaw5557cuklP2Wfj8xgxw+tRqkUFYU0tGL+oho3zvoLx5xwFWuuuQYjRoworplSKKVZZsLIQpdetpUbHdPaqhlNm/mAGdAb9WKbUsLdv/g877zdz+iR3cQS3nlrESPGjmC5CcNY1NSc+pM76Ouvs+2227LjjjuilOKee+7h2muv5U/3vcCMlScw6+dfYNQwu884KiaNLBjowQ5qQXKfd12J/b3RtgS1diyuu3cOKIduHZ4RMKVX0alPahEyAmut5gZl1/eEX6zZ/ksUsMVJmz0cGHmGZ0ucXZbt10pWkIk2/SVrEXdF5CImkXVSVfUVDF1hHZeQV8+U9/NOpPDPZCtV1BpmcHeuFSrXyEggZcKwEaPI8/w/Wojig/a3m5SSNVZfgwduu4UvHH4EmdJUKxW+8+2vsd46a/HF/fbhySef/I+Ulf9HTSnFXXfdRd+iRSw3dQJrraSRuh+QRHoQUkUSZeSiSkX0kUjj7Z2Ihs/HECiktkBZ18l0mbJcTF2OQmnJ8NJcQyhEEMsUlef/scWY1pqLL76YG264gW333p/dDjqEdVdflVIkqebKSotMcmrouas0bLfrbohLLueYAz/Bk08++R8Fx3fdeQcH7rAaH1pjkg/Df+XUGxg5ejzfPeWrPPXUU1x44YVstsZEaKXoEohSAjJC11uILEaUK2ilEI0mUbWEHmyilTYyi1RB1IJGBF1dRn7RShHVapHw3EVBbLiIro26aWt5KRxp4mwuI40Ak6iMIXySgFEGvJWfYZcbHsjmOkZGJoLmqnYKqYICI7awkpYGmDqA7n4LiZQpiR4yc6Yj6gDn1yxpepvPSBoSJJY2RymQPzrNtGONHdgXws7heWrONzfzh84sERiywEL6/C2ixJBYbpswqdwljitt/h4cslgrh2ZAPL7L7f07este87vRgkpcJFIpZQArmP+TkmWT8/YbGeotW412hs01t6pppe3bx6UAZNjqOpXYZ8eGDOEhn9iYC654iO8dtDE7OF9F9/3uGL3O2Pnthoyd01KZanoGVOZmoPShbIUiwVTxafoQNbQzxrCkPAKwsgcVaInsIkLlSJyQvkjmEmFoPmTOHcByshXHUEZRuyZJZUbTGgWgK2sWK1KwlXVytK3F3vZ9TpZSq1NOIm468kP84J45nP3b63jooQc5+uivMXXq1H9qMtlxxx154P772P2wy+mqlPjKJzdmWHeZB56ey19fms/sF0029AYbbOCLBAC8/PLLfPnLXwZgt+3WNH0itPpz55Clxb12OqtgBX3JFfdy8a8fZdyoLtZedRIH7bQ6G6w+iVojZfKHf8DCxXW/u9tvv51DDz2U2267jSzLWHfddXnsscd4cvZbbPfZn3LxKXtx+Im/J5KS0SOrfOvQD7PWasu09zG/KGwV4DmOQSi8wNgBXQyL7O8RmHtq6om33xfrL94WgcGwx44RNn3RAlqbxAJ47ZvR7cmlLtxC/XoItqVODLviNM95CrV+ku6SkURLiVYSJWKTNCgUpqqV2bdLeClFglznKAVNy17EkaRUMokscSKJ44gRyyzP83fNZf78+R/ojt/DtuGGG3DxxT/hxmuuYPs9P24y7uMhdtl1G06bPo0bb7rpPQHHZ555JnfeeSfLT1+WD22xBjKv0+ZsYEsRR2rIEwJxkHjsQtouKpfIlLIYoJV3U5YDpLpqK4uZiOUaq05iaGiIt99+m/Hjx/9bx6615rLLLuP666/n0JN/wC6f+DR54IAghfTWYtI+/81ctbkb7PzRj3L7nh/nN7/5LVtttRVjx479t47JtQkTJnDDg69w8sIBRowwBEOemcS4m266ibvuvIM9PrQSn9zWVhP04Mowk7rZMLkYcezlEqIHRJahco1WGlEpc/Etz3HZn/7KlHG9RJHgmAM2Yo1VJrcnjGk7/8ZJQXJIx7IqhJNiOgtMaaK6yKitWqqWhbQhlktGbQEyHXnpoStSFKkhsLaWuLneRaUdIWHHX+FsYF2UO7bRw3Ced9V9LSmFjBBaWy8At52ZU4VwEpHUzPNKtReIsvpiYSWgrom4ZOSOYCww3LGmTYOLavX24lYOKwVNpy10mCX9Ljeh/5OlhP6HWt8LtzFyxW3om3UUw7rLhb2V11iqgklW2rzvVoOwBIj1N8oV+qjVjezBORg4fZJzAlDKvK8UZDk6DTIyWy0YZsG7TXLSaQuRlEwClVs9OZbPMXixBfJxyepUEwNO3IMaWmiFoEUEnTFoWsZF51/ity6YXve6ky/I4BhcZTUfPqfQsFrApbNmu4baNn/sbv9RUny3k784CzsrS/ESCndcjaHg87FZyAwOmczkvn7yuf3I3hJqYYMXWi22PvEW3lo4xPjxY5k6dXk+//nPM27cuL/bl/I858UXX+Suu+7i2muvpVwuscoqq7DMMpNZeeWVmTFjBqNHj277zL333suppxrdqXr9QqNtd9fQHW/aNH0hy4vVs79+Jjqw4p4X8dIbi9r2PXTbkQw1UsbtdB5/r1VKMdMmj+KvL73NNw/9MLJU4oQzb/DvH7DnhvzszE8W9yWMpoSOFVBIktr+t5OCdX7xBUNsqdW2fbiBFyBK0LJIyvP2bFZ7DHig6/53vuRhM9KKIvPftYYyz5a07EskMsrRgNGpN4eK56XSSz0fRaqrZKpEpqqkqkpLlQyBnxk5xWArJ1OKVGlqaU4rVdSbGfVmRq2e0kwVixY3GOhr8PJTf+GWcw7i9NNP/6Ds73vYGo0G559/PnfeeSf7f/5gvvuDs+iO5wPw+1/9ioMOOIIjjjiCrbfe+l35/rvuuot77rmH+++/nxO/eyjfOG7vgs1rDhkno87nKynbsc6Oe1mzIBlkbKyvyt1GwykEOiohsia6f553LlowmDNmmX3/7XNrtVpccMEF3HbbbXzh+O+yx+cO9kUgJvaY6GRPUmdxq8pQmnurQ+ejW4klXUlEIgULFi1i1w3XYdONNuQLX/jC//Mxhe3tt9/m8MO+zA5rT+Cqb2yHSBIuu3U23/rZg7y9qMaeW63I6YduxfiKNPkgUiC6u70sQg8VkWMRxegFC9oBb6VMHsVM2esn9IwYx+L+Pt6Zv4DLvr4tn9h5bQOsK+Vie+cMVK60jYNEibk3Lg+jcxzstLlMyoXkwL0P6LhcyA8dOeTmVUcg5bkZk50ON5TrQYFxbHlm7yksZeHVHBJX0P5512fDCKCf89vneLQ2zmCwpC7Y1XTw+7J9vDlkoqbzFxp8BIapD8FxHEGjibaJeP31lFE7nEt/fz/Dhg371zvSv9Ded8zxBRdcwAUXXEDutMRaBYbSQaf04DMAzFmrkEo4vZKTQrgHabAPXa8VrHHbvoKSvp41FsVr7vu7qgXbDGjVhDhGN5sIB9hbNgzfUIUHMphVq/Nc1sokGdqHxQMUaC9GAcX3u220TUJwD1vbosCea/hAu99C4rNM3YDgvtOtQN3D2BhoB8XWls41DYWUJXzoAJq5SXJ0jLHzq3aDQtYqwGQcFYuURsM4NwwOoQfq6EaOShuIcsSK5SovnPIRbn/iDW57bRFX3PcXvvzlQ9lzz73Ycccd/6bcIooiVlppJVZaaSV22mknRo0aRaVSWeq2/tzsmvPp279pQ0iBk4nTZblQkpXYOIYDQCQlRLnMn378aY4+80/cfO8LjBpWZovVJ8LAAKOlYN4vD+CGJ95gjRXG00Tw3NwBPnviH9hlsxU447BtWG6ZUZTKCfMHGowZ2c1zr8znnbc2olJKKHeV+fKntygOWEp8JcAQKLt7Gw64SwPFrr9od0+lGbij0lIG1tywtDK2WftFtrRznnAaN8cQO+Y4bFlb2E96mzbAAm1psv+RZKpElJS9rRAArTrV0kISXWVIjyG3+jkpyt7KzVWFauWQKqc3luQqQilNrW6TUWJJtbtE3jTsyLs9cH/Q2lulUuGrX/0qU6dO5Rc/uYjPH/w5Vl1lPImss/++mzLrpu0566yziOOYLbbY4h/v8J9sb7/9Nq+//jqnn346K664LJ/5zE4ceuhuhdTMVUwNc060MqDIFnsCvCzMjA0WZFR6EYMLPDspWnWTSN4YQkdDkFQY1VVmxRUnM3v27P9ncPzss89yzrnnMm/ePL51/o/5yJ4fZ7CVEUvBiEpCb8lYj3XFC4FRSFG1ciPJQCv3Ti6RMB7gU8aNYbNtPszTjzz0b19f18aNG8chh36Z0047jZ/d+BSf2XF1Dth6Rfb/0ApkQlKq2vmxLfprorY6bQVzfIbOMkNOSQmLBzwJ9uizbzFvwQBfPeZbTJ06lb322ounX5pvZBrKkEUiMcDb+N5bXXMpKSqISt3m5dvpBW+ichakxtZiNowmxsbWVTh3DMe2OimlS/rLbT5U2kFqueYwgnsvjFyUqmiXt6P1kqRV4ohCB9hFMaa73bds9N0BZ+dMIWTh7OHOvTEAXcMDhlkbOWFuSaGeboSqFtc0y9qwlG5ar/5aHZrmXq2//vpEUcQhhxzCIYcc8q91pn+yve/AsbtYfc/ewshVtzMXtKfaXgDBAVBoZ4eVLkr0KqsRbqVFmD7LDSPZaBT7UtoCs8isWN32zoakZM3G46j4zlZagF93PLbptGUAMphjyLJin3Fs95kUD5R7KHNTJQyXKKUpHgjXHLZwg7VLfAtD6m5lGUo3wDycgVzCfIcugKvKjMQBzIpQqfYH1q12HYPoHRIovsdJW7R9gN0D5CYOexzU6uj+fkQ5KHYB0GqZB6mvHz1gHl45oox6x/wtekuUSxHbz5zCdiuN5+h1luPk22Zz0eW/5Oqrr2LjjTdh1VVXZZNNNmnTDoftn61OVauZ75w4qmquR1wyrLa7n17za85Z51lbKWddr8PQEMtX4Ddf3RJ91BYIx9zayMKYnm4+NaUIW26y+kR22uAQeksR1e4KkKNTzZjeCijFStPGcf7xH7WTra1E1Gq0PxvhIk4Ic4+chVEojXDyCeey4QbCMBHPLcbaGIMI51qhtQkNuhLRPhPaeXBaLZ7vQiL3bLF3T9FY7XxKqqtWTlH2SSg+K5uEIp5pgYo0+vU4SelNUnJdZigbw5/+Wmer6WOoxtJ7fiqtKUeSSAjqWU4SSbJYUinHxLFCSoFSmrnP3ENXV/d/LJz8QfvX2q677sr111/PhT84lQt+8n0T/hWCy37xbZ6f/RK/+MUv2GCDDf7h4vbvtbfffpt77rmHv/zlKR55xFREHDt2JPfcez6jRo81+RHNoQIEWQDimDtvhwi2LzY8c6jt36LcjV4018jU4hJa1U2UrD4QsGpDUOli4/Wmc/8jz/8/nUutVuPoo49m3Zmrc/nvfsGKq67J4lZMbynyVUqrUR+xaJLIOl3xQjRjKEfmuYylpJmb8uoApcicRyUGpYrF7PPPP8+ZZ53Fh7fdlj322OP/6Vg322wzHn30EQ77yV2sMXU0668+CQmU8hSyILGr1YJSyUgpQuDpk71yUHZu7On24/C8QTOPjB8/nkqlwmabbcYfH/krpzQafp7RpdTMO+E+nQYrbRYAMYq9DhkIcjqaBWvrCCYnM1S5B5BaZTC4sBg7bTEwoCCUXD5S+J6LbDsQDsWcqnJf7toTTW6cDsZo7Y7LOm94CaT7nbXasEJhv2bn4foQVFydh7KJ/rbqdjEoivlkcKCIkjswXCnDwkUFexxHiO5uw/x3VaFlSM+HH374A+b4/7k5IFEptz8U7owda+dubsj4OjkEFAyyUuYGhaAG0PWWCeFUrWdtWENZKQOkPdh1JW+CTutayFI7zWkpaQfxrWD1F2cFs+eqldkHUIehm6XqaouSl53NSx0ccA5lAMFntGoV8oa0UYDYpYEsJwlxjgatxpLbOL2tUmYB0GwUr7l9ALq/397HyABIx9AH2a2qr4YeSpEjysgRXeihFJFIyDVytE2IG5MxdniZsz+/MV/fZXV+/tRcrnnoWS6++y4uueQStt12W9Zbbz1mzJgBwJ133smjjz5KkiTedP9vZb/39fVx5ZVXMGZkN++8uYiK1NQWv8NtD77E6K6YXMMmq0+iq5K0MxuNZtFfXP+05y6kBaSd2bwd13Fc1fZjp+GS0iShJKUiwuEFtcG+YgqddxvbH5n76pl/2SaB0S4CAQVr7JmS2BYQke3SHpuIKXx/Cm0CU8v6Wl2glVy4RNFMJe2aeYxWr6mGeS/RVt7tGeVMDaMcLTYb2yiLBylukJYZOoqo5yNIVZWPrAyZGmKwVfbZ+M69wlWoMo9ljBSCRjMzLhZS8M6cp9lgg/Xfc+uwD5pppVKJD3/4w9zwhxs5NzuJrkrDe7H+/NKjWWPG53nyySf/n32Bn3vuOb72ta9RKsXMWHtFfvLjI5gwYTSbbrYWI4Z3gVrcZmMFLMH66axlXFsClx2gIBXKVfTgAptDgonANesmV2bxoHl+u7r8xzZYd3kuv+YeWq3WP+0x/MILL/DYY49x441GZvWTHx/GqjOG0cj7zCHbghTlaIBK1GfdipomMSw2i9ZMl4lEiUpcphyZEsiRaKC15q5Zs1hn7XVoNptcffXV/Pa3v6Vc7eKqq65im222Yfjw4f9P1//zn/8Cr7zyMpt+7TpO2H8mx+61jp2vdXsBrczlH6TFmOctNmTxmjS+8IuVYLev/QaA7u5unnrqKf7y5yc5YMNJFqTlfv8azJwvJX4uzXIoWRzhyCdRAOWQodWOUIJiXHTvZ612DW9ILuRZQJJVDNisD1iwaSPjrTo+J0ll7aBW2FLMzYECM+Qpxlwe87nGUJHb5FjeMLIL5u+6JXyU3XcrMxXu2iSmkSUhogKQyxh0s/1cnJVbw+ULFfO5bjYLbASBvd67396/4Ng2YcMUTsMpKIEMQDHYlWS98HANsu512jLZklAU6Gil6GaGKAeXzwHaTLUDRNd8sl7U/n/oCJDloDRaCgNmsrw4VimNR61PEkxNJ4ozk1QIS+qaQlnFUoCw9yIPa51rB9yDQds9aA40tQJ3BXcs4YIiPDfXmWtDxcIjNP2Oo+IBwILzABi6e+LZ8/B74tiwyGmOiISx5UmVAcG9JUS1hOqrES0zPDi2whBeThgGrZRlRo7nuDUmctze67CwmXPuowP85LLbueGGG+jt7QUhGFi8mE02WIn582t873u3s9JKK3HcccctoTUGuP/++5k/fwEAK+9wujkvIQjl/cN7Kpx95If5xNYrEkUS+vrNG6H8x7kduAHfDVpxXLwXyh3c9QlCiE46pDPjFiLCxIdQ3+4GcXedPLPlOomLLlAM/m4x5gByKLtx4T+/H/ChC5fNDT4pBY1xirCWVr6Soi/3XFgQhsVAMl32PtlOsxxLY/uWqSqVuL8oZ6qjQgKSNc1xdw1HlXqthVtKIuu0crOAGl2tk6sS82rSWoQJMpVTdeV9rd5S2sVGfWAxgwveZMTMf81T+4P2n22bbLIJV111Fccc/jXOPu8YKpUm5CmTxhmmadGiRf9gD0tvixYt4qKLLmLi+BE888SF9A7r8TpTWvVCRgGmIpjKisiaVnjtpZDoWn87+KgPFYvcVlB4wpEOtboZ6/LMjJFZZkFFxJabrEye59x8883svPPO//A8brvtNs466yyqlRJ777YBRxy2OzPWmkommpTlAFFibMDKcrH3ro1UZl0KlE3IyoyThnXbiG2ZdikyVF5DSskf//hHZs26DY3m0185mv0O+DTbzFiFP//5z2y++eb/T/egWq1y2mmnc+WVV3L8Zb9i0sgqB2ywHDTqZgyqlIuxDwoyq2TlXXECzp3JgjCtFPW8mMuVUnz7299ireVGcuQ+66BThZCqHXhLiW6lRsPsxmylQAZgvFQy441PzkvbwbBzxtIKnCbagfiQwFDKzFmOPAPDurp8JMfYZpnpN1oVxcMcDvCRXDv35oBy/sFxQWwlpQLIuvcApDb90l3PcsX07TgpIqLh/N9qFCy2uw+VLnxyoMtHWrQI0dtrMFW9Vpy7DI7bMf2OIHqP2vsXHFsLJt1qoJOCcTPCfOGZ36UBWC2lARmLB3x2pLAZljrX6Jq54arPZhlXYmPzojIPzkQiIVLtALrthlvg0krNitO9Hxs9p05bPtNV+AzUAGCHDEGo1bW6J4TyY0AbMHarxcDNogBYgRtEoGs2K8NG0VHDQcJqgnxVISmK7eLIDOoO6DoQ7BclHQ+UlObaLy0RMtwWoJWiFg4hpDB2PGEWa66gEqGbKb4GsKsUZ43egSIpMsvM8TYajJ6yDCdsNJbvHL4dz8x+g6v+8Cj0jGffj67NahMiWNzPA6+n7P3pczjmmGM46aSTlnAl2GabbWg0GsyfP59VVlmFWs1MFqutthqtVgutNb/5za/5zInX8btZK/C74z9cRChcyzJ0rk2/c8mkEEQuiuQK38JJILzGwYJKSxOOE1mQ9FApt8t1OpNGhMAn4ImO77P3yAPkMAHPbeNAfahZswl5xu/TeYCmSy9eYydjwGiHnSuFZZS9FlmYIiFD2Rhzy+OFJML5gaZoWUK06iaTOkq8xlPmdSr5AFkyippOrBdtmZbqpqVKVGNzTqnSVn+sTCpBJDwwbgwO8McfnoBQKbvuuisftP9emzZtGocffjjnn38+jz78NL/82VdZY5VJ/PWpFwH+pWqZb731Fg8//DCPPPIIf/7zn0liyWknf5reEcPwnq31ftvfbf9OmyY5Sal2raf1fvWSMjfONZqFNMw9h268deNno1lEmEQwZgrJGqtP5Ytf3IWf//znrLfeev/QJeXmm//ENpuuxO8v+gzd4yYgukcAEDNELIeKRa1zNxCSWLeMBCoqeas5rSURTSNfkolfpIqS4pG/3sXNf3qIx//yItvvvAsTpq3AmGrE8BHDefPNN//p67+0liQJBxxwAAsXLuTLF93Fx1abSPfIir9OS7DEYO3AIlCVAmRKS8bEMcM1JHHEXnvvw+zZs2m1Ui788hYsO35YMd42mlDShRWmwwtWdywcw1kyTjioJsilyyf9nBMk7bflLbl50MkGe2xyoYtGOJLNLwLscTlnLSeVaDbaC5ho1S7pdKSTmxPTlvmMwyKthgHMjUa7fFQr2jzxs7zou4sHLD4JXLycx77LFVo84Puznvd2OyYqlQrSxumQwbLeH1i5/fvNgd/+AfOQVwKdkHuI3N9QdKhWC93IC7uRXEMiTWLXgAnfeNACkEjIlfFKdOWWc2WTzQILn0QikiAMr5QBsVK0JWR53U0c+7ujpdmnIC5Wiw4k+QdLFB1RBuBGSCAvwigqAJk6YHBD3bG/hoXWuvNvr1N2wDcEvUq378M9WCH4g/brGLyG0mZR4c4lAMa6kZtrrswCRA9Zb9DuxCxISiU/wOgF/cjxw4r9SFkMAlAkV0QxJAodR+bYBwcQccQaG2/Ad7fcEppD6L63YKgf4oiNJsPd536MbY/4DccccwzHHXdcW4GRUqnE7rvvzt9rRx/9Nfr6+nngmRfQQzVEFKEGWwgpEF0JxDGi1MH2+0UH7aA3fN8NJm6AdRNBh4RHYwfnUgJDGcJp7d0A5wYmKc3gGFOwxW3G9xRV8YJy48ZX0wJj9zmhPXMi8pZNCM1N5TAZtVkbhWVVpchIlRngY9Ei02Zh2FnKvJX3eJN8Ew7upyQH7f4S4ub8gtGGIlkqbYCMiZtv09ucQzZsOvV8BJlN7utKIlrWsgpM0lGmtAkjW3B8449/wPP338LHPvaxD/TG/wNt2223Zdq0aZx55pmst9Fh/Oicz7P7DkYi9cYbb/zDzyul+NWvfsXll19OkkRssenqnPm9T7H/vh9i5JhRZiNnI+kWk0qZscKNjS6ZOHQXcKA4HFcbxoVH9PYWhEmj6YGL1i4qZJKOtZSILANM/9RZi+9/ax9uuPERfvrTn/KNb3zj756b1ppxI7voHjvOalBVkUwdzhHS/g3+WRdRSpxkSFmUNs7sMxl638bVhN0+uhHbf2QHMl32hXZWWWU55syZ8y/ezaW33XffnVmzZvHAnPls0zMJEnvMncn3UGiLHUgulaBiC3ZpRaVcZt+d1uKyK4pqg9c99AoPP/8OH1l/WaaMtjIWl2PkwJtrUhj5mmN5HdCFdlKsg2DSacs4YYAdo6MCKLrzqFSKaGromOEwjmuOkHLkXqlkgLFbVIVzsArOwYHRkLF2cwkYVtvtz0UZtWonzNxnFg8U3w/FfOz6b62GXtSHz8ty19RdK6VsTpeGll3EurkM2qPO73J734NjPdQwZ2lvlm7Y1xu2cypdML2lCFGOEJXIM8A6Vej+wiZKp3lb+ENIYcrqNjJrJC7MazabV7dsx0mlEak3TThMJFE7MHedvpWaQnglBZQMgMZ2+oiCYezU9EI7AIo6BocglNHG3oarMmjvsK4DO0DsTc91+346QG84COhm6pl2t+AQ7gGMBKqZmwVIbioViUpsQG+aF0y8a7k2+6hllpkXyBHl9vNwGvMsQ4zsbWeeoQCP0soIXGnlVpAICTByPLreD0OLzAInbRa69IV9LBtl3HnuXux+3LUcffRXWWWVVRk9ejRbb701M2fO/Kc8lLVWjO4uo+s5qtFEDi8jyklxDR0ADgdCO2iFTLlfYLhoQhsYloAZBJ1cyMuHysZCEKWNaT7WJcXXtMcyXY1igHUFaVzkAQp5hdUbizCBL22CSGnL2HaLt7hUsG8qL0A3psQuQEv1oHVR8REMQNZIFLaMKYqW6jYTM5LueD6VqI+yHLBXoOXvoc6tpk8V+k9Uhih3oyojaSbLkeZV+z1NepKUVFWBqpdWxNJoj3OtkfY+b7rXZ3nu4bt57PEnOOAAheycnD9o73mbPn06Z511FhdddBEHfulCxNmf4pBPb8HV1z9BmqZL1YU/88wzXHrpJfT19/HWm/P45rEf52tf+zg9JY2XE+W2aIS1UzPRuJbp61GCyFN0HgBjl6gVgiMHAqzNp6hWC9CstHGt8WNy3iaX8pafpYp/Jnt7yxz65X345jcu/Ifa48mTp/DMi09DbICxbgyCEIhSlxlz88w8w1ob9rMzWqQypDDPp4xLRHGZkk2EdcUrtC2JXCoN+QqtuS6zzTbrcv75v0Wpf/8ZmTJlCsN7e7jr+XfYZs1J6FoKSd4+hkK7BG2ciSqZhYeZV5192IXf3JXL/vC43/9JvzTJlodFkr22WJGzPrk240Z2FyRWSFRZQKzjYF4uYAOAryzqAV4cIapdhY2rlyDWzTzmiJxGw4P5NiLMF3MKnLVCgqQRHIBLQMyCfhgCdj9PBpjEve72VynbY08Ldt71yxAQO4bZfR+Y92p1Y2iQZeiB2pJkodveX9/Y5HTlqZFN5hpd+4A5/rebtprZfH6DbMj87QCVawbEWpARSQOGa0ZwrxsGmOma7VQBkywSbeupm5A+zRRRjtDN3AA7C7gdg+o7QQBodDMr/k8sE+CYPvcwhywgeEcDkdnOI+3KLwRRyuqU/QoxZAwDa7k2uYJu39Y9QK20AMRuQHeA2a2OO8P7ShnglirPtBOAYXDy0+L6+PtRjswiI7eG6mne9gDpNEcQtd9Hd94OFLuHOgT9waTjtWBuYOvrL1b5bmUupQHFXhoQDCiNJnpoCLWwzsQxkrt/9WV++oc/c+8Tc/nr829ywgkn0NVVZdNNNyOKIp566imOOeYYlltuubZzTdOUZ55+hh98fB1o5USjzESn69bvMQkWPPaaemmPXbj5aESY1OAGandPvFWh0bJ7kGwZZeHON2QAKnF7v8H2qxjTt1yCnmPNXBKeY/ezjglVSIgT43ASFIsp7AMNQBbWwiqSilR3F8U9pC0CEhrXa3wyntEpN4kECDFENeo3UovcgvI8hfqAycIOtfPWNUBIq5fULRJZRytbQtWWdkXiK/NFokozh1IkvO9yHEumTp/Ghw/6Or8++cu88sorTJs2jQ/af7+VSiUOOeQQtNYceMRlfG6fDZg/fwFvvfUWU6ZMoVarcdlllzFnzkssWrSQN9+cx5prTGXvj23MLjvOZNtt1i1CyK5oT6lqx1ibdOecBqIEcjtm+mcjKyIs7hlzoXQLaoyGOEen9fbtwnC+e04d6CiViqRz+9xttenytFotXn755bZIVmcbP348jzx8X1GgyUaDtHPYkLFxzQibe85jA/4L54QMYXXQUrZQolREdKQ5LilShFZkusrOO6zNKSf/jJtvvpkddtiBq666ijvvuI1qVxcnnvjdf7p6KWCkamuswd1Pvwz7yIIk8DkasSGXHKNqc1S8/KzDpnXO20uX2+S54qrbZ7PFulP4wk4T2kkhx6h2kkVhtNLKOHVWh66qcbvw4FO0V+GrlI3cz0VcK5W2aGebHjeKDatrLkYBnN01CJPZw3kwbOFc6e3pwv4afMblXrn3XAvxQCitdNFHO2cyf0FBduUa1cxNpLRSyFR0rqFhAHixrY3ER9KTm+9Fe9+CY1EaAYBuZRCbi6ojgUik0Qg78BEZgKwbBiSasL5CD2UG5Lrm2PxcQR6hB1oGmLgHsuZ+FzpXUbEgL2+Z7yxHiO6kYEATuYSswDO2IYBVyoSowa5Oo8JzMQzhLE3bFO7Hr/ZcSCVqZySzdiDcphF277tQnwW3bcxuKzfXLFWG+XXnKe15BtpgEQmzAHELCCkKVj61YDCR6NSuUiOBqMTIriAZzZ1rGArqlIWEDh9hQkOWo7Mhv3r3PsmO0QFQ9QJ4u4e3vx/6+pHjehErTqcURxy830Yc/AmJjmJuefA17n34RX5x5Z28/Mo8AI4++miuueaattucpim5UozoShC9JdRAiuxNzGAZiWIQCP7WkTCRjURCWbbf9zhGC8H8RTXGjOwytm9t10b7yVirzLLkLgpiB7KuUtEnOqUYbsBzmeFh1MNNPOZGmlBtkNTqQs6azEym0i7snC1QFBsWTkiUKAVyCmUP3fgVh+VWVZCk1xUvIlPmc05fLLJmoa9Lm2aydy4CrikFwiROCVEFlRPFTSKf4JchdBkUaBGR58Yebni5RDkyFlb1TFFKJFmmKNmFXHd3Nx+0/50mpeSQQw6hXq/x4yvvBeD6669Ha82dd95BmrbY+2ObM27Smnxo89XZaYf1iaNgTAHjywpGJqSy9opk1gNWZ02z8HRuLnndgOc0cDTw+Rp5sZjUCurNYvx1hIdLXnbjjzkZ87wNDkGpZYtQmBLAa642hTiOeP755/8uOM7znGYzbbf6dDZb0OGyoAppnr0eGjMWCmnHS6m8t62OTWIrgqJSapZC1iIWQ2y83nT22GUDrr/+OnbYYQcefvgh5s59C6U1V155JVtuueXfPfbOVqlUaWoz52At1jzBQCA9s8SGd1NwZEiWQ7fJ05gwqh2YX/6DffnEUVf6/8+85lFGD6uy5RoTGDss0PF2As7O/BCwyc9JkJtj76O7vw4Qu3m2ZKMDnRa0nXMxFNu7XKoQnCtVLAjCsR0KIi1kax32CPcvpWehfbEOm7ytXQVAZUmGIF9ID9myz3396P4hQza6CDuAFOhIGMwERaS+WezDk0QWS6hGMH6/y+19G/tTC98BQA+l5E4WEYk2BlinygI5+2OBsepromopupGhBlroWup/61SZ9wIA6Jk8u1//PVIiLPjxYBwDmkU5NuEf711cCh6qUNIQaNTCFoY9wDISadug28YOh37JfsBPO77H7EOnLRtu71gNW220TlUBjHPLkrdydDM3ixDHuofg2dThDdjPgs33CxZZLBb8QxEmV2BkMboeOmMEk5gDb25icRndYQjKTzr2x1UvhGKwctu5FmY+1+qogRSc7rBWN9clKSPK3Wz3odU54eu78dLDp1J/50p6eqrU63VuvvnmttvnkoLSUoS2MhG3cPD9UWnT79y1ku0LDOLIyEJGDKdVrvLxU25hwl4/4RPfu5lnX1vEMRfeQX0oKJTiFgdxXFj0OSbCNWdq33lNXd8K+1fnwimwE1zClF4ZtwzyDK0yWzXRMTxWXuFlH8GzgiISmcmYl02btJchbandcrTYJ+Qlsm6kFXmrYKWzlk2Ockl70roH6ILVs6Fi15xbhilCkhKJzGspI9miJFt0JRHlSFKOjMwCYPrqawDGg/OD9r/VoijiyCOP4iMf+QgAN954I0//+SE233gl7p/1PX55+fGcecYX2XWXjYlLgSwISDPFL6++m5tveazdYqvaC5VuiBPTx/KscNeBos91RvGC56bQE7cC4Jz5salNK9qpXZUWiFd60FmLSkmyzdbrcP3115FlSwcReZ5z6623sMdOawfHETzTroXOR42hQpNsz987Ojk7T3tNIjVkbBpzK2VSufmsk5akTUb0lqhUKmit6Vu0kBlTRjBxdA93334zRx11FKd//zRU5/H8jTYwMMCYSpj8G3nbTt3IzZzdzAoACFb7WvfuU7puFj6jh1e58ZeH+n1vv+HyqOe+z+9/+Ckq5ZgXXlvEPt++jo+d8EcyaeftSqWI7ro53I2HLhLsmN9QjuGAqsvzCOflMLLpWnjsDtSHiylXJKOT1XZRik5g7K5XmLjoHbeyJaq1hjhECFOsyieIZiZyoPPMzyuGWNNmEVero4dSkx+Um/lLp7kFy7m/T2qwhR5omTnPbq+HMvRQRt7fRA2k6MEPZBX/dgs1mdI5RuQanedtCXfO7cCxx/5vB+rS3OetaRUwcdjwNgbYCLmkfk03M0hzSIyOmVJkw+G0r2j9gQbh/c6BcCmrUd3JFrtVX0q7e0Sntiv8ztDHOZRQhNokGwrUuUY37YAfssS5NtdJacNw28WBkMViJFwNtl2jVCGkQKUpsivxLLG7X/7ztdR8lzTsvwqY+/B7RCLbky+lNKE1d56dg06YNOHY4WYTobRJ4ghbrY7o7SVaY6Q5zkbDgFNXGrpVM36kpQokZco0WPDCD/niVy7mhz+8kO7ubjbddFMAHn/caNu2mTbaRxHMeWaWLc78uYlKXDDsvVUY1otISgy2FAuGMp57bQEHn3AtL706H4D7nn6LLQ67hgV9NeYvbrDb5ivwkY2mIZLYJH+E/SLLzQRW6lhsQTHodshf/DMQlwtm2DULgM3BOhbNapRd1STnkOIcIyyDi1B2dwqhFVpIwz5px/qUEFGCFopI10GAFua5SGTdlOtNm0WFsWbqiyp4p4DQYq7NaqgKWQshI2JhvxuF1DFSKlBVqlEfQihaeTdSZWRRFyMqCXErZ7CaMHL8JJZfe0MuvvgnSCnZbrvt/qYX9gftvW9KKW64wfj6/uX2b7P62isj4jJUe8lFlShbDEDarHP7XX/hnXf6eejh2fzx5kd57vk3KJcTGguvQbfqhjV1nrC20peIS6YQkowKz+LcytaUDe27CKD7O3SqcARFCKIdoKpULHlgQdiIkeb9Srfp80kZ3RzitJMOYJ2NDufaa6/lYx/72BLXYN68ebz99jt8Yo+PB0naFjQ5h6K0afbbGCqkG05vraDw6lVeOiLisrUPs3aJLgFRK6Prz5q45NeekuCtt97iqaee4u13FvCTb27HR9ZbllwILrn1Ob547p1svMmmbLbZZn/3ftZqNebOfYO1Vu0xY2Yzs44RJQTBfGXJBtlFAVhDKWIpYe4bC6mUYraf1sXlJ+7Kb+56gTGbncwe263B+utOY84932Li+t8C4N4/v8Z9s+exxYzJdo4MgGUYoQzmWeOK4WQwVhPmQLPT7VYq7T6+IfMcJuiF87Ob91u0f2eoN+6MKHeO6eHnnMxSyoJAGaq3fV47RtldRyyjbG1CNc1CTmGJRXfeYWTen+ZgkEOjtCeDoMhP0kojkgjVmYT4Lrb3LTjGXmudGYbXSyQs4BJJBLlCBfrgQiqQe3BsgK+wmmQFMmp3qkhVwXIuwXpaJtQBn4bRM4tGhqjkhWa0E8SGWlHXwvfaQuV5ezjF6mqNrteBZN0+4LpLFFrAuc+GTHUrNXZolsX01y9XBXtsgbE/77JLqLP7zrX1/NTFNv58FCIxK33hz0d7IC3KsXEBwTwcblEj7HcgVTB4O8AWFw++Uubc3fulcnF+PoyUm++MFTTtNYgjs73TI3cVmjCdthClxE9WfnUeDjCtAaiY405aLS785i70L+jj1FNP5atf/Spbbrklr732GgATJvYiYlmEmnJl7pvTrgOqv2kiEGN6oVJh3sIhDjv3Bn4966/+Xq655hpMndrDyy+/zGtv9fnXL5/1HJfe9DSH7rMB53xtB3PvcqPdNuWlErIsZ7BviKF6SiYE9VbOI8/MZZlxw9lq3SlLLuDCEFzY2gbcQC5UrljdMR3bu0nZMk/avCaUgrxl2ClX7Sm29kG50S/6YiLWalDkmSlTapsvsJDaMqmuZHe48HTHIKQBPFoX+mggpkUsJLmsUpJDaKQvY22KlcRUY+NkUSlFTBrTwyHf/xHnHPFZfvijH3HnnXdxxBFfYfz48XzQ/vvNFQi55ZZbOP+ye/jRzDVBSt56cwGvvPI0f/nzHK7+9d08+PBsBmyFzXHjxrLGGmtSqY5mcd88SAPphJCQpYY1BnR9wCyUHWMKBRiz46nve86aLQRVYAFHwDh7L9vMLPrHjTGleF01yszuc+4rMHw4a03t5sjDd+ec83/JVlttxahRo9qugWNkq+XERlDswtFVSmsMmcV9Y8ifn3G0yIxtUqdVYxRDDjqz+n5Hnjgb0LTZ7v+ctthpnUmc+7O7+fnPf864EV18eLpJkovSlIO2ms6v7pnDlVf8kvXXX59yWI0uaM888wznnXcOA30L+dSma5lcoErh1KBqmZegaetpqmqZzRlKi7m3ldJUMPmjP2aDVSZw/8X78/FtVuGJ503k+bc3P8UNtz/Lsade1/b9dzz2GluuOxUiCwwdqI2ToiqfWxd7za+9v3FAjEnZTsJ45jfI1fFzu2qX5bhIZwioHSgOcULYPCkSJs25JD8z/7c5UdXqxfGHsguX8OdZapekp0x1WiuhcGSaqqWGiGzkqGYhrTDRZ+VJSZUqn3yXNXOiRBKVrXw1V+2Wre9ye9+CY7f60LXMFwzwCV65Qge2aw74aus2AQWI884K1jdZRKKt04mupGAuA6bTgN4l2WnPhDYy42CRKJNh6xLPwonb/e+aC9l0buNWkp2dGEwdeddC0Li0sFUgv9CpkUlg3Tb88fvrazqxB8Zdid9/IaUoADBWgiLKAXitJEZOEbZcI/KAbestmdfc/52yGDeIpEaeQaICNjgqrpu7XmEWd5hpK11Cnz2//sX2s5HxfXQtjvEFWMLkSZcN7NjWxQN+FV6q1zjjwI347S1Pc+utt7LFFluw8cYb84c//IFrHnyVT267snH1aJpFhJAC3cxRVr8uh5cRXTF6oIZ6ZzFbHnM9CxqCHXbYgYkTJzJ16lTWXnttnnjiCb797W8DcOyxxzJz5kzK5TJXX3015//yl1zxp6eolEu8s2CAD220AmefsBc33vYUR5/8+yX7AjBiWJWFd3zNa8+0C6f6ayba9d/unrsQo2udBWm0AmRhfyUUhb+2atdzJmUDjB1T1bYP7AIsSIJycgn3vwPFYeKpVLbUe8Oy2Rm06kYzqhXkScFwA1FkJn4dGf9SLSRx1CLXZXpKBfjIlEYuO4nv/+pGnn3kfs796hc559xzOeXkk5d6fT9o720TQnDYYYcxefJkLrr0Ut54q59nn5vLCy/O9e+vseaa7Lbbx1h33XWZPHky5XIZIQRnnHEGUg+Y6nZxyTOp2vXb5hCUqia83Bhoj56E1miNBrpuvLYNyMiXHNNdmN2BYoCebsTIkVDutotEZRLqstQUWBo+HKq9iO4RfOPre3P+D6/njjvuaCvVrLXmvvvuM4flZESNmrFqzIKxsNUoQFcUF89jm2ONJVxadcOeZ6kB7a2a+dtFhGo2icudz+IBbn7gJYYP6+XNN9/gwPUnEzXMItkB2h/svw4bHXMDB33us6yyyqp87qCDGDduHACLFy/mhxdeyD333ss6y43mj8duw0oThnkZmpNUmOO1c7abnwNZX0FACX54/VMAPPTsW9BKEcOHM26Z0f66NdMlo57brDfV3y8fjbPn2SatAQSyPd/FzdGdydChHENKaBVSC22LhIgwStdSgYwjL/Yb2qK55sa+Tpu5YGz3LhNuceakPktjmRcPtJNyWMzlJIG11DL3mbdbVUqj+p12WZE1CtJM233E5djfo8hisLxZyArVUu7Fu9Xev+BYud/ar0ycllMkJpTkQJwsF9v61xzbG+zTJI0VTLHX3FbsDXUhfncM1pFCO/2x+5yzj8MmWXWWtfxbLQylhAC38zOhRta93xmqC1vAGvsO7iQmQcJdyBLrVHnXCdEdF64eblsL8kQlNiv4rqRYIbqFRK79g4PSnmH3iwnXOq4rAG4x0WEPp5s22SxMElvaOXdeNzdodG7rmWRZSFXCwcetpMP/3cKkbnVtjQZTRlWRUvDEE0+w66678tOf/pTNNt2Ugy+6nwlje9l2+mh0IzMDwGCLOXMXUxWS8WO6EJUY1d9EvVNn9muLeH5uP9/+9rdZb7312k7nD9f+HoARI4az/vpFCePdd9+dPM+p1+uUSiW6u7v5/e9+y+rbfBcw2euf+cxn+OMf/8gTTzzh9ze8p1KcW5ab2gPu2gSJgG3hQvAMgunPsZk8w+IikXXMcBN0nlrtpQGholQtvifPbLKQYa1M8pwsmGHHOofg2H3O6TnDRCh336V7Rlx42EzQWimvgRbO0zlrQVJBAAktkig2Jdoxco6SHEEsqwy0Msb3lKjEERvtviMj61/n8C8cycUXX8yBBx74gcTif6Q5D/LHHnuMFVeawZ57HcCkSZMYO3bsUt0SGo0G9913H9/6mvmcdnZuruWpTyjV9QHTX8vdtJWQltIAxSw3bgUA9boFIxSMcSilyDLo6jJuBl1Va6VVN8/C/HdsEm3VyLvGLmNkHfUBRo4Zz867bsXtd9zeBo4feOABfvGLXzBx/HD+PPstNps51QDjZqOdCXQLX6WArN2HX0qI3bOj28kDb/Vp55NGYEentLHxGhyiWm/Qv9hEefbbZBooTf5GP6I7QdcyVuutcuuXt+SG5+dx+X1P8ZXDD+O075/OyJEj+dbx32DR22/yk73X5RMbLGckk1aq6KOStIfjHcEDoJ1dKwqk4K7nF3LURSZJ86TPbmpkAUNDbLfSSG5abzlue+QV/9mPbbUyv7l9NnttsQIbTh1mE85CqYQo5olO0iD83yXPhcniTlrj5iCrLdZ1K2mw36MbdR+NEOWK3cbcP+/bH2qJ7f6dZNJ9xufaOHbYJSo6TXOoX4aiNHej6TGQTpVnnMNkO5dzZPK2ckhzVDMnrWUeBOtc++JdDisAhjmWwjPIIRaQNvH5vWrvW3DsWjqYWlml9hc5SiTSyiVUqshr7c4U7oYIKdoAsXO5cDeyDa65TMsgREAkzMNbsgxqlHjXhU6wZ1htis7YmYQXZq0GnwH8g+m9k/+WxtgNbipd4n0njncMepvVHEWSmGfa7XUSlcgI6oMERWFDVrok0bUUYRl7D65DBwurIaYUAIdcm8ErLKrScX/IlfGlDjXK2AFSLuVnaUy5Y2ZClsYlVUhRmLMH11CnLYTqAMmNDmbZDWqLB2xVpQRZKvGjE/bg89/8DQCHHHIIn/rUp6g36px49VO8tOZYPr3VCjz9ykIu/ONsLrWDcncp5rgdVuXILVZgwev9fOJ3TwCw/PLLL3E6CxYYzXFfXz+1Wo3hw4cDJpy83377tW27/fbb88ILL5DnOeussw5CCDbaaCOuuOIKHnjgAV577TWqlYRUC5JKZUkJRecCQgrLpJfa3xOivXAIGPcIcyMLYJuU8XZveYoOy7BDGyvstXAOUPvvCli6TjYmPP6SK80u2icl94ypDLQ2VfSSsgHzeVoAfRkjopiqnE85Wkwl6qccjWRYaRixNJZviexj30/uQd+ixZz4jZNQSvGFL3xhiXv2QXvvmxCCPfbYow04/r3217/+lTRN2f3DqxdSg0DaQ9ow4LhpE8+qvdaqMJDyuGTW8DiSkg3JO9AJUCRqie7udotKl1Ttiiq5YiGTJhmyp1VHJGXIWnxinw35za/+xBtvvMEyyyxDmqaccsopAEyeOJJDj7+Gp2fP5fzjdzGe7INDxXMTRn06bTtDIsBFzlwo34GrIIrp81gW9hnypZHzwuv9fve3PPUmK43opmtYyc8dRIL1p45i/WVHsMaILj5x2UO89NJL/OrqqxlY8DZ/PGAD1pw4HNFyDjjSjEF2TjCWrGmRBO/tWgXC2oxqJH+49yX2OOUWAE7eb12+vv0KsHARlBLWmDaGSeOG++Pcf4c1OP+gDVhhXDenXf0Yz7yyiBu/tyuTJ47wyc4ijJB1zsNtcoQOD2u/IIoM4HaL+bDwRqNZeB1bBlk7G7c4smSDgiwgz5xG2BUa8UDWgmJvH2fnOQeIawaQ61wb0wAp0QM1Y1VrsZHLFdJDqY/gOjCsG5lniPNmjso1cSUiSiR5alhimZi5O29mBoNlqg0oR1ZWGHfFfltgqUqRd6sJrd/DYtXvYXv7+qsYv8u+zNl3I4aVYl/goY3ZDWzEZCTaALR7PbJ2a07rEnUVyVG4m+kYTxcScNZjNhFPSGGS8nxIJ0xYKwAgkVw6SxravbljDCQa4cMfHnt4fm2yj3Af9rs9+LX6bAN4c3+MLkHM799ptmsZ0l4T3cjbrOkcW+y3t1Z6/ligXVYR6LtDFsBfb8cARLLtfAwgtiGzNm12EPqXHQOXa6HkwjHEYbGMDg2YN2sPw1nus9bXUfcvhsUDqEV1u6iKTBLd9Gm8+tdXefuthXzrrFv441Nvcu655/KDH/yAV155hWHVEgONFsN7e6k3mjRbLZZddllee+01dpg+lpcX1ngjhb323oePfvSjSxQamTVrFmeffTZrr702J510Ev9Oe/jhhznxxBP5wsdmcvIXP8Sokd2eGVlCGw/t7zmW2Jae9QxxWFnPhXZiVyq0ZIC01mHYp7hXniHOC0Dt7mUYSQkSTAsNeACgw2er7d7HHX3F/Y4KWYiTeWSpASEy9uBZJ1UyVSXJF0KU0J8tx2A6jjeHBJf/5CLO/sbRnHbaaay22mr/1n35oL337ZxzzmH2Xx/n5YdONvddyMK9QQWA12l3k7IBxk6eAD5BqXh2CqAihCxcK8CwgOVyAVocMLXApQA7EiYtY56dWj8IiRi1DFR7GWoKxo3dlU022ZSDDz6Yr3zlcF577XXWWGkiTz//JjNnrscjjzzCZad/nP23X73QjAbPtgFqWQHOfBEIzPHFsddNe6AfLgAWD6AHm21zpKpnjD3kN6y74Sa8POclXn31NU7aZQ2+urN5Ljz4srkml//5DQ782YNUyiWmDq9w5d7rsPLogt3384Ulalyksi3yCMVcYYtM3fzUm+z0XQOMD//wypy+30xEXOwHKXmnnrHhkdfyzqIhfvbVrbl3Tj/nXPGgvUUR3/vsJhy597oF64pZ8JgTNQsDIaRPEPdymnDh4FrYT9y9KJXM/y6/xc1nYVEYP161S9y8/CIYr73GXenCxcLLOqycspZ6+1A53FgIOq02UOCHNPessEvEz/ub6GaGCryIs2ZO3syQgRsTgLSRX5W2Lxhbg+Y6OALT3WPHJA9kGctf8xD9/f0MGzaMd7O978DxBRdcwAUXXEA20M/zr89tA8cqzZFJtMQNASyF72QPTqtkPlPqKfkb5VZCMhJeKC7K1n+3sypdyLK6G12JCj2yCkpROwbVNq89DX67Dur0vs4LuD2crRFlJzmQbWJ3z/AFv33yYUsZobxLSmxkRRJicDzhfr20wa3a3euRqXAnu5IiBOPKQnfFbYuCNis3dw7QviCA4vgtgPbH4xxAAq14W+lpaNd7dYI7950hcF6aNluaQc75OwKFpEBKb4HH4BB60WK/cvbSkqkTEdUqet7bqPkDPHD7S2x+yX3+9KZNm8akSZOoVCrMmjWLySO72GvdyZw16zm22GILeqtlnnvhRb5y5FEsu+yyLK1prf2g8e9Wn2o0Gnzp4C/yzvwFjBrexYkHbcbBe61v/JOd/ZBrccD6h+FDpWwynU3+6ZQBCYHPgteq/W+XOa/yAjRnrb+d0R8mrCjd9r7XSnceo+tr7nhctrgD91IW3+0msmovotxtCiaAcdyQ0khBkgpkLdLyBFJVJdNlFjSWZXEzY6PlJrDPXnuy5557/lv35YP23rb+/n4OPPAznHDUR/j6V3YxLzoNrnNmCVnVsHCQZeBCq0wPmMK8ELcP20RirbyCxZ2vauk+W0oM6LI+skgJEycjRk6CWj86a/HTax7ioC+dTxRFKKW47eef562+Gvse9gsuvfRSLvnpT3lh9p956bovUy61B5F1vV74AjcadoyzTGag122zmQvYSD3YNGOgHf+cA8/cd4aYepxJbvvxj3/M1VdfzbOPPcBL390J4ebPYF5Mc8X2Z97BCiO7OH3rlemKIj9vuXlAdiWQSGQ59gDZux4FhJAhZiSiK2b4AZcz1Mj42Wc3Yr+Nphbgz80xScQNT7zOsb96kr++0eevywYbbMB+++3HBeefx6bLJ/z069t35AI5CWXJjz1t1wfa2eLQtg8sGC776MES9Q7Cwh5h8RH3tz0GI7GQ7cfgvt8xxoHkzJNjYEgoF8lt5e0LjUByqRvmHqha6m1um4ubCCnJbVJ9VDbzfVZPyZo5UgpkIonKMSo1ZFprMEVKQdJT8jrjzDLKYfErgMXNlOm/eZiVVlqJKIo45JBDOOSQQ5Z4bv8T7X0nq3AX661rr2Dibp8AQMaG0vfAjMRQ+alCZTnaZkk6N7a4HBerlqSEauTkzZzM3vDysHK7braZGcDt2OEAgArwgwMEOuWoY2KmkC74UokulA9FAprb1lukmf/b2GHwncpbpzTygum22/ps0kT6VaADtuTasMIOzKvcDyyyHKMGjR8hkUD2lBCVsrVXi9G5GbAKP8Mi4dA8TJkHxdpdk1pWgO9SAICttsm/lwrviNG28HD3y7HHnaxx2JZgGztlJx3AK5gItGqaMJYPUymvpaOVoust8rmDhU6qpwSRgjfmQTkhffod8kUN1opL3POpjfnWbbNZ2FXipTfeZM6cOf4Qf7LfTGaO6eWsWc9RLpf54qGH/YOeb8LFI0aM+Ifb/TOtUqlwyaU/Y+HChfzyl7/g0DNu5rV3hvje4dsGINQN9h2MvNXEGVvBVqFpdJOBlIbtAsME+8E/K5hhYVlbgPpQsX/vzR24tNj9trFvgVYcbDJh1DHc+UViEFpUqsgydy1KjAtBqw5pw+yr3G1eL3cV22hFszSJRNSJY1O0RFZTRlcSNt1iS2764x/ZaqutGD16NB+0/xvtuuuuI44En9tnQwOEneY3D/p/qB2lVTB7HZEM7SwJu7v9c6CbDcO6huBXKWhkS4Aaz0pmmflcrW5BbAMxZTJkLfRbLxpt8+gJfHafjdCtOod9/TK+c9j2bLn2ZC699nEAhg0bxr777ceXvnQPv771GT6xw5oFm1mrFxKzWq04P1+uOAB8tbp3TdBNs10+zzh9+Kgpdn6Sgglju/y1Pf4bx7HnXnsza9YsXl5UY9qYQO+tlHFbTBW3fHojdDM3zGQSSP1seF/VUkQ5NqQVljzqSorCXamZF3QzB2nkf3cfsy0jywmTeirogVZRaMnOj2/Vmux9wT200pwoisjznP3224+9994bgImTluGZV58rFg5u0eDGm7B/uOvV6aEfRh47fZidg0Uo/Quq5frXO5tSgAy8+wMZTFg0xGmaa6nXZftcqEQWLlFB0S6XIO70xU5OkQ+0yFNFVrdJeDZSKKQgq6XkmULafhB3GXyQNzMia7Fb6jFYIaul5JEgKpttnGOFiATSSVztaT788MPvOnP8vgPHvrnQuBVw56kitgkxWhXyiVKPGXD8jZCCPEzKc51DCmJ7M1WqigcP0E2rT45MR5cuAS1wpvDV9HKNUoW2SiSWKPOrNoV2wDUEw84dwrVA+0wkLYC27G3NJTpZsG01zs5CRVN0epM4YVaCziLNAWAdSCwc2CVVqLQFUiB7S7beeeotW7TbZyVCSG1X0LpNxO/O3T1cDjjrNG+XWeTaVDjscAjRzcwsCqQF2KFEJZEGjPrM5GZ70tjSGGLHHCtVgDhfdlmZCSIsQWonNeomPKUXLbYFUIzWyg20cni5WITUUtRAv10oSJJxXayrqly310yq+2/Cc68tZJWPnufPMUsV3QgOnLksj77yAo899hjrrrsu73UbNWoUhx12OBMnTuK0n1/GxmtNZtctVy4G9U4JkC9FmhbMk5sEwrCgUiYZSKvCIsoxzU6K0ehgijuLtoTymHDgd0xKlrU5ivgwZqqK5ErsMdXq1nIrDs4jWFhpBdVhxqmgVLU6atunhETLmExVaebDkEKRtOYj8hY9sp9mMoGzzj2K7T70SY448kg+vs8+bL311lQqgab9g/Y/1wYHB7n++uv44ic2ZfS4UUUfbVlGwgEW1zcdSGo0PEvc1kdt/oF/PVUGGLtkKcv26ab5vAdetlKaX/w5dhbMoryn23z/gvmGdezpNZ7DrQaf22llPr3ZsWair9WZOtaArvnz5zNlyhQA3nx7sflOp2cGz0j7AkSOdAiLQ7hFad36CecatbCOHF6243zkLSndj8zh8eO3p5nl7HXxA9x5xx1UyiUO+eUjnLDTasxcdqSRiwUSR+fzbiKROcrK93Su/RxtagqIwtkgaZrEOzefOJu3XPPka/3cOHse+6y5TCEjdNFPQI7v4TcPv0PLnnuem98PPvgAO+20E186+GAWDwywzNhes+9SIaWAQpqnlY1ihUW44qi4fwGBZSrIqqIAiPs7lE+47whBd0g6QCCTUEVlRQeMpfR90wNjZ6XmC3vl6Jo2UeHAAtdJJTwuSXOywRSVa7J66snGsKlMUepOiMt2kdTMqS9oWCwVARlxNQEkQkJ5eJm0lpEOtojKhmhMLKHpE/TeO5vj9y84jkaPASDpNh03rhSVc0ILkbSWmZtnb4QDybllK0P9r5DCa2iEBV8yjpAdjgmGfbUA2jGkSvvO5m3boFixBgVInKwhlDLo1HoyQzEoALK3BNIxC7JI0rPgV/U3Ec3cg8U2xwkLiMkNkGvTOpcksmK1W72FU4c/loGWXY1nXibiJRJJsQgxx6IMgwq+uiBNy6yrQh5irPQUIoq8q0WbRjlk25VJGtS1UHcsC3lLqP2WymSEm4NZinOBxPshu9ZpzA+gRDERuvdqdcM8OPuaobTtHutaWrDmiUSnElE21yZf1CBZphfdbLLimCoLr/0itzw7n3V0k2XjGNXX5OztVmOPXz3GueeczaU/+/kSOuP3qm299dZcfvnl7HbU1fzyxN3Yb4c1zBuKtnBiMRCLttAx2O3cIgMKqYKknTlRWQFyQxauZS3bOsOSUExAIRvsnTYCNhlM9n+otfPHFvwNZnSUFsC731oZeYeTgSRlu+uEVFUpy8XE0tpb2fcSUWfq1IncdMf1nHT8Sfzwhz/klltv5cQTTqC3t/c/c4NsazabvPbaa7zzzjvkeY7Wmp6eHtZee+3/Wt/5v9p++9vfkmcpRx24eZH86fqM6yNhP5VF2FyH9oEBIKJWt2DWgJUw7K5zQ174wiB+bJLFM+WcI3whBlulrZWa30pDbbAAsVlGnGVmQRnHTJ9g+tuTTz5JmqYkSUwllsVzBn6Mc3aeJoonEHFw7o4Nd4U2Wjn5ooaNlFmpX1TkvehG5ue61cYPgzTnmB1X4ZBfPsKHPvQh7n3gATY983Y+t9FUztxtBiXnBZw7NjOCcqFdBkwCeyA19LJCKUw01Bfpiuw+BIuU4iPn3807/XUuvPk57j1sC6aMcNEfSTSmSjSuh42njmC1KSP57n7r8NHdNmTsjuey6qqrMWvWLBbb6qbLj+suFkfOgSJkjt045QBpeN0auUlmd2SRu88hMA77me9rS1HBdkY6IQDGuhgLvZ5ZLgGMXV2CMErr53tbstk7fjUz8lqGsgywSpXXCjtcEpcj89OVkFmPY4DysEKO5xLwotAcoZl5kB13xWS1YP4ANO/dGPa+0xy7Nu+6q5iw677M2XtDeuLIU/hRIr1mGPAg1/9t9a+6kXkXC5lIMvtgJ112wo0Erf7CzicKHlqZyKLaGxRFLaz+6m8ZWfvSyhbAepeMrsQwk2lObjtLniricmRcN7oSA1Rd3XHnK2w1wO58/PfY1bRLmHNJCu68RLmQgfjP+HMxx6H6msaBwg2gNgkiBIL+syFT7MIyVrrhvzNMxOtIQAx/h4DbfV+xKBHtn7cDRRjeCx0u2phs5zLR4QAC+CIooXTFnYNPSnDm5tgFi91GVCJkV2JCUrZ6oE4V+fwa8cQe4uWGmYG7p+y/P59XM9ZtAy1Ic3711zf59E1PcckllzB27Nil9p33otVqNS666EfcdtvtbL3+NJJIMOfNxQwMNVlnpXFsPXNZdvvQKiy/XHCMDiSHA3eo+3b/h5q9UDqhVJFd3Vm5EdrDkq51aqId8+JkFp3aZ2w4O9Smu/cdk1MpwsHYpCxR7jaWXXGCkhWUTtCYynpCmO+IdB0lSgxlYxjKxpKrEo89+hSf2n1XkjjmkEO+9G9HBOr1Ovfddx933HknTz/1NGnaWmKbXXbZhc997nP/thb9/y9t7ty5HHroIRzz+Q9xwtd3M+DYJaiF8iCns3XRCWdrFup1w77qCjaECzHXz2o1fLKUW9hZ1tiD8kYT3Uy9i4C32OrqKhadrbRInmst2RcOOGMWV9z8V7TWTJ00ktvP3Ytle4NCJUoZ2dzCuhnPE2lJmGIO8GQGmJK/tdQTIGGRLcBbgobRUN3IED0Jn77yUW56bhGXXPozbr31Vn70ox+xwZRRXP+5jeiRJqFdD1kP+OCzWhkppMo1KstRqSIqx35OBDPPyeFlMwYPL9Nfa7HHBffw+OsDnHLqaRzxla+w/tSRnL3fTNbbaCoiSYprVimbe9HTjeju5kvf/yM/vfYJ1lhjTV/d9PoTd2LHzVcuZGTuvrvxadDKwUqmBoCuNwtwa4Gh7FpKsnhY1MP1m3Busk1Uy7SVoXbf29PT3oe8vtiAaz3U8C4TPjqtbNW6kpN2WPvbwKJV1VLyWuaJw3SwRVq3GCmWfo5UmULG0pOOYTKec6boGl2lNdgiriYei+WpQqU5g3MHkYmka2yX0SZnudclD+qc6b9++IOEvH+nebeKvTdkeLfNFlXK64/zVBFXIh/SEIn0oQMRCWQ5WiLxTNfSNkNqKLIxTXjANHezHUh2QDAEWHkt86JzB9y1UiR2Pw6Uu+/yADiQQ3iXBpfQZkFm3t8stF6JBa0hQPSJdMpv4wuWuKSFTpAaNJ2qwp/YNZtE4feb6/bECNesyN6tUp19XKh1djIMwIB3m9hXFPzIi3OwLTxes8iJzIMeOFo42Ujo9iEqcZsPprv25ru1X1G3DV7QpuE21yT37LrsKu4huUINpv48wkqM8bLDkF0xqpYRja2amvKuznx/02jscs0Zj77MiQ/P4corr1yqD+t72bTW3HjjjTz55BO8/tqrjJ8wiWnTpvH888/xzDPP0GqlbLTGZH7x3d2YPnmU+VB4/VxzLIvTDofhwUazMKV3A36Q1AQUyUy+n+hiYReWve4sFx4C8a5q8Z2djIxNavFaUOdSUnJuJSVTYldGiFIXdA0jl90IrKQmr4OMUKJES/XQyIczkI6nkUkGWjlvvPIyJxxxCI/ecxf77LMP++233xLAdeHChbz22mvMnTuXVqvFlClTmDJlCqNGjSKKIgYGBrj88suZNes2Go06MzfdnG122pmZG27MStOnIpMykZRc8rPLOPO4ozj88MPZdttt//1O8D5vWmtOPOEE3nrjRZ69/Ti6qiVb9EC2sW9gwXDYjx24deAkcAfQqTJVP9PcvJZrxDBTQljXm16aJntscpWzcQtZP7fIc3Zbbs7qKaMHm2Z/YUW+sNnEqwXVbi649QXWW340W600ii4sq1mrm/ybhQ3UwobJJxlVSH9EJW5zOAJQgy0fsSuAlol89tdarHnarbwz2GTNZUYwdViFNScM4+hNp5PY/Zzy4Euc99Ab/PKKKwF4+umnOemEE9hq2giu2Xc9H+1w0jSw808AjgGvd3XeuElXQjy87Oe1RxYM8IUbnuL1xSnHf/NbrL766tx0001cdcXlLOzrZ93pY3norN3M9yllFiAOfMYxtVbOudc9zQ+ufJhmI+Xp83dnygrLtCVk+zEqWCz56x6ATFdsrKgWG0SnOx2X7D3zc1Ewl/nm+kSWmzEtXLi5RD0XbWi1UH1NH+kMj8cl2JnXcuNSobSvbpdaYk5lOVktI2vmZLXUmxzIJKLUY4qiJT0lL7cQUhB3JUSJpGkT1WM7R5rPS/JmbvBSAKQd1gIDnKNEMqhypv/mvQHH71tZhWff7cOishwZG6DYGmx50bd02t9ybDIrIxOSKTx+o2Li7EqIXSKYZZwj791bAGk12PId2FmjiK4EEVaTA/PwgpEdjDLgyCfyVQrw58FeYKNiDs05PUR+he/YaSiS8toAapC9SxR5xtZfq+CB9aWtw6YUIlFoe41UKBuwA2ZotbaEJVtiAyO5MmxAOfZyE28JN5QaQBx4VYogRCcqsWdufSlpKIC3A8BNA/xlJSnkK6ksGHpXpMUCYLdI8K+3AaYAhOWFRs1pjXGLEGdZ1MxM9nRvGdFtwXIj9/c4Glkx5uj9Vtc2z7AMeihD1VJULfXyn/He5WdJFui9bkIIPvKRj/CRj3xkifcajQaPPvool132M9bb/ydcfeLOfHj9qebNgMUQUezDyd4wv1YvtJghQwzFxKNUOxjGJbiaxZWIInPvmpl/PsmCkGUIyJ08xlXzC9loC0h0GoS2Q8ZaKSjZbeMErXKEyohK9eJi2LLXsiIpR4sBiESTNK4yvFRlzCrT+envbuDn553J2Sd9h9mzZ/Pxj3+c6dOn8/DDD3PLrbfy+GOP2cORlMoJjbqZWIYNG8Zyyy3Hs88+S6VS5oivHcQBB+zE2MnGDquRDUcR0bKVJg//8sE8fMctXPOrX7HxxhtTKpV48803eeGFF3j88cfZe++9vf70gwbXXnstjzz6KNde9Bm6EgMaDWPbKPpD00mrcsiahZ7T2Z45wNSyC14vL2gWuSDdCSyuF842XYlZvLtxb6hhk6HrhT613jTANZDYiUigFzaIRlWsBrgA2k5K6MYmMXo4Yyplvr3bagGQM2Bb1TLDAvcZ+zXZWzKyMDfGBnOPbuRtY6aqZZ68MXkXGXEkmDdgmPQJq2/Aq2/P48a7/sJz7wzw0x3XJCpFrDC6h/6BQZ588klmzJjB6quvzmFf+Qrf+973uO3Fd9hmhXHtjLOVa6C0t17NailpvZBHuvG9Ob9OVku55KV5HPfIHJafNpXTjv+qd/vZcccdefPNN/nd737Hy/MWk7+1mKi7ZOZepVF9NrGwHFFVmq9vviyHbLIMfRlMWW5sMTc2mt4j2INqKdBDwX2yxFghZdAImbQTNVKgkZCahHU12GqzthPlCFzelMMSZccYCxstaBWg2EkzAnJB2Wh4IasoxlGfl2SlMrqRtQHivJGT2SquTk5RHllBpYrYRtRzqw1uvL6YyBobJFZioZUm6SnRWmz6e6kn8a87hjhspWFlsroBz9Ien/xAc/zvN7fiLPWUSCoxRIlPEBCOFXbbOmeEMOkrcFjwYn1lrMjCIhhtumC7XZFsF0FvqfhfFgVFolGVAuzZ/UhKS4DiJVwtlGovrwwFMLbyCA+O7Yow3Feb80OHltdrn6Bg38IwNICy5271Z8I93FBIFty+y6JNKiKgGCxSgShF0MjMsVu2AawsATwA127xYd937hQitwkXQfhOdhWyDhOSy7y+CyktEA+0YWkBivwg7GQeUMgjAtlJWA9ee3Bt9dtSILpj4rHVwslkoIUeaFF7a4jedceb/TiJxZBJONQ1a6qe5qjBloks2JV3lwXXtVqNUaNG8b/aKpUKm266KTNmzODUU7/HDkf+mt+dsDO7bjINaHqpg67VvVzB6CxbhTYuSLwD8FUbXTShw1rIsR/CRmjCxZ7G9APltrHPv7NqMsUDOnTHTlrRIbnQWkG9aaqalRID6nu6DbAomcpkRmdcMc4VYEDTUB/ECZXuOhUZkcfdZKpEl64yqgLf/sZ+rLf2NE76xokcc8wx/js33HA1Lv3p0Wy6wQpMnT6ZuFTi6Wdf5+WX3+aue57j6b88ywGf/gqf3G9Dxo8bYTXQLwDQiEcgyFE6ISemO57Pxed8nA03vJtDDjkErTULFy703/Xqq69w1llnfyC5wDCXP/vZzzjqU5uyy5arBC409XY2GCwIFm3VNfVQoy3C5xfPbpy18gKSCP1OzYBMnzMh0Kll8Ow4rqxtnAOjfgwLvGR1SZokuEZWOBBhQJ0OnhdRLqMXLUZ0VwpNchyj+2qGie1rthEVKMN0+2ev2YFM7ILT5Z2oWuqjX9HwMt3dMecduS1fPvNWRo8ezaGHHsq9997LaaedRk8Sc84Oq7P3mpP49q2zefjhh5kxYwYAG2+8Md3VKn9eOMi2lYnmuVYCrSQiwlcSlZEgV2ZxkFRj8kyRLm4hY0krSikPK3PTwsUc+8hL7LzzLnz2s59tq1L5yCOP8Lvf/Q6A+477MEIIs1DpL8YTwFubiXJE76geer0zhTD9I8sNEMbUOtD1ppfi+c9H0ievA2DHJX+vuy1QHmghKjH5gElcU8olxFuolmu0TRAUlQhdbyGGW2cLr323c1etVtyrLC9cqPqbASjO/XipBlte0pkNpmS22mzeNCwx4Bl7wFivWW24Y3mjckTezCkPK/scqbSe0VzcIqka8rEysoKQgsYi86xk1qkqqkQIKYnLdp+paovI/y056rvV3r/g2N5wWU0Q9gI7VtizsEEil7BSAA9qXYjMtjCRrb3YhmzXCrv9uGp1rllnBf9JB6odUwzQU7zn5BKdANr7TAUdxVWYE3blTNWEqMVSrF5E2X2HPbayLABwGAoKnBnc8bfp15SCOEKWSoapsEbh3nvZMXcWaIgYA3SCY3ZOGq7AiJCJGVRcwmMi0ZFlRtJAG211TGBXvImRXchg8eGukeiODQD19z+QNmDZ45YFx0FpbH+fbaKHTgv9cVtkwTIsjuGWvSXDvMyr+UkwW9QgGVM1wLhV6Jfzd+r++6LhZTM4pcpLbtyAVLeD3WOPPcbkyZOXuKf/a62np4evfOUIjj/+eD556s28eul+DO8tF2DCMRqdyXAu9Of9X7UPP7fbB7UzHkQCrK2gdjpzp8F3UZtgoSmiIFkwnFB8UgwFOAe0lX2IclCtLHQ/UQocrmzVQTSMJZ0qzlEP9YEQyKRCCSjJCKIYIWP222U8++54NrPufIpXXniFzTZamZVXXQ5Khc5ZRwkrr7oaK66yBjtvv7ZhpktVtJAoLZGqDo0BaNWp8Da61o/oHWOqtQ0OsNKwOjefvSe//Pm9yFyz1XprssaILh59cQH7/OIhjjzySE477TTKrrTx/w/b3LlzOeXk77LZjMmccug2FvhkBhgPDhXMnEuKI5iwUwN6lLXAkr2lwg/W9k1ybcCjIwispaWbk2QSQS0PImMtw8JaokZUImhlbcSNd0QaaBkXokC6p9PCJlF2xYZ97E5QC4eKpLWBupV+tXx1s3Z3CJM34cZ2zxZbaZuzAlN9Ta8LFmUjaYuXHc6XVptE/6vvcPwVV7BgwQIOOuggDjvsMM4/7zzeHGxy6T4zWX/SMG6/6y523313Ro8eTZqm1BsNuoSEVsFOIw14deAsHl4mn183UWCliIhQ5YismRPFkicWDXLwnbPZbNNNOeigg9oSUhctWsSZZ5zBh5Yfw2nbrMK0SqkgUXJtHaYKiaGoRIX+G4rIQKNproG9bgZ4uvkkIKdQbew6DdsnLC5wMkWRSPKFBjSqIC9HN20E2Ls9mc/IEeUiyTksMlOrt0W83GJKLawX0VJXA6GZFnlOSpMOpN6nWKVmHtK5RmXK14hwRTxa7riloDysRFrPKA8zLlZJ2eR6ZfWUNMArzhLX4aakp8gPccl5IpjjzXMmyZo5cfzeJeS9b8Gxn6w8SAsY1LDlGh8yD6vYQbtdmjLheq97DZLIAHxpSggyQM2/bcxxyFAHn/XH0ga8RVtupgAP/pYop5zIIknDaTidsD/MZu1soS6zA8x7Js89cF3VIuQdbC/iCFHNzYoZ2q+ha6XE+AM703EMs0GqwOl+7XV1+jXdzOy9C+QZ4fUJZB+OMSaxq3O30EF6PZXTWoUsPvaaugRILwepxH6l3RpMKfUkBfOeyMLdQ5miK3JEGTWQ0nqhj6grJp7cS27DpcmYKnJUxSbYWa2ey662/UM1DetjatAbbXupJ0FLyS1PLKKrUmGTTTZZ8rr+j7YxY8bw3e9+l8985jNcd/cL7L/tSjaxSAesVRQAY9svPNgoitfoNDf/BwVrTOa0BQnWFQYl8M+yW0wqjXYafKdJ7teQNPwiWZQjw6Z1Vkt02d5g2EMLoH0hmNC5IA5ecy1021jYFyxCI+Ms0FVFy9hUN0vKbLvRVNhydfPZ5pApNmGr8gkgaS3yoFvnGSJtIJKKOZ6sZcpyD9lttEL3z4NFb0KjBo0mM7s0a390LTNJS0G+qMEe263ErRuuwI5fuZrrr7+ej33sY+9qv/hfbf39/ZxwwneYOKrMb07+KHHWQrt+OThkoxmZT0CGwMHHLcRy7UPhCqvHzYvImmpm5PPryK4EUY6IxlQLrWclKrxlG7kvrABWRoZ1DbDjTRENsUApSFDWFJFCUcEelwGZztceKdC1ILzeKhK+TN5E2Yy/5djngQAWJGee/HHMt8+rqMTIEWXDoDYz1JvzOGrNKYxY0OSrt91Ks9ngqKO+yogRI/jB6d9nsx/fw5X7r8d9F9/Pjy/6Ecce9w2SJKG3p5v5/Y3iGa8kqHk1GosadC07jGxhg8bbQ8jYRXfN4c1TimvfXMDjby3mljcWsdy05Tns8K8s4dRy3XXXMTg0xI8/uqFxqwglhWDJMytZ7K0Wz7hzGhkc9BXlnCY3lEX6egXOBcJKQLTrD5EAJZBdkQfNAPkiw0BrR9gojexNPPj2ksE083I9oN1ru2XGJD3Y9PsAu4BShROW62sOFKtGYWvnjietWwMA2+dVqkh6Sh7DxLbgWNY0SXMqU20scnNxq20bV/nOuXw5IBxa6cpKUdgrxGuyElGvd+jo38X2vgXH3oEioh3kgr/5be4DSuM8h5GWGWxaDatb4QUge4kWOC8QRYhSVIDl8LhKov3GB5KGzmowbclDYP5OzHGIzvcdm+Ue4nAf4e+lGYeHWa2uhfpL99nQPqgTCJRA2DKZbXrRzv3ZwUzE9rVyglDWlq2Rm/uVK+SIMrrRLploa5ahdlfLG5hLgRhe9rrhECATCYSdBEySomGtRSRQzWDwV9pLOPJmTnmkTehMlZ8Q1cKGX+hISv54yquO8gxR2+n3mYpRcni5XYftwHJ/02cCtwZT4nLE06/1s/t9s1nQaHH00UczZsyYJe/d/3AbM2YMK6+0Ir99+FX233qF9shDaGsUWEYB7ayGYzksMA4H9nZLxMxHF0LtuttO28mIPPW+38LZO0q76LUAqK3PO9eLzoIiisAekHYmPGxxbBaVPYHtkytGkmdmJyqy4KRpQLFrShlP5axlAHSetj1XOk8NUy2l2ZfWIGMDqsMErixDDw0hqiVym2ilAXJF/toAW4ytMmlEF319ff/aDX6ftDlz5nDKyd8lbw5x/QV7M7JiE94sS+wT6FBeNiB7SyhnT6aChKlcoyOBml8vwvKp8n3X9fFofDf5vCGz0B5RhtzoOEO9ZyF7U96LXvaUzPtB5AqbBKpz7cP0OhKmWNNAavp4WhArpLmP4KmBtOO7BLLbMMOiEgcyuLxwM8g1WFCslUbNrxliqZwUfseulLAdBz89YzJJKeILf7qT9dZbny233JIfnHU2xx7zdb74myf51OoT+f6DD3HllVey1157MW355fnxo8/xqXWnMClJzLPaFdM1fQT5/DrNxU1kImktbnpP3Aaw+bWPUcs1y0+bxuYfNtXsluYnvvnmm3Pt73/PyXe/yI/3m1mMJZb5Fr0lQzaFc2pX1SyUFg9YiUTmF+4qnOvt3KGaushRsSDfj11gFhUOWNeU3y5MCHeRSNGdGLbeRaelLMgylizYoS3ZYjqbs27LfZTUL+wsGHbAOE8VrviYq1CXN3P/mjMPiC1Dn6cGDDumtzq8Qm59qJuLW2S1FJXGxF0xUSy9dEIrjQJixxRLQSlMlrctjOIKKRBxsIB5l9t/HRxfcMEFnH766bz11lvMmDGD8847jw022AAwq/l9992X+++/ny9+8Yt873vf++d37K6vFEswtSEo9aL0vAi1E6liG6dTdC3Q8LYVrHD7CkovLq0cNICwfsqdmamiFCT+dJaNdNu4r1xaFr773aGZ9Nsr3f7+EklIUft+wiz+kH0OPTjD9xzIDlm0Tva5k2HLMlASEceIcub3p3NtQ1zmgZGdixsvMzFN5BqS4P543XUegGTMfXZVgSjkHWHBlzZHieCBdUbkxmEkMo4mdlBNX11MPK7bJNRZoB8yP6IcIUdVTHEVZdgX934+0CK1+uLWoDm2DPjGM6/Sn+accsoprLnmmvxfbBtvsilXXf5Lfn7r8xyw5fQgOmLvvwMV2MG9w3cTTJJi56QBoCncTQDDqtRSsoV1RFdiGLpK5J9hx7TIrriIrHSCYNdCBtk15zVLsFjrWCyLapcJv7vSvkKanzgxv131P8BXBMxS87oDzu59rWBgUcG0l0rQ1Q2txpLPdxQbgOytxpwloZ1Ihw+HahUenms0oaMq5AMtRHfM66/28cr8AfZaYYV/cDffX00pxU033cTPLr2UlSYP5/en78FyoyqFVjPLA/a3sLbSqTK91zKnbjGnbPKSqERGZiaFH2vc2O/kFvm8IdRgi2hkxfRvZcAWmDErb+aoXBtHpZoJe8tKVCTvOcYYimINPuJlvOLVoEniUqEW3z1/VjrgjpmUIrE7t+ynk1i4BakDxrlGWembS/ASNkojEukXqqHnvhxe5oAtpnPrggHOOOMMnnrqKb70pS/xzW99m+OOPYZHX14AwJVXXMGDD9zPF754MCed8B3OvedFTt1yZaNp7m9Sf3PQM5NZM2duf53PP/Eya47pZngSs6je4pxzzmH55Zf/u/d+2rRp7LjTTlx94/VctO+6RYQ2yNkx409UzHUL+3j22beYPrqbSBp3pT89/ga7XHg3v//KluwwZRRkhdzL5ZC46ILqbxauUTIqFjItVeh9XX6THbPyhQ1zr4fStqRvUbGRhJZAW6cn75KENPpxJwskLxY3UESsOzTkzhEiT41zVlrL2q51XI78QsQlQCplrW5rKXnDFPDQuSaqxMjYAF8nj3DV7kB5RxFXCS+Uj7Y5hbloe2AN+F61/yo4vvrqqznyyCP50Y9+xIYbbsjZZ5/N9ttvz+zZsxk3bhynn346yy23HD/4wQ844IAD2G677dhqq63+uZ2HiWFgQiTutVLxd5slSoflVCiHCJsvBx2EBPz2bpsOBrjQGtvJKu4Alg6cdoZzg30scW5uRdvJDofbORkIFPv33xsAYv+dQQd0QBb8irOtGlAn8A3PsRN4O8DvtndAOmShW4FkItidgLbJXoS/+RtCffewOaP1QNKiwDga5NqEIK0GR2Pt2Jp5YRcnI5MsmGcmHDSqQpxEZoJb1CDqLRkQ1pV4j0hJTN7IicabRAmd5h4UuyIoull4dTb7m+SZ8rY3AM/nLe5+qx/g/ywwBth222158IEHOPCH97LzKuMZ0VUqJp8wKcT+r22SSpsbSCBBca1twepekwLRU4JRlWLxKgWiOynsj0LnCsBndnc210/DDGr3PMURIgks49qSWGXxOaWgXDH/uwIirvqfjI0dnFQFSI4tA5wVRU8M0MbsIykZiYStnqaHhszxlBLj0+yOoZS0F6Bw56M05XXG+bB99uYg2euD3PTq2wD/lQqM/6321ltvce655/CXvzzFF3ZcjTM+swFd5bitApwasFZXrghCM/MAQzWaRQJtrXgfp91MrQWl0qS1DK0UcTlGJAY45vPrxrbK9e+OcLdzDmgN5sS2NHKeKkrDSwacN02hkWh42WtdDVhNjVZ4oIUvRuWJHu2TVnUz96yuShXSR1EsIEH5ZFY9lPr503m5A1621Ja0Z6WAohIXzjxjqkRjqqjBFpftuQ6brTyWwy//IxtttBEzZ87kzLPO5qWXXmLu3LlceeUVzH3tVU78zncYrNX4zey3OX23GbZEdETXssOovzFAVstoDaUc8PCLvCliXnxjkFqjwRabb8bUqVP/qT4wceJEGmnOl379OOfuO5NSKebh1xZx1YOvsMP6k1l/xXGM6tWIRpNaPeXISx7i4tue5zu7rck3dlkDUsXZtz8PwG5n38nJO67OkRtN8wy9qhUMv1rUMuA1VWg3x0iBblCQNWHOU5oXhEAqwDmHWEmMGytJInTaMn2tkUEpQi9stEVOQylamx7a9m3X55zFrVaKvJEHUorcVrmTbfZqqf2cjCWl4WVaVg/uwLKby8J5zTmEiUh47XFncyDdWeqGFrRFAel3v/1XwfGZZ57JQQcdxGc+8xkAfvSjH3HDDTdwySWXcMwxx/DYY49xxhlnsOqqq/KZz3yGRx555J8Gx2LEaACTkOe0OS5ZLNQWh4l3LpM1ANZLszrrnJjb/Idd8xOlWLqeN2xOm6hCQC2CyW0plmrhfsLJHgK2N5gcl8byOs1wFPuEMpQyYWOXoe+ZtWRJiy0HINzvzrB5eN5hXXj3fgieoQApfytrPnQWcGBeCqNj7twuuN5OsiLtQCFHlNFD0kxqQYEWv0otR0tWEkwkclS1cESoxMaODSOJcEyL7C0VtnQ+0TCYVJRjB8yKWeVBtUMpELHgwLuf4/rXFzBuzBiOO/74pV+L/yNt2LBhTF9hBWbPnm3YJSgcJzqZeldMpZa2sfeGQQgiOHYiFsrqhV2yUiB/8qVjnc7YAVlYMroSLvD8b+G3aQOeYeLe0vqps1JyC0DHHmtlgK90TDJAbICxkAYYq7z9+BrNoNJZE2oL/ddo5/ABZptq1TyvzYaRKdWtrVwpMYmEyoZjRwxHz11ANGEYck4/cniZx243+w0z+d/P7ZFHHuEHZ5zO6J6EW07aia3XmICz4SIEDS6CkeYeZDitO6kt32zH6bAwhWPkkl6jzSwNL+ELJqXKl9qVFeMy5C0obZPW3kqmpmKYK5aQ9JpFuLb+58LlZ6ii6mvSFZMPtMy+U8cUmggo9jMq1+T9TQuEzPeWu2Kvpfa6ZApNqmMy/fVwSXnmiP24mA9kXu7mbS0HWiSrjYNSDd6p8aVdV+eXD7/OH/5wLTNnzmTy5Mk+0XiFFVbghBNOYK1RMXnPcHZYfrQH/KqZIUeUqdp9zm20eLp/iOOOO471118frTVJ0lFA4++0PM8pl0pccv/LVJKIbVabwP6XPkSm4Lw/Peu3664ktNKc1ALLtScMtx7Aisdf62OnnXbikUce5vHXF6EGJ5tFfeCG5aQThqnXhhl17wVa3qgcFZHsQOZCJM1ixxkJOBu3SoRKLQh10o5a5rXieqjl3SjCCCoEeTa270jbL1Qw50XliKyekmeKUqWEjKVPynPyirxpEvBqb9eIKjGtwRYi18Zlw4JcrRR5E2QiDeOsIu9pLGS75NWBYpeQ7yKzXtIq3rsx6r8GjlutFo8++ijHHnusf01Kybbbbsv9998PmNDHH/7wB1ZYYQX++Mc/ehC9tNZsNmk2m/7/gUETGnOrJt8plDaTB7IdIIfNrd6cSN+xzFEwuRYHbd6TSwGMZgPa2NhOttcBuRA4ekZWBObyqp2ZCr9DKfMVzmHCvRb+tpO8lhSTfdQhr3DnIiwoUPgiCOa4O45vqefL0v93Mo22RUPgIR0eR8hYh+cAxfllmQUUaslevJQFqWfq80DqkCqzKrdMibH2arcKg0Lb6irWKbcit5WiChZS+zCY//FhTssGpS0bejRhJpXmViNmPCD7++pc//oCVl99dU466aR/abD/X2yzZs3i+uuv53ObLk/LsuPSAgLXTAla5zld6CeF1caKJHw2Ams9yw4vrWCNYUYMk0KeQhJk+HdKJpxzhc9EL55z4fTB4SKz89l1f9vjawPQUDDGccmAYa1ssp2VU8i42C7PTMEJMEl7Ltt8aKhw8uisoKWUSZJyx1tKEKXEAGT3Wcdm2+IRbnzRqaI8rMT4sWOoVqv/4G7+32+33nor5513LjvMXJbLv7oVvcL2PcfmOd2nDXn7RF0lvLzCaYh9gahcefDsWK9Qg+pYOpEY60JXHtcAptxXUE1rmanAmphkpZIUZIOGYYucrWUz98+QH1sqsffxd/vQFnA739+iSJE5ntZgy/vLxuWIvJYR9ZY8i13oZvPCVSk1QE01cySFtEigfBTOSU9wdpyWIc1eWlAwmItbfOrDK3H4D+/lzTffZOLEif5arbfeeuy22278/ve/Z9dVJvCND61oxtnx3eY7G7mv2NffY56b8ePHE8f/GpR5++23ueSSn7L9quPZYNmRbLLyOLY7646lbjvUKJLAvr3z6uy4+kT/vK88oZcbb7wRgJ7JU4rEuYChDZlTd03937bPxOXI/x0lEqGKOcVHPd0iLZHoZmbInkYG3Ym5XwFLrBpBMp5ddOta2lah1/UR11wynuzI0QoLk4Fxl1CpG8tNn3OFQKJYegkGMT4J0fADBnCHLLKDIoAvnuaPJ7XR4UiaxVokELnZZvHixW3blsvl/7jTzn8NHM+fP588zxk/fnzb6+PHj+fZZ82q7Wtf+xpbb7013/jGN9hll1346Ec/+jf3973vfY8TTjhhidd1lhfhHxeOAITUOL/btupqDhS7bN8kmOTCkKybBDtBbdt2f4MBdduEvx0j+zfBb8ckHH421AqHjGnICIf7iYLvdsx0aF2VZ9bCrAAHHiR3nkNY2nRpEgt3bp2Dl7t+pY6M205GWQoDdkMGWsp2OUZ4TbIMpF7yPjngb7fRWG/JZmEw75vXngfnkivI7QBRM0VKRMAAGJeNpE2y46v4NZ2dV1HPPhrfTZwOkEth7WtMuc2fv7GAJI457LDD/s8DY8D7Mv/k3pf4yb0vkUSScz82g6mjutl4+hgqQvhMeRNCLICHJioyvvOw3woPjJfq9OLsGimcXKCQb4jILsCyvD3y4fctl3yeWqqolifkks+vGwvC51eK9uIhrnV+pwoKSihlQLFSUKuhm80ishQ4Y4ikhK5bbaz1XdZpy1jZDWUFqHfMc8Nmwdetc0eWEY3vJl3U4PePvsF6m221REb/+6099NBDnHfeuRy41Qpc8NkNka2sHRw0svbCQJXIO3uE1oHKuuGoVBF1YYBlVzG+5anySbwuT0FIgexJyNO8CF+nCmmdKEQ5RtYy44wDxsEnkkR2jJGJNAlSlgByf0ddMdIy1nkjJ+5KyKxtl7QJcy4xPLQcLSq95uRSEFcisv6m/z6RSBa/sphST0Kpx4DmrJmTdMXeSs1pRSMH7GrmddXMiboMSDbuO5kfh5NpwxFSsPdq4zlzwnCOO/YYTvruyW0WlQceeCCLFy/mpjvvYDBXVJKALMmttVsSMXm6GVvmz5//DzXGne2ll14izxXn77wmE6slLrrLeISP7y5z8gEzGTlmGGWhef3Nfm74y1usOHkkW43pYtvpYwtrPmCT5Udz/wvvAPD5taYUFnq5pjbYYtac+Vw15x1WHdnFFsuMYv0JwygT2WIa5p6B0fi6armuSFls9cm5dbPw8429/s4nWYLR/lrpntfH28Q4t/BzieZhzk6eqjZQaiQRVk+eGfAclaW/51E5IopNJLY8rERzcQtRLvoV4PuIKwgiwZBAqpCoRk7KqJR3GtFKeuY4jNqH+MyNwZ1Fi7797W/zne9851/qA/+o/dcT8v5eW2655Xj++edZuHDhP8zUP/bYYznyyCP9/wvvvpZpOx8AUNjBBM4QolxMrEvYu4HXFLcxna5JGwZ1f0M7oPt7bhGhRtF9Tukl2VL3mc5tl/pbguz4HLYj5apgigGRlNqPOUzccZ8JmF7tJm6/GIgMi9LqkDh0XodO5tdt2wmU47z92oQJgEtoPjvYZWg35ffh7ODvpV0XH6631kculIVld9K0CCG6lgeJgKlCD7YKrZ3zuAQkMbhCLkE1KdkVQ3dCFJTzTiZ2k2Am3CSRzH5rMef+5XU23mQTJk2axPuhrbvuulx22WXMmzfPeIv+4AwOvuZxAMpJxJbLjWbf1Sey87SxdMkidO1CszrXVjrRXkLc3B/LhoWLWGWiAQXosYVxkqWE40pJu9ynY2EXPjc6D7SWEuNWAe2LNpew50CuW3wqvaSThZTFwjB8dhpNI5mw1dXaoi5OH11TaNls25duOocKm7keLjKz3EgGXGJpLQVbmObKB15mbn+No9/nZaVffvllTjvtVHZddwoXfHoDxFBWOBHZ8cDJHvwitt9GKbpinxDUZhOp2u0gHfiMEsOuRpWYqCum1d8iBoRlpqOuGD1ofZFdyL2WonJtgHMzR1qtrk5zsmbmmWiVFTpWmUTEZcMSx9WE1NnI2THTSNvN+ObstKRliSsjKzT7m0jLUsueEgy2aA2mxm5LacrDyga85MZmKx00fStvZsTVxB+LOzYjK7HAvGkKSXgNq9U3xzY5bdToLu46fRc+fMx1fO+U73LueRd4WY8QgpfnzGFEOeG5gTrjljUSSUom4dZINiImxRHTRvXwx5tu9En8/0zL85xnnnnGXKPFLdK+lF8/Ow+AZq7JFrc4744neOil+dQssXHrkdPYbMwwUPgqcgCfXn9ZfnCzIfOWLyWQax6c28euf3iCRqbItWbZKVOY9fx8Tn3iVZYf3sXdO80gUZBnpu/E5YjGoiZJNS6cIrKcZr9hZLVSyDgiyjV+FIuk0ZknEmXHRGUjC05GiNI2YVMV/cz1I8v8mr5irdaCEtdZMydvZESVmKyWUh1d9f7RIhLk1pate0I3TVvxTiYRaT2FzIzbkZPVKE1kv1ulRelvmUgPQA0GMzOokVRE7ZjLzcX2+F977bW28tHvhj/7fw0cjxkzhiiKmDdvXtvr8+bNY8KECf5/KeU/ZWHVSatnPcZAXwhjaeNZYKdBDJr3vu2soBUyQK65MIVU7ZNqJ9tUSgLJQIccY2mAN8yc94yoXrp8obNYR7ivpUkaCCb31Hy+beJ3mfIhc+0+60F/AOqVbmeNO7+zVTxk/twdyJWiKAYhpZnMXcWhpUkpOoGLA9quVGsIOv4WU++BdlZcVwee7EJJt1pFNqwNYTpDeL+dDenLStymyXP2bNpPsHkQSrWvN4RJ2HNZy7VA0mMZnj+9+A4L6y323HPPpZ/H/9E2cuRIRo4cCcDoUaMZMWIEB37uczzzzDPcf++9fObaJ+kpJ3xqlQmcvNF0pHADZeYXIM5HlUgat5cw3AimWIwLO+a60KxhQ7upKkqRO6AcRjxKpfZIkFJGauNaR//W/hkRBasbFjUJW2e/ds9YWGnL+SGHfqVKmYQ8+50OPPvnzmmg3cK1ZeyadC1DdMWmzDDg9NuyKyafV2tLImtZXer7IUrxt1qj0eDSSy9h0ogqv/j8JsiWQllWrU1TbJ0nXJJcPtAyzJfVdOpc20Q4jbbWj85RQvaWUAPG09XIJhQy14iuhKhmSu26/3ObPOcsG31BI7soTOspMo/Q82qeyUt6JCrNDVjKc6rL9CJt9dV8YYOkK0ZGwjDJXV1tRR3cuYb++DKRdE3sptXfMqH8Zuafm6yZQdOE09O6ZQwTiSpHvlqae/ZUaq28gjlSRDbh0+3TSjtEIsnn14jHdyN6q0wYanDpJ2ay0am3cuutt7L99tv7fRx2+OGcduqpbPuje7jn8A+x3qQRJjrXWzKM6WALpOC726zEJ371KOecfTZfPuww/pkqj6d//zTuu/9+jt5wGqObmrf6hrhvbh8HHngg99x9F1+8/BGq1Sp1C4yHVWJW7q4YRlhJW/BKQRIxPYq4ct/1WNzfpKcce5eRIbtIWHXVVTnxxBNJkoT77ruP73//+zw4bzGbju4FIKtlvDp/kNvfWUxUTVi9XGa9KSOMdzsYEFqOaNWaVEdXvUwDoDXYIulKgIx4ZKUoN50rL4dxriZOk+6AtutvOtfkeeblFLm1Z3N2tyrNqYys+GIfTeu/Xx1tKvymgy2kNAVABucNFZKfgI3WuSbL7fNmv8cVEckCiUlSjX2SnkM1zuPba47tfocNG9YGjt+N9l8Dx6VSiZkzZzJr1ix22203AJRSzJo1i0MPPfTf3r/W7ncRDiO3djSYcpchC+Xs14y2p2CvzGQp29nPOMboZTvCpVCAPMf6hKFWHPMVTJCO3VwaMHa6XDDscAgEQ1Acss4hWLW/NeZvISREBij7iT/USioFUuN8M9veD79PCjxucAC3jTmP2kGAA6adi4Lw+nTKStw5uSS/8LPub/fe39Jih8fV6TrgmGL3wHXHMGSYHOeJXDBKRUjPlQHFPuzCao8BnBm+TxxLpAchKlVF1rvSiB4zsbmVvqpljOwyOr1nn32WSqXSpsV7v7QzzjyTarVKFEWstNJK7Lbbbrz55pvceuutXPirXzGqK2GfNZZhRHeZUeXEFkYxDL/oSgKnC1U40LgQXJi8gUs6KRY8IkrwUp5OWU6rBZksIhmdUqZGs/1EwkWfi7qkufFGhSWfZ/ealMXi2gFcVzEw/L7Yeh8vHDROKOE+sc4J/a60sPN4jsjnDhbJX82cvL/pE7yK6IjrnxH7zVyWHzz8Kj/+0Y846eST/ylw8X+pzZkzhxNP+A4Dixdz1WGbUWrm5E1X/Szyet9QA4+VQIhyVIBmm8TWGmz5BGwRCeJybC0djQ5WpAqZ5uQuMSpKiEdVSOfXyfubxt3GLrpLDoxYQKWVImsatldl5jijckz3hG4aixo2mUnTtUwvybThyFEVzwoOvdxPeXgZ2VMimTYc3cxpPj0fWY6Ihpc9490aTCmPLNuFvNG3NvubNBYVvu2JtRrNapkpB+zC+HZelG5RKoNKpS5B0LKWTtrhgHMjbVAeVkYkmbFctCWr1x7Zw8e3mM6VV17O5ptvTleXIbWmT5/OhT/8Ift/Yj8uvON5vrnHDJZLrVNUwNbvtsI4Tth4Ot+eNYuP7bnnP1VB9LHHHuO4LVbkaysvQ21BncfnDaC0Zv3112e77bbjt7/9Lddccw1jeyqcvu0q7LvF8uTz64hqZGw6veNHRjSum49OG+srH6a1lI2njuKqD6/Bb+Yu4NrZz3HaaacxefJklH2+n144yEbDupGTu/n+Q3O46NFXaeUKIQRaa2aOH86R6y7LZr1d9HQldnFUMoumNPKRgbyZe5AZNTNUI2+7H7IrMYxxEkiHck1mk/hUqoi7TMTBAeLYJuG5+5k1cyoB41yxciFn5+a2U82c6uiqKRJSjRmcN+QLfDhNNZiiIiJVJF2Bc4UdS1WqkDbU6xYAMgqi/pGAZnEu73b7r8oqjjzySD71qU+x3nrrscEGG3D22WczNDT0dxPv/unmxrpUeU9GkthT9qFe1IdrA5aQkqX109CBwbI0dqIUlYgltLRQAERoB89xEhyYaV4bKKVPqmlnmgL2uU3frIMJOnBCCIFpOCkT6IaDCd2XncyydkurToBQay2dEXPSjE65Q3hsodQkZNc6PY/d/sPPhK91sttLq0oWHtvSXnMLD/Ceuu67RcXatvmKbAXoKfYRVscqXtYBdnJlo02JUOktkVzEQvgy5BLRZc5P9pZo2mpEF154IctOmcwFF/5wyXP4P956enqWeG3ixIl88pOf5PbbbuOk+1/ipPtfQghYd8IItl1xDOtMHI5SmuZQi40mj2LZkTYqZKM8umn0djRys/h14M/d06TD6WKogXAMrN0Ol1AbWHm19bXAiaUoPV70CyfD8ayS8zoPdenkxYLce5w3QdaLvAEpAOn192JEFzSa5O/UPWvmLZpsBrsrga4W5mRv10xI0yZluWQgp32VPSXDMNlICAsbnLPlSuz6h8e56qqr2G+//f6t+/u/1B577DFOPOEEVpwwjPtO2pGpw6qmSqYFdsqWfNZDaaFRTyQ6NfZ/MpHooczoQtOcqCumBLacrkmAi3sS6+QQeTst3bQRCit5AP4/9t473tKqPhd/1lpv2fWcM3PO9IGh96IggoUiFkSNWIPdXBM1kWiKJWoUTbmaqLk20KBJ9Jrcq8nvohIrdtGIkSYgRWkCwzDDlNP33m9b6/fH9/tda+0zmJgEUIjr84E5Z5/d3/Wu91nP9/k+D5LJPIQyAIFp7qXemaKYr6E0s7R1YNrKpQrKKJiE08U4ah4AzMHr4a7Yjt6BU6EC2k1hB7VP4pPjDaNR3zOA4j4HAKgGlfefTTuJTy/zCWqWHDOcdV4GkLYTpJ2UmqtYQjLiZDelNX03RgEwqIsi2KZqhQyA3jEA1nXIk3zrIt5+9D743GV34u/+7u/GiLEkSfDS3/gf+NCHPoT/e/VdeNfTj8bvHrOPP791TsTEsw5fj7ddegtuueUW9Ho9ZFnmQfbK0TQNyrLCmsSgLmrUgxrvu2EbNq5fh3Xr1uHiiy/GT3/6U+y7z2bohT140oYpNLuGVDWIUguRGpT8XV5/9wK+fusujOoGG9sZjrer8Pi1Ezhz/xksLBS4+PLLcfnll/v38K87FvCbB2/Aiz57Df5l5yKec/bzcNZZZyHPc1x22WX43EUX4YVfuhZKAcduXI0zZnp44SHrsTFP0TT1mMdwU9QeEAt4lqEGFZarBkf87XcwO6rw2ScciUevnaTvgTctTUHPJw4UTbRJrAb0Os46JJ0E9YBeK8kTjGZHyHqpB7E1AstdLJSeLc4ncyS58/IQqZQU8yVvLiliWhlFffQM+JPc0CaShxBS4gX+QIxfKDg+++yzsXPnTpx77rnYvn07Hvawh+HLX/7yXk16/6nB10KVcWk83n1g/MImmeVxI4+/j9i/IWKfoyYgH8rxs6QF4iCxElhqB9iKNEErwjdUmoXucxXLIuw4S1zfC8NbI5RpE260s82404QMKQULSIjLuvK+702PaW0o80ZsV2x/BjCDl2XgjoHgvSruGzEIjpm8GBDLa4997hUsm42+F2Gh/d/c2PuU+E7pzB37TIZS0+g1qQGCGvCw1xAdu8pNmEvSNCYXWvbZdaz3UtJdzGBNYrKlQ/5lJ27BbFHh3G/9BFt+Tq/Oh9J42q/9GrZv345HPOIRmJ+fx1VXXYUPX3UVFrhZBgDyxOBVJ2zB60/YD5OWpUtx02njIjkEl8xbCVwamfsD7JbJ86QkuyNfLQA8G+2sI+u5ypJfshzvsqEUMGl8aZngB9sKzYCustCTuS8J09tyPlpcd1Jad+RcyFIvOXLD0oMrs65LloGV5cSsBM2ekS+lqn4GVJYAEQN13UmBJXqOZKZNVQqjUC6VyHqU6pj2Uzyusxpve/RBePsnP4mpqSk85SlPuY+P7C9mfPe730ViFL7wqsdic7cFK1G/rcQ32tmCQKR3qSiCJMcxsQLr2NlHwXQA088o9KNoPKvv2URfjaRNi9cqC4iYLwgoV+SDjKohxq9l0FrVouY1HqTRrJnhSzxjbTnkxo0aqO5uwGhKzrP0t2bbEppdQ5R7RsjXdehvlUW5awjTMr60LSEPtrbIO2Q7J8A47SRciocH566h25VRqAYVqkHl2XOTJwSwUu2Bt0k10naCcrmCToglL2yBFH3+zsh1ZrNVePdZR+NVn7oYmzZtwjOf+Uz/HZx55pk4/vjjceGFF+JNn/8Szt60GjMTbb+5AIBNvRY6qcF73vMeAECv28EZTz4TV195JabXzOD0xz8Bhx56KKanp3HzzTejsRbHrO5RNaCscc3CAIcefRC+9rWv4YILLgAA/Omf/inOPfdcvPHiG/Chp5PPfDNP0oSrdi3iuV/+EQCFJ2+cxOfu3I25FZ69T9i4Cv/nMYdiN2+IDjvsMJx22mnYtm0b/vmf/xmP+OJVuHtQ4u1vf/uYv/hJJ52Ek046CbfddhtuvvlmXHnllfjA5Zfh3dfcidM3rsIz9p3G49dMYLJxxNTWDdJ2inK+5I2wRrVUIpvIUQ1qLGuLPey2cdbXrsNZ+0zjvEcciISJPoPgSWxZHy7NdAA5U4xmRzCtxFfklu8ZQBmFwT1DJJ3Eg2Jp1qtBOuqkQxvMZlRT+iMAkyfIJzJ01nSgjMLydkoEtRVVZ7JeShuyKkhxRC+t+xlU74GDrL/whrzf/d3fvU9kFD9ruNoCGTHGKC1llrciUJQajmLm0qy3YBF20QFNBI4RypIidleddG/wG7swCBCMwd6YDIB1zHyBdBFAdD/LQuredMvAuPTCM7v1GPjeS7scvzdpICorz6I5blyjv98LA1zXwV5LbrMWPtrSJkFTHX9N8WeKJRDyOvFnLMvxzx8DX63JomrldxQDaESNeMBY93nsZwzAe4eioU5vJxc9gPScqfYlV5UaaNYRijWdmLXbAQEjuYASgyMMFSUkiQ4ZVYPLbp/F+ZffgTXTq/HKV/42/ruNZz3rWWO/P/GJT4S1FvPz80iSBM45fP7zn8eHP30h/v6au/DJs4/DYw9Y46UVxNo5Xx0S/TFQAyPAcfTvGAiWkenQhBltgBWzGqTpbWBHtZfbwChALL9K2hg1uwZjaU6qk1IaWtFQGlplYYT5Y8cN1TLEMi6WSA6a8lIeO1dQyXJQob51jtLIxJc7TyhhjUvp4pIi52GcAAkEPasb1WjvN4li6xLydR1UHEbx2ofvi3sGJT704Q/DWounPe1p99+BfgDG7bffjm98/et485mHY3M/J/3tsgTwUIy7T3lbLLkELed2wiQFMbwShxtvbAEDI24pvOGWRi1Jh7OjBvV8iayX+vt5V4vI0Ua3SBds+hmyVoLRPcsEUjVJLcqlihi7dgptFIrZAgk7GOgOpW8WP51HurZDzgbsg2wYWOgOufLYukG5REmcJtWoh8QaZ90USTv1hIGzFqPZkQdJtrZeY10PKwbC5Mub5IYCI2oLk2iUSyXyiRx10SBflSOtQjWyqS2K3UPom/ZgYnOfwK3RSNa18bK1Xdy+MMRf/t3fYf369XjUox7lH7d27VoK9nBAq6L1tV4KASYGwLUveRSu37mExbrB2//1p/jaFz6HJ+2/Gpfe8CO88weXYdXkBE5/whNx4YUXAgCO7LRgRw2SdooXblyFj1x5JW699Vb/mrt378bM6tX43q4FlAslSR644bBYKrFzQNfGT95aYEOvhbmixkSeYqEgIPq1bbO4dPs8/umxh+KZl9yAq2+8EbfccjP223dfvOxlL8P3L/0eznz0Y35m8M7++++P/fffH0984hMxGAzwne98B1//6lfw6u/fBAXgkWsn8Or91+EJ+6xG2k7RFDWD42aM/Z0xCbY/71H45C078AeX3YqL7tyN25dG+MxjDkOmyYWiLhqvE/auFClVKWxNFadmVCPppBjuHkEnGlorTG6ZwGh2hNYU6Y+LhQLlcoX2dAvD3SPf9CdrkbUOCUtvyqUK9bDyunsAyFdl3i3F1g10nqGcL8nfW/rE6v8msor7dfD8cBUxO1YYYEOCeq9VNCS3EN/asaFD/ng8/P2kySfVY4luP7PUL1reuMkvlhzEj/daXXkMv4eEHCecs37CuKYONmgrn7+MQPDKRr170xeKJtL/TCeHxKaqtGL95rjDx5jlHW8MlCR71Q29P3EHWFmyXqlPBsZjtIEVTYgrNNZjj5fvlTSbgdGOJBQrR3SBQ+PgGChBOzjDNoDsU+xWOJs43vH65h4glL8BemxuuLPYRH6jDZVuOynsYolKKTz/s1djauM+ePMfvwWTk5P3/l7/mw2ttW/mA4AXvOAFOOOMM/BX73kPnvq//xV/89Sj8awD1gCNg+ZkPABouMzrJQyycWNgqyfZamsxhGmESHc+xkZBLVfeYk5GbP/lN4Q8h5LNfZp3mSYd4oCcTEwniq7m11CpJhawn8GxbKK8ZidFO+8Zwazr0muVxFqna6jRyi2W9JhBRWx0/H5Z2+4bpFqJD39A4+ByA7tUIVvdghvVSDd0/YbvPWcdDVQWH/7IR7BhwwYcf/zx99lxfCCHcw6f+9zn0M0TvOGUg0gzPKphl6oQiTwgZxHHYNZK+pjYn1k7ptFWKbOzYgeYhh4VpJFNYMpkR57ApAZNNcJodgSdGpItMFss3t0+XdUo1FxNyHoZ6hHZphXzBXrru+RYUTcwaUppZUahGtRQt80DAAp+bFNZaPZabxjcmpFIwcgucjSosbhtCWk7QWtVjqyXke+xrGON83rQpJN48EvWbSxpYIY4n8xJL83ASCKDlVGolypqVjQ5AbZBhbJxKOZLNDuWofMEzSD48/7PVzwKN+wZ4APvey8GgwFOP/10by94yy234NDpHqUYMsCC0VCs714Ng1PWTwEAHr92Cmmm0SxVUCca3FmUOO0zV+Krn/9nAMAT95lGb7qNwXbSxb750E24cXGES/bMAQC2dHO8//3vx8EHH4ybbroJ77vydvzuoRtJZmMdjul38JmnHIO7MoUjYHD82gk451APa7TWd/G9u+bwZ9/6CdqpQVY5vP5hW/Cir1+Hffs5WvO78LGPfQynnXYarLW4/fbbsWXLln9zPnc6HZxxxhk444wzMDs7ix/84Af45te/jhd+/wa8dnkzXn/oJmijx5rbSG5hUPF1+9J7Fvzffji7jK9tn8OZ61f5OegYX9RFg7SdoKktVOOg23Q8q6UyWPhpci9Z3LZEuniulHXXdLC4bQm2smitysfkE/Wg8iB6sHPArLdFa5XxrLG4WWjQvHKVRRI7P6Up3AqIdn8O5aRj7SE2dn/jQsw8/jm4+/efgEm+MAmD510IVjpXRNrX0NTT7M0yScJeJMPwXnyxHZkwUPFtcVdvOi6n8I04WpPXaQz25D4RoB2TXMRNfLEd2krWONYjx02G8YhlE/cSKuLBJgO/vYIZ2jl9jlgqAfBnUhHg13u/3zG99TjzO9Z452UaK/TVY4xxkFR4HXF0bOTY0gbIegN1AMToyvGLj39kW+QZy7jZjk3vwclE4jkpUg7DoExYQ7O6Bdc4fPume/CkD3wb73jHOx7UcdEP1KiqCh94//vxrW9/G087dB1O22c11vZyPOOYjUgUNVHJxiT2MPfNHyw3kJLdWBpmdMzGJFTR+jF2X95A02tp6H7q15CxQAkguOLEKVmDOqSOAVyOp3lOaY4VpXw2zpeSNTOQcZqZlPXFh1U2Y36InMiDJf4uCvI1V7lBUzZ41v+9DN/fOcT7PvBBrFmz5r4+dPf7+O53v4u//Mu/xLueeSxec8J+/JnrkAAn1p6x0wfgQ3uUVmPnv+NOffHZ9iEG1u19fTAqWGfJ8ZRADdZ9i7WgHBtpnJIhMguR9BWzDHy5QQ8AWhu6gFYY3bUEpRUGOwdeA5pN0BqTtAzqETcgyvWM54BINdJ26sOITJ54fbFoS8nuTfv1zmtVawutlQ98MCxBk9e3deN9l/08qxvf5Gcri97Bq9DMjpCs7cBZh/xh6zC7Z4BXvf+7+KdrtuLhDzsWf/ja12FqagrveMc70N95Mz7zzOPGz0/+zkkiQ57KtmqQ8uuKjGjXqEK/laLFldnWdBv1UuUbHV9++S346q4F1HWD8088COf8a5ByAcCdZ52ALKPzXjYB+eR47Hy5VKEz3YZtyMWhWCihWVpy+6jAfjM91MsVPnHXLvzdddtx58IQg7rGw449Bk8/6xk4/vjjf+6GWGstLrzwQnziE5/AGx6+Bb+/ZZ1n/oV93akszr9mK3590zRecfktuGVx6B//qUcdigNaGb64Yw7P6/ehHTXbjWYLdNZ2MJqlBkoA3qe7HlTIJnNvPeesRT6Rw7JrRW8jyWVin2MAKBYKaG7eTJjl1qlBzbHZNiIf4/tp3gQprWjttQ7zVYON7/sa5ufn73e3iocsON5z+TcxfcLpuPucx2FyskWd7sLYRel345nmOoAlcbbg32OrGu/DKjrGqOMSAF3IdFgkAfhFNwZVK2Oo94qcXpmkB0QRy4o8i+U9uRXAEtjboUEuBHK7gEvRHY8BTLf3860AyE4sfIQtAW8SYlssAbMrPV3j58MK/2WAvF7FImvFa+/Fet+b/lh+Lyvu/Kd0sRjYA+HCFR8rAP7CtHIRBhASF/lC47WGIskpghdpfPGVkrhoAJVR0Ktb0L0Mi4MSB5/7JTzysafhnHPOwa/Gvz+cc7j44ovxlS9/CbffcQfKqsZzjtqETzz7Yb5ZLfarVi1DRvlx4IhvrgtsqzSyGU4rA+CPY8xCi77Xb7LtvSylsqYUdQDXmpqm/IZS1hwZJooiHzVQncRXPoTxjDXMHnzznJSmPdVNSU8NjG0K5fP63wXEMajeU1Q44p1fxROfdhZe8pKX3AdH6oEdb3/buciWt+Hbv3cq6q2L7HgSgKgb1eO2ZnJcJHLcbyLqsX4TVzThPjxEtyybMb8xBoDU+Ma5sOZHgDkCdSu99qtBjbRPWtKsl6JcqoLfbap9IIdrHMql0ltwScBIa1VrLOZXPmtT1Mh6mXeVqIZVsN9i4OidCrjCKgEkrVUtz046a9nqjUrjOrqfNIjJfaWR0HBIRZIb//7l+6WGxgRmMkezZ4Sv7ZrHb/3fK9GanMYfvfFNuOCv/xobl+7Gp55yNNJe7hnuuLlajpO4f2h2SpDvZDwamfx7TYtkIft/+jJ0plZh586d+OqvPRzbixIv/sp1AIDMaNz05IcjFfuxIV0nWlMtv+kQqYCtLDprOmiKGsvblz3D356m9MlP/XQnLl1YxnuO2BfLuwe4eG4RH9u2B9fML+PoI47An7/znf8hx5jzzjsPl1/yTfzxw7fgrM2rsXt2iOddehM2t1LcvDzCtmE5dv+TNq/COx+2BQc1Bn9501047+bteN7G1XjHIZthEo3RXIHOmg41z7VTdsTQtMHSGu3pltcYD3ePkHQSTB41g6Ub96BYKNFd3wUAn/wq0oq0l4XrJOClRgXLd1zjvDSnqS36+/Spb4IJB5GEzdcWGz/w9QcEHP/8R+FBNtz8bgDMEqfC8ESNN6CLimcK5XFx+b0kkLwXMNZRY5/PLBdmSJO2bbGkBVBAkjT1xM8VXZwBRGC1GXeAiJnT+L02Nf0noQExSPRBBPx6sbuFsNMr0/3GtNBqHICu0Dur1ED3stB8xJsF+qwRsI2bByOXDGUSqLwFlWYBGEf/qTRjy600yDGyjP6LGwVjpwv5u/weu4bE0drRZkaJ1pzL3P52v2ivSFDU3LRnVKTzjC5u3LjjvT25BAqA/ZDJlF/3MwJLvMD3Oxk2rmrjK1/5Cn41fr6hlMKTn/xk/K/3vR8XfvozePnLX47PXL8Ns/csM5hhfW7E/pJV33jFh5oj2XWgn0GvbiGZadOGmps09WTu1xKlI+/0VhKei90BPCvMDKFqJaRdZn06DOlavQ9pKq4a5OGsJXRCxywjnzfsEEC6Zt6Qtox/PP2ehHmbGSATx47QlKw6KSRpjKQAJAVRLYPVeYpfO3QtvvG1rz5Qh/I+G4PBAFf98Id44cn70docERISHCM2ZnH4DxAAJK3p9H3bATGp3nJxqaRGzBFZellvBUePccy8SiqrbKBsRd7KdlBRIIYw+945Y2/Jlx1Rg1s1qEOgQh255TTOd/QLcyufQ8IkYh2xs5TAFoPXZkRrV5JTAAUBZDcGjAGgs7ZDmuheiqyXojXd9n+T5xIphoAdzZ+r5nQ2Wzf+fSmtyO7Ny9ks7J6R132fceRGfOMtT0CxsAfnnHMOrrn2Wlx82y5Mnf9NbHj/1/GGr1yPH+9cDN70RqMCnc8mNx4Qy/oujYTKKA+MZSSdFH902Ea0hkt4yv5rcGC/hSdN9vGxUw7FIzZM4u9OPcyb2dB7D1pdG13DHTPGANidgW43rN2dbxr84Q9vw/936z24atciMq3xtMkJXHTcgbjg8H1x7fXX4xvf+MbPN9F5PPe5z8V+hx+F1116Mw6/8DL8xS1349alIS7ZtbAXMH7ywzfhgjOPwmF5jqao8aQ1UwCAT23bg/+3YzbMKxs+kxzPSjTuqfHJdzKqu5f9fUn7TPaENft7t6fbNG+YaReNMVXy9Fglolwm9h3AODCubIhqf4DGQ1dzLFGosvPnXT8aC9dYHz3rk40aB1fyxZQBT3xgYjAFgJr7jCKzbmaGfLlSdDKNg+qHHZMyIXAkZpkkqUmlOljHaRU62AXkrXR8WJnEt5LplcQuAcISmrFSvnBvjW7ye/y8K9la/l3l6d7389ZUK3TQe90W3TeWYdQ1lDVASre5RN53xLB02uMbgXgTEWuRo/uQv7Wl+OjKwrGbiQIAS00wPrOe54WwSRAdYswAoYZP17IOCjy3Gkt2SnzRUkKcs3WT7ISbxZIYQqMw3c5grcX73vc+nHPOOQ/pYIb7Y5xwwgn46Ec/iu/umMfT9psJ2lIeDgSULYc7xJIex7ICL5dIA3Am+VASjj8wLsmKKklguYOeUsz6prR56gRmWaX0vjyTnEVhNnxu6GlmhcGuFqvaQF178AAgOOnEVZ/UkUVbFAMsmzXXODqn+POiBYD1ya7S0J0g9TjhgGn8/Q+34tprr31QyXzSNIXRBhf+6x34tc3TWMubGQdpgq29jEHnBvVSBZWylIDt1uj8NFBGwzua8AZatOoAkSt+A8bzRhkEP3R+Lmlei683zaCGbWhupp0EukXJdQJIs0lqRqIIYcdesRSS0FQWaRRX3d7QI2eKCQLDWcTgikuAMMWuaVAuWQ/u0jY5TdRFjYStvPx76GVIZ9pjZILVysdCC6gWhwsBvgLmLbPEZBUWgWJmjatBuF0zy9jcsYBkMofqpjhkVRdXvPkJ+PzVd+E1/3gV1m7YiGc84xnYvn07Pv7FL+C8y2/HC4/ehKNXdbBcW/zF92/Dhok2Tls/gVPWT+EpW6bRTekzaauQtul8ayoL0zI+ctvZGr99xCa88rCNaCqLbo800k+2U3hCt4d8IvM2dk3RoLu+i5ot8OJAFG+Pt1R6OYBrSDZQDypsHYz8MVvfy9HMsYtHaXHmvtN44uwCvvLlL+MJ/4G0ynXr1uHtf/In+NKXvoQPfehD+PStO/GREw7ExQtL+MKtuzGqapy03zT++KUn4Ix1Eyhv2I09SyTFObbfwhn7zeDin+7CG264E/+4fQ8eNz2Bc/prYRLSlFfDmlheUIKepOGNZgvkExnyyRzL9wxga/I3FvZ8cssETD/D8O4lmrszbTRs/ybrVx2F4SijUCwU6G3so2Z7umT/Sdi5AnappKZjrLTGvH/HQxgc87+VJU1uw+Uiv9ulxgzdT33DhoQKuKqhC2t0QrtIQ+qdG/yOsfGPiaNqReQ+tmgCgDF+tywLMXjh8tpHLwHBOPDzemGMl3GthRuWofQLjIeVxPpc+dvKprj47zJi4FzXgNV7A1FgXP6QjWuxxrTcniWOQO6ooNvkX/k8TQ2lNDUfKg2n+TlUBLAN4OwIvggi73OlljoxdJ3j21Vq6IJZWXIdMexawpphscaSZhti2RKvKXQMlv2xgoZO2Roq5d/7mX+sqxroPIHqJsxg1tBTOVQr4earDr7yutPw99+9Ded88lvYvWsn/vTP/tw3pPxq/Ptjw4YNWL92Db5+0z146qbVAOpQ0QFoExNXUKwLTDAvvisrQwA7VrQMADPWVOk1qMz+0n2DfEjxaaBa+fims66BPA1ypsRQdSQOGeJzTPU7tLHLUmBgoSdaQToU9w1wkIh/ffmbfI5OG0pS+LSGyuk+LjfUkMdsMjlw1PjNUw7CP/7wLnz0gr/GB847/z48SvfvSNMUb3zTm/Bnf/Zn+NCqLt52yiGBOY42DHqlzz1A5zXAay83HrcSPodD9UjOf2UUFEukyFIv+r5t4zv1q0GIUQaIeWwKAcKGQGLdIOtlUVNS0G4qo6At6y8rZpAj+YKrGmSTGeoltVfMtK9MpmSjJZIMwHqPWR8qUVkfQd07cGrsnHBVQ7KbaMRsN0klqJzeXdvBYPeQPr9WMAyQdUUNgbai1xYPXZ1qNIPaO3ks37EAddciekfMYBpA3yqUjcXWrVtx9dVX4xnPeAae/exn4z3vfje+dOst+PRPdmFYFDDG4NATHo1v33A9PvGtG3DWwevw9085CqklwO74GOiEGEvA+uAS8QNPcoPBbtLmmkQjWZX7YwIQQy/ewgmAYqFE0klQLBTIJ3KkvQyDnQNMbJlE0klRLBRkb1Y02ACDM9avxsNWdbBlso06JVlK2ibLsyMmOvjmzTejKIr/cBzyLbfcAgB4xfH74pS1k3jOUZswOq6CWtfF5GRO+GJU80Yt8Zumdx28CTvLGldum8MVs8u4YnYZX9gxh3MOWg+1mOAZ3LSX9VIMixom0SgGJeuNM1SDChP7TWDhpwtjoSC7btwN1zi0VrXQnjYY/HSeNiO1RWe6jaSXoqkshruHtCbXQH/zBGzdUFhMJ0W9dRENeyt7QvABvBw+dMExrwmuQWCEAVSzI9qZMiPsmQKtvO+sMokHvnaxZLslAr5SZvNssFwUTaTtippg3AjBQ5kvqG65Is9SYYtTAY9c+jMaYLCOFmsdU0Bs0+5VZqE1VFt8VO9Fl1tzKtfK0WqFn+VxPyucw/shVxRre2+6ZA+Wo4jte9NQ1U1gjSUGV15LGGnkwKiA0hyeIXppa0lKMsZAAwD7OmsF1Mn4psC6cXYagGonQFoDYsFkNFQ/2vhItSBKGFNaQU3m7HKg4EAet+JmoPMEyMjqxo0aYhcjoOUdLWSuSXNo42D6GV56+sGY2dDDM/7yG7jmmmtw7LHH7v3d/Wr8zLFq9WrsqRd8A4f3nvYSBe03wQA4MTNUhFRqxkqGHlw0jjS+qYay41pm8sRm+Y8A2fjckXkXR57XNZ9D0fkjm8q4YqQ1nW9lteJ8V0DGF1ABy3XU4CtVo/gcFYmSBJ1oDYUaEEkGb+wtgEwrvPrUg/C8j30fd99994MqrVHcTY7fOBUqA5YT3HokJ2n4uJqO8xdgsT3z3tmyCRYnI9ET83wiWQYBYDPTpsphtPZLI1vaSZmRDXNNfIHFgos0q6JP1kiZJFGG5AfSXCclaZFLyGs4fg6dwqfT6dRAp8bbcqlUe6mGZR9ZWzdI8gTVoMJodogkN+gdOBUqHq2EJSOO3Ccmc/LT5pJ3PV+gKWo0Bb2npJ2iXCqJeWT9qNYKLtFeq5t2Eh99LHIXO2p8ZHBnbQe2shjdNo+0n+JZh6/H3FvPxPv+5Va885Lv4Tvf+Q6yLEW308FoNMK+++6DE04kf+B99tkHWmt84xvfwHvf+17MnLcTG3otPGKmh3MO2YBjpnsoBwWsdSjgMHQO02niG8PkfQNkPbdjtsBknqAlCbqN817U3bUdpL3Mby6sdUjbCaqlEsPdQ2JJtUK5UCDJDdoAPnTwJuSTOYrZEck9GBhfuG033v+Tbdhn00Zs27YN++yzD5J7CRibnZ3FRRddhO9e8m0UowInPeYxOP300/G4xz0OX/vaV3H1zmXM9yfR2bZI2uF7BnBakXa9iqQ2PP9XGY2vPOFIJBt6uO7SO/GYS67HXGPxuqt/ipF1eLUCvvyYI3DUDIU3SfNmaxVpj4e7hyiXKqSdBMPdQ+QTOW2EtIIWq7+iJg19J0G9UEOv15i/dd7rsAFAJzSPdUKbxRRh86V7mbdR9Ba7D8B4yDbk7frK/4c1Z/w67j7nceg2NCFEnJ9M5rS48e7drOuOs8S8U17JDgmQcdbdewezaFBj9klrD55kwQmxxE1oiGG3BOlSj7PEfcmuFXVYA+HnmAkTHWJnRUleGuJiScYKne/YcwIYc88QDfGYLnpFot8KXbIHv1kGtHLaQAgQlot2rAteyVzHzytNhAI8VkopECUAymNXhqRYG/yU6wauiDrKq2aciV/hSjHW0CQJaKwjl1KpsMk0X8LzjrkVIISDxI4CbrGEXt2mC0Vj8Zg/+ypu21Pjfe//wK9s3X7OUZYlnnf22fiLxx+C3zlhv3DuyIjjplsBSI6l54ntWXzuZcHlAkbRJjQ+L+IhgDc+F2RDFqdHWkdzMQ7gWbkxlcdoTcBazinPOCfhPgK85bWFTfaa/8i1pmT7ulYe+gMi2ZObX4ZdLLE4X2Djm/4ZL3rJb+AZz3jGfXqs7s9x9dVX4y1veQuuefVpOLCV+3VWT+a0wWkk8IOcPhz77cbOJmNONV6jPH5NAKixzq+1shlLzZh3+pg/PrOQEtcr+ky/OWNNLEDXlLSfkl8yu0L4JsJ0/FrjvWQjNtdE2nVwg2GxULA22HiNsehvdarR2XfCN697eaBRaHYMKP56pg2dJ6jvWfbNVJKIJpvK9nQL9aD2bgWyNlYcva1Tg9ZU7jcFJk+8ZZd8FluR13LaI52zniQypnQOP7xjFt+/ex479gwwoRR+ODvAV+7ag2HVoN3KsX7tWnT7ffzouuvp/bTb5N1cFPj1fdfgfx27BR/+8Tb8v7vn8OPFAabzDH905CbcPijwhbvm0Es0Duu3cOvSCFfuWcJbj9wHv3PwBjRFg1ubChffPYs7lwt8dcc88kTjm884DpMIunHXOBQL1EiZdBIU86XXIstxSjophWPUFkoBj/7+j7G7cZhevRp3b9+OLElw8ikn45zffbWX183Pz+OVL385jKvx3APXoq0VLrp9D7YuDvGud70Lzjm8+y//AuubEp99+AFo9XMO40iQ9TIU8wXyyRwLdy76Yy4brPZ0G4N7Bkg6KfKJDFffOYfHf/s6P5eeuWkaP14a4qKTD0cvT9CaaqFaKtHZ0MWANceiLU9YnqZTjcHOAUyeoD1Nx0BkF731XZhOguW7lmAt6dNNbpBxTHa+qhXOQz+hFRYai40f/MYD0pD3kGWOLWuEyoUSrSyBNizK10FrSAsAlbU9q6Sj9DJuoAAAV7GHqHe7IIkFWL8s2kTRHbuMSgEq1wyAtdc/Kq9D1VGgAAcBDCqgaeDAWljDIIujK4OnLpfrVwIy/kxuvuAGHwaTo4JAXDsjqYVP5MoCwwrcC+vMsgxJ7hJ2TICm+CvHF1+54PvgDy7/OUv+oRGGdVUJhQhsxExaDPyzFeVirehzeL4BpLWsa04ThL/Y0/35vh5cWChJEASzyBHYd1VDbGBBeijwxoUudIbTqVK6nRPWlHRMRyA7+KOOM+zS8S7ATK/r0u64k8C0DD79eyfjsNd/DhdddNGD0jHgFzFuuOEGVHWNR29eNaYLH3OFia3Z8qhpjV0dFMLx85vSVAd9cVwZyaLKh4DYsgyWhTJ/hfX1fQN8/kj1JTEhfMefazqcW4mBardDvHwsP5KNZacN1e7ADQf0PFkKlBVUtwtXBKbYA2vZ5Ipl5Gjk1wLHjVLLUxkSozE3N3c/HbH7Z/T7ZCn1g9v2YP8tMwBoDriiATrSxMjH0WjAA2YLFfU0eI/72I6xCY12AKA6wVvbFQ2c6LtBx1OB1ipnKegDAAdr8Hwzyluv1ewcITpnnWpUi2TJluTUCCUODKJnlc+meW2KY8OrxQpJi50AKuuZW7HlEmBkWiQzaO3bp0bhVHPqY+IT+VzjiC1uHFTLYHjPwEsRmlGN4e4RytIhy+i5u+u7PlxC5CVpL/Mx2M6GOGpblbC1QZInXhaSdhJ013ch8dTDOxd889uRxuDI9avh1qyi72hYY/Tw/XDF0gDX7FzCtlGJ2aV7cPgBa7FjWOK72+dhncNknmLzdBtfazv8z59s88d5d1Hir2/ejlsXgtXZj2aX/M+HrelhLlf42M078O4fbd1rvtVlg3SyjQp0fRZXjmpYE5ssoVI8mqImp4w8gU4NbNPgsat72F1WyDHCK47YjFFt8d5vfxu7du7Cyaeein6/j1arheXhEBc87Wi8YMsaVIMabz16Xzzxqz/CX3/4Q3jnX/wl3vDGN+H1r389XnvN7XjOulU47YAZX7XQqUExXyDrpagHFaphjbSdoC4aDO4ZsIVbAVtZHL6qi9ufeQI++JO78eEf340fzi3htuUCn9y6Cy8/cD0Wty7QBuuuJe+F7fXl1qHYPUTSoXAZk2q/Ketv6mHhzkUU8wXSxqGztoPhrqE/F8qlkuKkU0OVHNEac69IOVf82yf/fTgecuD4/PPPx/nnn496aQEAkPZSoGTWODfQq1ukscsN3IAWrr3cJ6z2JbMgv2h853GzZ0QpVyMbtMlGASXFjQo7Id3uMoQ5jq2+pIM6tgUD4EGqG1ShmUTYzCi6WJnweCkZwyhiQzop3FLpvYg9SBhFE6yM7MvkQj/WzMYXeAHP3gVDB0Dgy8Bqb4C7Ut5hbWB4Y5ZLWGp/PxfARPRY/3vMnse6aa2gah0AsrwHeVwrB5CPgxS/MWAAXtdQHSr5KN4MqBZJOlRC70OxJZieaMEVFckq2HJLJBZSaRBpjswxzRZMkEavoh4HcFmGjZsm8cqnHI4LvvB5PPvZz0a328Wvxs8ey8vLuODDH8LhayZw5FSXzieM2zbuxRSDfYJ9pYfOHQVE55MKTW8yEgMkkaTB35aMJ2P6+0fWX2lGzaX+nGFJVp5w0iRrgk0CTPfhFhfH5UOAB8rK0Pnnf85SqNaqwAyncR8Ab2rl/G61gMEgnIMi6RjRplpPdnHN5XdgeVThpJNO+q8engd0HHDAATjxkY/Eu753PZ45M4kkMxRo0Difigerx6t+TCoACN7VrNMUOytx51nJHvvrh9GUxDeINufQUIZCojyDnWrYSvtqpq0bb3kljFs9IP1mbWsAuW/2qouaSs/Dyss0EgC6ZajHoXFQqYOqGmBQc0R07Znmalh72YAP+Gga9A6egO5n0L2M9NMSa56KjV0dXFOkuVOTy0A1rFHXDguLwETfwQxqDHcPkfYyb/sFAFrTRkAbip9uTbUo8W+eAFlZEQMtkcXKUJy1NsrrseuCZCHS2NcUVMLvaINTVk/g1DWT3jlDrqc7RiVuXRzhEdM9ZKnB52/fjW5qcOx0H8es7qKwFh+78W5oBRw12cERkx1M5imOme7imMkueq0EJ33mSixE18oDV/fw8sPW4yXHbkY+qFEyK+6sQ1PUSDrj0IqaIxtmR9v+PSqjoHWK8x59sK8myJx6xEwf51z1E5x37bX+eTb22jgkTVGzPCbJc7z/pAPxaxdfi7ed+1a8+Y/fgrPPPhv/+I//iM/unsc75kd4waHr6Hizc1LWyzCaLaD52CmtUFU0V0SmY3gzuTpLsFw3uI03c+f+8HY8d+M0Jtj1xOQGi3cuUlQ0W/bVkXVdNazRWtUi5p79usVP2bQCuSWss8yVYtcQ+SpeY6sG9XzNQTX0Pk444QQYY3DOOefcb9anD1lZxc4v/xPWnnk27nr5KZjen8zGpXPbuxCwxk51E9i5AjAadr6AYUpfdYlVjj1vpUTu0/aESY6a6/RkTilWLeM9RL0kwzd/NF524Yqa3C/EIUNKWqkZ+0wCtKWM5z16EXLqPUBuyJeTGNAkNB+yDMAvgilrlWNZhSwCnfY4CybDWrqwRvHSQBRq4i+4abBey1JqpIsZNGBcLhE7SwDjYBkYB9nyPuT2MW/jMrBwERj3oSkCzmO2W15XZB7C2sXfh41AvbiGyHuMbOpcZKGj2tl4vLa815LvI6BEfk8SAvB1jW27l3Hgb34Sz3v+C/Hc5z4Xvxr3PpaWlvC2c9+KHXfegW+/9EQcMt0LZd5UB5YvbqqKXCdE+qSlIS0u5cWNdjJHZbRa43NUGk7jjV40Dz1jm6V7n08Crus6zDetaS6MCqqw5K29JUwtvoCIREOY4ZX9CXXjN4zS0OqGwzCX4/OW38NoaYB9X3khHnXK4/HKV77yP318fhHj2muvxZvf/Ga87zGH4IVbZgjcJgbZTNtbLI41VAKwgwpuQBZtsRTCh7ZwNWI80IWJlNJ6OV6cpghEsfUrAoEE7AHw9lnUwFePaYsbbmATyYXIIQCMSSqS3HBynPLyDZFjxDIO0SGX3HzX3tBDsu+EjyrXvSw8x1IJO1fQtYQdfJINPQxu3EPfWdVgefsyRnMFdt5jMdGnKdSeysgXeVB5aUTDnzXpJN5TOZaBiBZbAKZYw+k0MOekbyYZimkZaD7PJGBEdMNNbb1sQ8r81ZAZeR303K5x+Jsbt+HNV9/u38ezDl2PDx+zHyzLO17+g5vxxe2zmOj3sUo3+NuTD8XDVneguapbLZVeGhA39/mfxc86+qyxDZ7MBS/N49skHMM6hx2jCrctjfDI6T4STey7NBOm7RSXb5/H2RdfB9Xr4+RTTsWtt96Kq666CgDw6k1r8DsHrMPEZAsmN5SAVzRY3jnwzZGW30M+mVOISi9DPajxuXtm8aorbgUA7N/JcdugwL+ecSz27bXCho7X1NYUrUXicNGMGpTLFbJu6qsUSTtFa98+lm+a9ZsXaQqVykDSIds3mdvNoEbCTjKzyyX2+bvv/EpW8V8Z0uSf7T9JhvmTpD3TnZTifZmed1UDjKISbIcaqtxyBZ3mQSZRWd+M4YqGkqtGETDmHbVKCWArrch2qeW824UsjLqfAa0sNHxw3KzuhkZApMaDZm8BJBqwxTKUvrqJt5lyg4bs5UTnLF3ZGS3ubtQEG7GiDrYocZd8khIoHvNAjjTIXnfsPLBT8XNIo1EWexcren8aLKtQ4eKeRayYXOhjQLHyv3uLwo5fX55Lg14HJjS4yq4coPckQMZaOD2ud1bdrgfWKs3GI7oF3I8K+pyDwRhIGUtdZFZOtaKGwyQBet3wnsty/Hf+ftcr4KUnH4B/+uxn8NSnPhWdTge/GuNjcXERb3vrW7Dzrq344gsegYPaedgocmCDyIt88IXWUOneumLxppVgG+9KE89NAaAxSNaRg0osS4qrJnUdNes5uGJEYFca60QjnCRAp0NzIsvo34k+1NJyYICj16XzrRV+j3XJsdZ5NIJK2mOPUwDNcamyiJsFB+e0Whl+58zD8e7PXIwnPelJ2H///e+PQ3i/jKOOOgqnnnIKfv+SS3DSZAcHre6iriqkg4QqagM6x3QvhQPGEvQABA9q1iKL3CBurgXgm2n9+o5gNyVBM4i8lW1lKW45YghFYyvgSeR/krCmDDG0dmih2X5NJwraKNRFWPeaSnkQEgMx6bWh5j7FDXGVB1ZmXYfOjVSTtrdxHDYTNgEAvEVls2eEfFXOgRuamUGH1dUIZQnkbbIBc9ax1tShXKqQsY0lAHbNCOEjSivUwwr1sBqL1BbwVQ2DfjmVUjtXWbVR0CbxVnTOWqhKIeO0OhnSGClhIBKc8uKNMzhloov33rId358fYdWItLGGQXXDh/ygBHjvsQfgiIk2YIFiufBA21UW5YAYY0kOVGxv5kF6mnoWHwhphf7faMMswFhrBaM1rr1nHj+aXcbJG6b89yCAe2l+GYdagy+ddjjees0d+MHXvoytC8v+uT54104c023h5CV67XyCNi4m0d5qTmuFpqFAmZTDP5RReNrMJI4+5SjcvDzEpTvm8ZFBgRMvvhr/eNIhOHn9FEVELxT+MQCFo5iUnlvzRkWCYADyRZYNWjWgiPFySZpIae6oPIEryHZRaYVyzwh65bl3P4+HLHO851+/gemTHo8dbzkTfaPD4tY4NPNFSMqqLLKjZlDesJucBniofobm7iXSi3FwQHrIKlQ37IbqZwSAOdVHGGEzmdMkj1KsANBrSbOG0cEr01rYxcqzDY6Bc/w7GfQH7a3uh+ANuiFckH0ZWPSsufFOGd4xQ0pisktl0O0aR7HPAMYa9mIQKkEjP8uhQiQOK5v9gMCkCeMhLLIEfKyUYgB765h1BKrFgWIl+xsPAQlx978Ae//eTXguGcK++efhk7Juxsrerhq3NlImITYubq6KtZ3C7llLG5BRMa7hHgwCINIadscC7pwb4PDXfw7Pes5z8YIXvAC/GmE453DuW9+CO276Mb7w/EfgmJm+L3vHfQAyxppdZXCJ1jfdxRIMAZbSBBo3swIMZkXXvGIzKMCULdb8iP2MyyrMf6lW9Lp0/PMWUHCT3agIf5e5vPL85GqDf8743Oi0AaUBF7HEA9ZXSsWirEibLBtgBuejPfPY9LJ/xFPOejae//zn3x+H8X4bs7OzeMlLXoJPPeNheNp+MyjnS+Srcpa3WfjIbz6urgjhHzAUyEIhLib0hwCeVfXpmVLCj6uDjYVdosAPKf/XRYNqqRwr+WvegImXrLCVYivW1HZsvba1hWYPWmFPgVC9M3ky5oEcp3xWg5ot3yrf7Nbat0/XLSZa4uFGzZhc0LHHtsoNET9GoeHelvlb56G0IjlFh7S0JjfMRmY+Ya9cKpFwAI5Og1XeaNcQ+WRO1Zuod0PlCerZkQdWcm5DK9SzI9iGwkXEa9lwqd8fJyA4T0UbhmpYoRk1fvMhrCUdK3YR4bWjqS0qA3SMGWfqOymxsMzKK63Y45g++2LV4JnfuQHXLwzwqkM24A+P3RerOun48VxRZZDPLXMBALYNChz/pR8CAC56wpE4aVWfWXRyjhhzz+I55doGP1we4tO378KBq7t4zakHobh1HnO3zXvWPLDxJPeQdVFY6aSd0nxlvfiuu5dw5rW3YNuoxOsOXI9X77+eHVHCZqw93fKWbv67XiqRTeZoigZTB03587CcL5H2U+/vLXrjbKZN52YnwfCn82it76G4Z4C0k2CubLDPR7/9K+b4vzTik0lKYVpDdQwwX0D1M7g9Q2KZVvWBZlcop1my1YImJ4vqJ3sA69DsGNDCymwyGtKXuhF5JstzehsgKTl0FFSm4ZaDpMIO6sA4gxY31TI+stQ2DmYyDwuxWAgh2IEprYCmoZQrXpjHwkrkPUTfh3890SCnEmkL9gJuwoV1r0hpS/pauSikmsrNcWl5JbPLbJdY3QEYL0/HmuUx7+V70S/LGGPHok7x6H3+zMfEDYjWBeu3GHhrFVi+eGQ6sNBcQveWcjUxL/RdRKX12NZLwFRZkW9zK6fnwIis+4Rttw5IaLOzz3QX5zz9SHz4s5/BU57yFExNTe392f6bjh/84Af44dXX4MJnHUfAOCp7ixuFWukcYS2cZe9ikSABgS02anweAuNzQZru5GcASAxcUUDl+fhryfPEGyC5Teaul0KsOIecJYAMAJkNeuGV0iJ5bpk38j6zNDS56oS91Rta8WOXjFjKxE18nsFeWESr04LR6kHpt11VJE9ptxLoXoascSwvy9DsGvL6GwCUX8c7CXSe4NY9y5iayDGt/53LJK8LcZOc2ISWSyWqYe1BccyiEThquBEpOEYAgK0tGqM8owcQW6jB2lUGvQLwAuscWYxGMr6QlEe+y2kvI4u3PKGeCI4zB6KgK/5eZOjJ3BNFiquZEqjSnm6Te8HaDpXGGWwJO12BP19ikG7oel22yqmJvHf0Gg5mSQhwt8iGzo1qpDNtf3304LRxFFDC33UsWZFqkPwu7iAm1T5sRScGume8H7Sk3VVLZSRBCdeRBECTKx9u4hoHDIhd9oEmdbi/sw6NAa5fGAAAPvSTu3HRXbN44UFrsKad4+QNU/jetlncuGcZLzt4PQ7ot/3jgACMAeBb2+ehlIJzDv/3JzvwiId1/OYCgGfCQxWigTEKj2i3ceLRW+h+u2kzlE9kY3NNdMa2skjTxL+u0pTqp1Pj7Qb73RSXHH9ISM0b1lAF+XM3BemMxaVDNNUm1RjxnC/nyRQg7SQoZgvy8eaExmoY9NzNfIFkdYscnHhdbO3bR7Kui+T2uX/7XLwPx0MWHLtF1kQNKyCji4rdM0R6+DTsNTtDcwGA+if3eMY12dxGed0u2D1DqFSj3rromdhq6yKSmTbJMipKv1ItOplNJ0Wzg0sZvHAAAFhmoaNSkF0sfenXrOui2bEMzbt9YTViFnqsgaiywcFCmkYGNdRA78V6iSWPaywUEgaz44yTWJiplNik2JLHNZToo2JttJQb20mQGNxb6EesA44v6EnCmuJm/PbYdkoW59aKpif512syI0cAca+osTeAuDcpxkq2Or4fAGQIIKjVGge4omvWBqrVJ2aPy+pqVdQQJYBb5BjyvaQ8FxLaJCi0yJbbM8m0MVG9HFgq8ManH4mPfulG/J//83/ut+aDB9uYm5vDRy/4a5x+0Fqc+fBN5FSCqDlK5kDj4KogT4qT7WQj7LXGXPpG6oJ+Xua31+5GLi8yosoOxBllpQxImF2RDMVVhSyjc0Ks1fIWgWMZK0GxsM/CSrdyYoK7XXpcrF9fCeySSONuHbHKEoxT14BJ4Obn/Wf/wr/cjN2LoweVpEKGNLHuXqb11jCYUqmGmSHbxHrHgEDc6hZ0b9JvmO64ZxFHvOdr2Lyqi2tefzo6WRKaNMEWWJ3UH3dX1HDL9D02uwaoFiuM5kYo54vxSgWIiUQNDx4ACpKqhrXXDQtwFhZS2GSRTYgThKssHD/OWofB7qF/DtH1SlObsxaDu5fR1BYTm4gxVv0MGFRwy5W3tvPsLDBW/YRRUKtb7N9uPNtumPHNZ9rke8xA1DaUNuqDLjop2pv69JrWjtlluhE9j2olMOtYPsYNsmA3IHF7soPKA3+v8U5ZPmdpkyObFJFlJHzNtKMG1aDyeuxyofDfJQCfDCeOIADJL9rTLSTcuyA6ZgHdPhGQweRcUeJtP9qGz27dg36WYrGscNZZZ2HPnt344GWXYTgad1z4/LY5fOcJR6GtxnXIliUgn7htJ44+8kisnpnBZ7/zHbzj4fuhl5rAbEcsd+1zGEoPoJPcYHn7Mqx1yHoZOh1ihMWfuFgooblJU3OTJQCYiNAiX+oEVRM2ZE3RoL9Pn7/jNEhnouNaLlWY2NzHaK6AaSUY3DNANpF7jb13p9AaOuGkwT65O9VLFUkpLGGc6rZ5ClN7gMZDFhzLxDZruqRn2roI3c9Q3bDbL5JycazvWPAn2fL1u9A5YIoSzPoZ3KBCetAUuVX0Us8IUDMehVGITZDvRu5nMDNtCvqw5Fphl0piFvkCrPsZ7GKJZtcAZnWLdu9iXyKJXHbv3TI14ZmghR7VvINPfMCIeCK7+QKaF5y4IcwN+T2LYwLrl73uTrRnfMI5AKhkpy/2bCpc6GMmLZZSAPx3C5fwxT666Ksk0nECezO11gLeG1mTkLxpAHAMZ93s3bS314wOdm3+OeOSudzmH5+MAyC5r2fV0nA/kU9MTRA4kc8uEgp57EQ/YqstyKmfgZIG2XBZSefiMjhAny3VmFos8WdnH4vf/9iXcdppp+HII49c+SH/W43bbrsN73n3u+BGS/jQix4NxYyYTyDUwmxJhcd5KZKE9AgvowxXa7p8XKUaAozLe3yFIWKG4+Y3reF0TZtQqwHYAGK5qQ6dNoHVcsRMbRSXHsssmjo8t4k2mHI+eFlE1ITXyil6Ps0IXA+HQLtN55uzQM3nqInmd6fDkp8sbOBk1DWuuWMOL3jfd/DIR56ARz7ykff1YbzfR7fbRb/bwdbFEet+qenSVRbNjmVaNydz5I/Z4plzN78Et1ji4ivvAgBsnV3GX1x6K/7sCYcB2nl7Mz3Jc6RxtM6DQFszO0IxX6BYKFlfSn7CMlzjUA+DB7A03nkW0FLYhk41bfSLBkllvPwCAKfrUcqeeCJLs1lrisrXRHKQ1rUeVhzOUaEpGor2Xd2iCotRcEbx9csGGR5XVHSaQEUNerShSn0zuF0s4QoCy66ySFa1kIpGOTdwRYMUoGtKP6OKjVGwg8ZvVA1fuzzjW1lqHm8cPXc/I/clvuYEy1Prmx/FTQMA+TILM826XJFf1IPaRyArrRmYKS9fofQ3h6qw/jSZOXiSNLoMmiXdMOkkMMxMi5TAOYffv+5OfH9hhBe88IUAgAMPPBAPe9jDkCQJmqbB8vIyrrrqKhxyyCG44IILsPXG69DpZ0gsuTbU7DBiLVUarp5bwmkPm8Hzn/98XPq9f8EHbrobbzx8M8k/RvWYy4WMWHIj7y0Omknbqf/c+QRtmGVTIw2Nju3mACLIfKNoTvPR5iEFEiAGW+kMzjrUg2BH6KsabUoSHM2O0GLTA0nrI6cKkgRZtqzVqfYyG9k0qYUQwX1/j4csOBbmdumq7WjPEIPQ8AmkcgM7O+IyUbDRcpVFa7rtm9/qu5egcwO3XMMyg+sqC7OmjWbnkHakm/uomTFudpQkhUi1Z4clVck1DmYy9SyyHVTsS5lzM19IaNKrW8REDGoGtwZKnmt1a6wJUBZm1RImnDWQvAj5UBAG23TGq/DeGgcHy139bfK05J3aXp399CVCTbQjwMlAIB5RE5C/4OsWVLsd/g4E/9e6AZL63q2wbLk34E4SYmpTEJAAIklEBCaamoBBYiO5ReSoIQuK1yJHQCUxBGLjxwiTnWb02gKYtQZWraLXG3PQYGA+GpGWVG6Ptaiipc5StnuKSt5Tk1DWQXUbvPKk/fCxb92CD3/oPJx3/ofx33n87d98FNvuugvf+u2TsS/r5HWf2fhO6huKpLwslRYAY+mjup/5C7KvqLBsIoSB6CBRkJQ7YFzHLseaGzsVe20rkwAdPS7LKFlH7B1LIimPNgR644ZY8TaWeVmMwnuS9yFVEPE/jv8eM8kAgeN2F6gKwKRAMySte2Los7VyYJgBWYaXvOefsGbdRrzuda9/UMoqACDPcwyHFXW8MyFh5wvofob8pLVQ62bou9ozi+aO3VCtBB//3m1451duxD6bNuK4R5yAv/3GV3Du4w5BYhHCPUag483XmWbPCDUDY3Gg0KmEx5AEohgUXt8pelwgsIQ61WhGDSwDNQAwLcOJbMIqc+Qza2SlGcpZYi2Hu0fIeimllKUahq3RZGTdFMnazlhzoRs1oXlbgnIyPXZd1GkStNqgnhwJsHKLJSRi24cisezDzLTDdWSl3r9xLNVrqFpjEu/4Ud02gFuukBwwRQ4iTNrYpZLSSQGgsnRd9k1x9L2PdlF0teFeGmGJRVus2RZV2EvSglcYjaQJjp4+SYDemhb6m3qoBjWW7xn4pjNlFObKGk3jsH51SHq7ZMc8vr5jDo9//ONx9tln7zUfjTGYmJjAqaeeilf81m/i7h334GOnHAq3VKMCvI4YoI3U7Ut0fX/Uox6FjRs34vhHnIArb74uBI7w3GnPtFEPKjSRK4awyPJcolcnb+iK4sN5/Wlq63Xs9B2INIWbJhsHpyMrPaOQ5tnY9yGNmfK7fPcVyNe5XCoxuWUCo9kC2USOelhhefuy90I2Obwnd9LimPHJHM2uAVV5ti2jieQX9/d4yIJjTw2xzqyeL2Byg+GuJb9bUbMjtDf1Q0mUh+6kqO5apF9SQ+4V88Eard666Jvq6h3LqHcMkB+yioAyC971TAfNroF3tCi2LiGzlNLU7BoAqYFZ02YLN+vlDQCIZQYokrSypL3h17NLJZxuQmOdNOFFMhE0jsA1l570oAqWckZ5bd3KEpq8rmol7KFMEcieZUtpwXTLo7HvbC/dcR0xX6Kr1CoABp/sZQJTGpeOhSET1kwpAsOWdZvCPisNZK0AHOQC7hwxZSv00vSzgwfzvtHKjjf8xX6wkQxi7HWNIWePrEUAJkmBNGfQkYTXz4Rlt8DEJDBYovvG700bYGoSmJsnX2VpfEwTuF4XalggaVKcevhafOCLN6BpGhjzANaXfolGVVW4+ppr8d6nHInjVvf8BVmV3Fg64jhvLsf5pDO5MKdAsHO0gKFGHrdUUvWllROTD4xXFOJNzcoGUGuJLY7PCZkzo4LOBWFp5f5SmfDVBAa1vQRYWg6WfsJQx9UOdpPw/y4th43b0nLUKFiH+RzJJ1BXtIlsqqBtLkbhs2Upvnvlbbj2lp348z//PbTb4eL/YBupSVAphWR1yzOfZl0XyYYurVtzJCFxyyOYmQ6GwxIv/9vvAwA+8IF3YmlpCRdddBFu2bGEQ1d3Ae3GEvAAoNk1RD1feFYuLssrQ01awrDlE5ln8sZiyq3zjK/mEj0FbISmsbpogMjnVxkF1EFzmk2lSDopaU5zAtHaKNjGYXnHMnSi0V7fZhelBGaSqptSfXQsD1Kphs5y2jCKmwszu7qTe3kDtEKysYtmj4HdM4Ib1czMiysSnVv6XhJB9WSO+u4lkpV0tG+gDTHvBq5Xwy1X/npkl8rQ7yPkDuCtS5tBjWpAoU3GmDEAKddL0c8CxJQWC+VePa6hIKRgrcNw19DrtbNeigu37sZbr/opZosaiVZ4zf7r8OqDNmC5qvGHN2zFYYceihe96EX/5rxcWFjA3TvuwVO3TOPp+69BuVQFwG7Z/7qymC3pnF2zZg0AoNVq4bZRhfNuuhunT3axH2hDMdw1DHJIlt6I60m5VMHyNa9g6U+SKGAy85ULkZ2IlZ7IegBi2F3TkKxEpC1Vw5HPLMHIUy/tiJvxRFNv8gSqarB01xLSXobh7iHPY+2bKJuCLOA090+lG7rQnQRWK9j5AuV8iaJ44MCx/vfv8mAdwXt3dM8yLAPJlD30AFCk4o4Blm+dg1nVIi0Texebydz7DQtLLIEhShNoBC+ARiQSOwZINlPKkO4ktPAUNexiifYhq2jR6aecWufQ7BzCDirYxRLJhh7tjPn+ANgRQ5M7Bu8C9WQrxEgDwY9yMD5ppNPY+zP7pD+63Y1quMUSza4hmj0jWoRyAz1DE9LrL9kT1LMInJ7lgy66LX9xudeAkBUsrZOSMUDyg0473E9x81CS0c/CCjtHwFipAEqTFU19AoybhoCpAPRREVjuFaUnPzxTzEA4S4OcI420pTohAJyk4X2aFGgxK+ws/awN0JkE8jbdp9Mj4KEU0JsMpe9+n0CNlMZ73cD6tXL6rlo5VLcF1UnwgkduAQBcdNFFP+c58NAbmo95K6GSrYR8eD2xUeM+s1y6VS0Ozeln7PiSBIkUEKRFcwO4RfrPN6daR0BUosfjRlHZAK70QAZoTnXa9+5YIZuyNFuht9fA5GQAs3GjKsAyiHzc6UT0z+IrLjIQAdYi08kYBCtNc1gpIO8C7X44N6yFMgk+fcnNWDMzjaOPPvq+OnS/kDG9Zg1+dM8iXbDZ0jPZ0CVCoK69J7lqZ3BNgw/8M0XmvvGNb8T++++PDRs2AABunRsGT+OKSv0AYPeMUM8XqAYkhUg6qW/SMlFJ2VqHfCL3+taYWWs4AEEAs+iLBVB79o9/rosG5VLly9z0nIabyhofRgHA28YprZD1UmLj1nXousaR0FJ91D2yOTVr2lSx7Kb+XFGdxF9z1ESb/uvlXpqSbJmAnsqD7ai4fOQJN9Ml3m8cFYWKkFK6tugAAQAASURBVGMUJ8xqBbdcwc4XVHVlT2Vxb7LzRWhMF8adtceje5Yx2j30tnbNqEExX/omO3EAqYcVyiX6bzRL0hcAyDIVLlM2AGOTG/TWdZFw705nTQfvuvVuvOrSm7B7VOH5L3gBfu0Zz8Rf3bIdr7vuDrzmR3fgnlGB1/ze72FmZubfnJcfueACTLZSvOu4/SmIhfXPw90jDHaPsLynxOJcjZT6+TA7OwsAeMITnoDbFgZ45w1b8ZJrbsctReHnmGATsWqTOSO2bwJaZZla2ln45jyy0+O1dSr3UosY6HrvZtFDs8ZafraVxZ3zQ/yP7/wYl22b8012wkAbcdFiEF+zllvepwBzAIBRqHcMUN4yF3qroud4IMZDljkWut9Zi2K+9hGOtmqQzrSRT+YY7hqiPdOGrRuUdyxQg8OohpVAj05KF99I12RmOsQe7xgQG2GoucPMtFFcuxOOTdSHP9mOdHOfJmw3ITG5VtC9DNVt87T4iLyB2WhxtxBHDNVir2OZoKn2qV8ODZRV1Hnfz6AqS6A6klGonAH6gJsW5gvv3yrvS7cy+pxi/xM5NoiGy0UsiThxqE66d5hBnHQXN7BJh78ws0aHLbrWtEVT9wJ0o2ho/3dnifmS2+LGpfi+GXf62xVWbrF0Iv68wgC7iLUWRriuIkChI0Zbh+1lK6fHNhUBDmcJINfMzpmU3othUD1aDkDfucCId+D9ZlW3SwEQnQ5U3eD4A2fw+2cejvd97GPYunUrXvayl6HX6937CfAQHcYYpEmCEZdsY42+T5Fk5spfjMEerSUHGchFnn1b0TjYUekv0n4jJ9HhXj5hgsZeR3MYgDTlSWqdd48YFQjWbi76WeahpXkkTXjW0pzSCTWFxlUUYXetJcArzDEQVWN0aOqsG9IdK02bOlvLlxjOp6biOWlIXsHv/V+u3YaDDznUb0YejMM5h9FoCJMFn2LVIjZTVhiVJ3DDAmVZ48Uf+h4u/N5tePazn43HPOYxAICaj+8nL70NZ26ZprU21XCLJeodA7hBRY1nHFQBkFRYdKkCSjRbVdnKwhUELpaWHbQmNYvWjpRXtUOah/AIgMrjcWgEAJjc+KawfDJH2k4YlKesZw5R001Ro7e+i7pg5wf+/M66IEfiCqJqGd9o7pMDpck7rvjxB1WTXZICGQWzoUfa4IaSCNHYiOnlf5k91pMtYDUludo9ZCuoe+z9H7PyI9rkqVYI5HJFg2ax9OmCcfOdJK056+AKN9asJpsO8S/OWHYhILnVJbnL8h4K9ZheT014SW6Qz7Txwatux19dcTue/vSn49nPfjZWr14NANi4cSM+/KEPoeb0tuEwxFD/rHHX1jsBB9w1KDAxcqh8dHhYGqwFVvE1WCLcjzrqKLzhDW/AmjVr8LZzz8Vbbt+BC4870NvKARjbYAGATmuUSyw/MdREaC1FfQs49gEy0F6SIjp378vMAFa+S8tyUZnnOtW4bjDCxbvn8dXd87jp1KN95WQ0WyDtpcjZ0g0gDbKzzofCKK1gZtrkJANAtwyRlvMF6iWy3mseQMT60AXHzBbmEzkmZkgI7iwtOKO7SSPc3W8SblAhn8i9d6J0u5J7hSEJBEC748192LkCdqkMvostw44WBsnaLszGHuz8CMm6DuyeIT1mkSZgeshqeqzYwXHkp1rXRX3bHPTq9lhpmJrkyOpEcQenbgUmU/UzKF82bqDTVtBbjhrYUeGt4RD5R8Y2P27UUNToog4LZZ5CoQ66zcjmyrGFG4AAjMsyNOoBdFGOQwikHM36XJex9rPTDoBZtJUCUIU59vIKkYxIk5V07LeC7MJZAp7WMihmUJsyE20jkOy/RB2AgoBfef00D/cREK7ldXICFmkOpRO6jfXOrhxA5V0CvUpTIp9J6T5VQZ+lM8E/gwGKARr+PBgBWdfrT5UlY3utNd7z0hNw+JoeXvv/fRuXX/YD/Mb/eBke97jHPWg1of+ZkaYJhiVVWGTOQusxYAwgyIcMsVKKN3YO4E0eNUW6hu0T54rx9Etu1JOqCaUdFvC+xPFcEh0xvxc/vFRH/LhFHhE1ewJ0/GuqXPm5KjIdOQ/ipjwAaOlxJls2hHXkp93UQHeSflYa6PZpXjYVV2LScC51OkBd48abd+Dy67fhjW98yf1y/B6ocfnll+PmW2/De1/+aIpsr+i7Ui1ygqmHBX5y0z342Pd/ii9cfTdu27GIN7/5zXjUox7ln+MVr3gFAOC2XexEZC3quwee/YRRSLjxydtoRsCE7MxM8KM1ilg2cRdgYn8wDGoawCLNaQ6Vw8b/nHQS5BM5OQ9UFjVwr8BYyuHiymBa5GfbWtslANo42OUqVBS1ggODoMgO1Fsbet19JDUDSDImHyDLoObmAbY01QBVM1mDLJVLlRuYdV1u5KtpczJqQo8OgiWZD9RKNVVZl0rfaCdhHwA8my7+0QLcJCUv9iaWf6thjdldtRxS9Lr0usO5kopBvRTt6RbyGbpGfWHHLN74tRvxrGc9C7/xG78xtt4+6UlPwumnn46iKLC4uIj169f/u3Pzta9/A/7Xe96NJ118LU5eN4XXbpjBUd3WmI1elgL/yteIQw89lL4bpXDyyScDAB554omYu+YK9PeZIGCZBs/mir/vumjIZo6/26qw0XJEmzPR+VrrfAVCGYXO2jZGs8WYLIWcUzT1lDMzn0/kKBYKJLnBdMEAH8BdtsF+SNBd3+X5m6IeVEjY69lahySl5Mq0T+x8vWPgNwr5RI7mriWfENnddwIL8//+xuO+Gg9ZcDxf0pcoDHB7Mg+aXCAslqn2bhLVYuVTh/KJ3O9g0n0nqNyzh63NmLmVxrtkQw/NnhHZr1UNWfpwuUlO+nT/STS7hqS3YncKMZCXnbOdL2BaHWoYaVJfMnaVBQYVPW5+FBjtsqHyMGvGJA4aRlGCX9UA8jkBAgQN6Z6lscFM5rCLFYFxLk2TGX7wyhTLHtXLoGDHL/7xxZlZFjdqqGlPAETsRuEjmoWFyMPFHghg1UUgwGsyDWn+lAl/MyyBEPAq4BoIrGwMuOW54989K9gAriFQYvixAL1Hy6BcgK7WUEYSxwx8057SUKZPQCfNgUZBOUcMoTZAymWkcghkbQJbhl0FmiH9mzGLKOxgp026rySBSgx+65lH48kn7IvXffwHeO9734uvfuVivOjFL/lv4WIxPz+PwXCEzf3WWEOdjLFgBrmxMaFZSCLXm2B6D2DMJor8v7lqMleEaGmZhyLZEZ0vnw8+nrwqg+bYkmf12BgVgK6CfMeR9tlXK2zNm8MmbPYcSzBEz66T8aqGBs93C6R9Ar8Av+cEUJYel7bGzxGtgeFi2NBqje9fuxUAcPzxx//XDtYvcNx555047wPvxykHrcETD1tHzOaaHh3/xmHnzkUc8prPYGFQotft4BEnPBIvfdWT9pKRTEz0MT+/gD982L4E9lhXLI1gAHwUtTCe5EFMgNgkGlY2V0XYuAkBW3IZXzKByjLqpWyIRa6K6LrFzKek3NmqQZOEUrd4C0u6nkk4NnuSGe+qgRtQ/4zMdaUV9Op2sDk02jtZ+Dc3thGMHIDitX9qEmpuns6fFtnHieZfnJDsoIb9yR56PWaWJV+g2TmETy3kpnCRUZAjRAWdULKaYZZTaQWXOyhd8fdhfeBKnDooKXCK2fQkN8hK+gz9GQopSTuJ9ypOWLN90bY9+NvLbsfXf3IPAODFL37xvRIRSZIgSRJvH/jvjc2bN+Nd7/krXHLJJfjMpy/Es6++BW87ch+8cHoS7VyCYTJc8sOd2GfjRmzZsmWv5zj00EPxd5dcgqpj0OtkntwDk1pNRb7N1VKFsnReGRbv57MU0AyGlVGoBxWyiXw8iKYK0eNex83VEMffqdYUGnLCftN4b9PgD354G075zvX46VOO57RBul5nE7kPIBGnEG0URqzrBkj3XC6R5CNtUyPo39+wDf9w4/awrj4A4yELjlet6gMAkpk2lKRfAf6CCgCqcVBio6YV0n6Kcp4SfHxqTCclgBs3v2nly7GemRrUcP0MdsfAM016Kke9dQlmXQd2kbTFwghLGp+zZAWUbO4DqUF1y6xvrmt2DUnLlSZodg29VAINsV0Emmsfs2hmyB/SDaqweBsFN+AduSQ4FXVoeqjIBs4OarhUw+0ceOsc0WcCAEY1ge/cBIBc1/6C4H1hAcBY74pBjx0FDXJiuPTMfysrQK9gdGP9pTC1NYPlrD3OMFsGE6JTFoDhhx0H3/TE9I9SzO6qABbSnMCEbQjMKkrDE1DrgbW4YshCOcYWJuStLTIKILCBzgFJGoCU1gRwqtF46Vs+bzs0CqrEAO023PIyNnfa+NS5Z+B/fP+neMP/uRJvfOMb8fCHHYsXvPBFOOyww+71nHgojJ/+9KcAgKPXT1ADUD8LUeuR96k4uvjgFu6CB+CDcZwN0iEJ5VGpgR2VgAn9AdKw6qqGKiRxlLTX16sQw6w1AeQ4UjpOtJN/y4qkE0lGDHGShQ1dXY7r7uPqRZrTfZyj80HmJPi5AMDFDbIlaeFNGp4L4PmZBukHv7c7tu7G5GQfLYmlfhCNm266CZ//3Ofw3e9+Bwet6uAfnv8IqCREgatWAjtX4Gs3bcfCoMTrX/96PPKRj7zXz1qWJfq9Pk5eneMpm1f7hmxXWXIx4qYzscWMfYhTo1AuWWho6Jq+77hRL17uRB3jlToNoAtij2Od6Gi5QXu6DWsJBIqnbD2svANDw5pmKZOL9lSs2SxfL8Aes631Pe93LNIknwgo/RDyBgDfxKzabThnoRT9G5B9Bt1v4LopeSezcwV8gMg4sHTLtd+IeiBtnbcWlaFTjVanHc5F+Ru7PGW9jKrCswVspbxXtOaemyRyYmhYftHpB5vUtENJd01Fkc/5qha+NxrixZ+8HA2Dtle96lVIkvsOMiVJgtNPPx2nnHIKPv7xj+Pciy7CB/stvPXRB+A57R7KTOELuxZwxtMfd6+PP+6443CBtXjbD27D/zrjCL/GCVMsHs7WOowKke/QYWoaB2MUWi1ikOuFCu2pDA27WpA3NEd1L5HMpOFCZ5zKl+QGNRML4tl95mQPfwhgTTtDmQBqUHnnFj8fQRuQ0ezIy1ryiQymRbfJcwPAl+/Yhd/79o/pM++75j77/v+98ZAFx9k0lTb0qjaSVS2vxbVLpT8JBfiZVgKzpgM3qpFYxw0BDfn8rWlzMl7CTXjw0gi7VEJVFqZFpZdmJ2tl+hmzxhWSzT0vjTCrW6QnHlQhjSnT0Kvb3mPSrGoRc8tMQHXbPNJ9J+hD8cVftcg+R7Uo3cjvzKtoN19aQMp9UbnMZdrLNjR/pmbAzQ5yv5bljmzrfZMBeNbFAWGDkY4Hi7gBs9BVQwL/xI7LK8SpoQaB4jhOV/6NGQoPQCS8YDiuM/bpXxEgFlZY2F5gXHoRma0DNtwumlLUxBbz+3B1CRhLvszizRwD5Pg55WdpMIz/ZjJikB1/D1VBSXnaAFmHQEzTBOeL0XJoDAQCyOJGPSQJzjjlYDzxsQfh09/6Md7+qR/i9a9/Pf7gD/4Ap59++s88Nx7M4/bbb0eeGBw03fUbU9honjZcRh2RZII2bY3fpNBjgv+xHZWhlKwZGMiG0NIG2C2WQIflFdaGwA4Z1lIdMWGXiiTa/AnakSFzO457Buj9WWaQpVGuqahKUkf3AXg+WdaqrwDPdUla93LIm8UUqGmeIcvh6oIss5oaKs1pbgtV2dHAqMDmDaswP7+IqqqQpiusFX9Jx80334yPXPDXuOHGH2Pf6R7OPfUgvPyxB2KynQbXhZzXTaPwJ//vWhz38IfhlFNO+ZnPec0112DrXXfhr19yIoHaxZLDNUKZHg3JdFSH52JlgVwBRY20TUlhKYBGN3A29axeAsCkDmVlic2LihCyT1GaWE5xT5hY1yL2VBqjuBSepgSSy0GBnMGHTjXa0+2QAtmwRpTBqko12vtNBu/hlhnTGXvNvLw4QD0QQoqwPt5pcMonfBiN0gpqVMB5VxjnZRJyLRGpE8BuCBFwtnPUyG7WddlRht6v9BcAACLvZVc10LlCsVAQW94iH2TH35FvYizDqas1ewrXQGcqxT13jHw7wYbNgEk0Xvv163HYYYfjuuuvh1JqTG5zX44kSfBbv/VbOO644/CVr3wFr7n4X/CljatQW2CpqvHkJz/5Xh+3ceNGPO1pT8PHv/JlvOO5x6I9JHmKSTUa78nMn7sOfd90qVaoKgdAeZWYVB7KpRK5znljZpH2KPRMGkel4VM8vONUv2J2hEwp/OSxR0EBmOzlGAwGPh69WCggNnHD3UMvCVJGkYsLb1RMnuDHwxFe+vUb8ZOdC5huZ/jNh2/G8x6+D447/9v3y3HY67g8IK/yixjLewAQeNOTOckC+GS1O4fUUAYgdn1QSICKWF2XGjgBlzlHMzPzKo0+AmTdsjTuKG9WXd82Tw10VWBnpfnOsiZYsRuEZ5FZnyWaXsOdut6TWLrxG95ZVxIPrYMOWFOSneomwRSdFx8rns6Ng12sGMAq7hoOXcB+cMOgAoMK0YSJLlpkF6DmJe8SkCRQmhmyUUEX3ZUexgIykvAe5bFjx8VrNRkEpBnGvIyFuY3BadMArhpn20S37NjKzTcAJuOyjEh6oXTmf3fOBolG2gqMtZSp49d3FgAD5MaOS0SalWAcgR0HQnm9jupf4vbBUhTVJrmFYx2gBvCcJxyBZz32IDzmDz6D8z74QezcuRPPec5zHlKWb9ZafP/S7+GwdRNI5CILjM1bVzReBiTlWc9VMfKQhldX1X4doGCCinX90n8QtJdy0fEsl5+XiHSYhkAnMM663Vsao3eiiDaDQNgcORs0x2MMdArv3CJSIJHrOEugGAgssfysxqUfKskIGAMk4xkN6NyqG6xfTRWoHTt2YPPmzT//AfoFjeuuuw5/+idvx8Ebevj0Gx+PJ29YBewZQrc5rIL7NVyLXCbs/CLml0uccPgR/+bz7tq1CwBw8gFroLYv7+3Ra1SYDwDNpYGwpcZrj6WxqanYM5b/bYrG641HRTRNjCwJzjsBtloMHCsLw41XddEgyRmgCPnRBHChos5+sWorl0pkPSJvfGO2MMbM1Kl+JzRSx84qANfhla/GkD82U4oSiS5/awpq3m6ZEC4i70euH9Z4ByWvNW4lJF8RYMxBWIptVSXhUqV0XfPyFmYbSUpCdng1u4gU8yW0bjAYOp+/1Mrhm9KWloCqclhYBDZuURgNa9y+ZxnPfdIj8IY/+iN0Op37vZJy3HHH4dhjj8Xhhx+Ob379axgOh3jta1/7b2qYTz31VHz+85/HDdvm8fBex8sh5Lv0YTPJuKSCTG0UioIa83SLdNhpO4HTwSFFvleTJ8gnQvMiEBjkhkF1w/pm0zJo9SkFz+QJRVCzlMVoFfmAay/nkPlreA6mnQQfuewu/GTnAgBg97DEb55+MFa1HrjN+kMOHJ9//vk4//zz0YzIp9jMtKDaOVSfVhxTVsTq7hnRIiagUysgj05UgC6yy9XYiQvAW5s5tjuzgwrpgat81LMbVCGqtJOinlskbQ77r6b7T/moadVJic2QRqDJnJqKKkr18Wl+AwmsIMDazBcwa9pQ0JFEohn7LiS6WhYTYTlk+JJgZYNmzmjUO5aRrOsSyO4QwAAzDq6yvnEJLDPxvpfis7hU0IVCgjTirn6t4SRtrKmhku64bo3vg/JewEeWjnfsr5RfSBk6YW2mUoCLnsMYAhJjfsURG6c0VJqPN+nx31TWDqBWZBzS8JRkDMojsCzPmWT0uwBo+R0IEouaXFXoOXUoqad5YA0T1jWLZtta+u7k934PenYOX/qfT8Wff+oKvPcf/gFr167F4x537yW5B+O4/fbbce2PrsM/vvREukEqKVoFRgpAcfcysigqWCorqk/d8NJIqjspsYk8v/VkHrT2POQxjiVIbqmE7mEFWNDh9yQhJnEEIIvkFFnERgszF8ssBAADwSsbCE2hTR3kPwKKkyTIiaoRN+0ZYLQM1Zn04FclGZytKa7cyXnhQoNo1qZwEgBIDB514iHYtG4S733ve/GOd7wDeR6x5L9EwzmHL3zhC/j4xz6GRx2+Fhf98RPRhSV7NVAPh2scdEXnCK3N1ES3abKNbdu2/ZvPf8Xll+ORB61Ba7pNqXbzZJtlOknYkImtW+OgOhrgWGVYB50bpFqF7vwOXz80pduJZKIDO7aUyb7JNQ6JYaYz1Sg5OU/SysRWC5X1Ws60nSDtpEh61IDdDEKCmrPUlKdyYol1PzRRyzVkrEqlNemNrY009DZYTkabdlWW4dpiHdDrQonlYF1DiY6aq5GuaNglSQMjBZW6Ma9/s6rFRFOXrqv8nqGln4avR60EZkSEU2ffCbJGHTXklcuvBwD1qEG1VKIzN4LSGsPdQ2aTHX2kHrGpaQrUZYNXXXUrytriyCOP9K4UD8QwxuCss87CWWed9R963NyggtMVknUdmuNLpY8nt4Udu7zGI8+Vl1poBSQ5294NKthuCmWV13nXQ5I+kLa7HG94bByKeQq5UUZBT2g0hcWen+xBPpHD1haVdagHlZdWiJeznAfSTJj2Miit8NQD1+Ard85ioajwvGM3Y/+Nk5hnu74TTjgBxhicc845OOecc/5rX/rPGA85cCxf1uxVn8fq436NbsxS8tTlRgKVGKhBDdUNO06xKNNGw3XIJQKS4c4nnGKfTJVq1DsGXpule6zD6qQ+plZphWZ2RAlnrYSkGFmLdljblijmFiCmek0bSqvA7LYMMGqgAd/oR+xVBcwVwUd5sUSyrhsW4142vuNrJUg6KZo9nAbYSX2sro+LBojVtg5otG/Yc43jchtbt+yk4BIP4gEfo637ITnJN/8VzKDF/q/e6k2kEiCrsqamRbSVQ9mwCdjLDUCAoEQ1i+erSQKYlY59cYdwTQAuzgEKQaKhkzH2jbTQLNMQgGxrcpGoKwIRAkCcA1nQJcFNQECy6EZFUhFrnmP5hzCDzkKl5LrhhguBBazLwCIKW55aigYWmqnX8que6nYxtTHBe17xGPy/f7kdt99++891zjxYxt133w0AeOyBM5ArsRs13obQzhdwVUPm91zC1f2M+wW44tI46E4CxaVaZx3AHt/IIhYw1eRO0cqBsiLHkKWCLaJqBkNRmEtZARGx5AAoGx136cBKIjBtksAcNzUgWvisDR/QYW2kg6/HwTTA81SFOcOae1cOyTEF4CpIQrcZaeqraM4lGTeHSphNglWTbVz0oZfi5Bd8GJ/85CfxG7/xG/ftgbwPhnMOH/nIR/D5z38eL3vSYfjAax6Hdp4AowK6b+FuI2ZS58Z/382OZejVbehehjOOXIfzLvkBBoMBOp3OXs+/vLyMK6+8Au988fFQLUO6XCYbfK8JM5vyu2jgLTeSKZ0CRQPt12QOZQCBXZ1qArmJNDyxY0C0L8/b4ZeE53kljg2jBq1VLfIvZh/lJDcw/cwDYwkmac20PRkiTXiuslC59hZuXiok10oeXiokKZGyLkudnuekSslj22lFPRMAJP0zbOi5R4W/LwDwgT1aA2DZh8hUAG5A117nqvptkm1wNVevaQM7Qe4ZAPRkTpLJ0sIWNfX4VCP2+6XvXcr8NUueeA8AAPh/98ziK3fuxlve8hYcccS/XV34RY9DDz0UBx14AJ77v3+AVzxyC177+EMw3SG21rQMMAKAwB4XhUOeqzF3Ux+y6eC/Y9FlpzznhIEWVjoeIrcohnQfk9NzyAYun8hQsoQmm8xh2dM7Zp2l0dTkCRJ28Xrao/fDr51+IOEaeX0+xpdddhkmJibu1+/2IQeO/ZCLR7cbmMYybJ/0dDewlbLDTVO4+QLeOi0liYLmKE0xo4ZRSLdMwM4VQA7o1dQ570akmbE7hzAbezCrWqRXTjX0TJuec9TQScyTQ3O0p+qmMN2UgLNkyWca6RZqPFLWwaxuUVS1oe5iSuCjk1/1M2oyYg2yX2FTTWCgTz6rdq7wGmGJnJbFXOcJbFEjWcOsWysh5lxKXWDQ0GLP6LkCZjV/Rt4YqFzCGQQQNwBKWnCB8L54cfVxyZ026W85Hpd0kQVUzgIx8WD1oQtZABiSWCdAVLr7pfycRwlfSkMa7QB4MKt0EoBG01B3v47AikgvxBJLKWLFLbtbNBWDZoQmP+jAVMttApQF/GbtoBt1Fqo9AVcX8C4DdQkkLty3Fm9b/vwxcykR1IMBDt08+e8yYw+2ceWVV2Kyk2F1lsANa9a3czPpKG4O1cHTVprzePML1sgpEzWoil65bICoeddXJyb6NP9MCT0zETYmgyG8G4WkLXoP5JSa8mLdvGVJj1yVJG5crAYBmi/yr9IBJKctdmYBzTkTSXFkI6YNVUq4WdU1JVU8LM07JXZxNVsHwoQY6QzEPhdkH3jcsfviN597Aj75pW/iRS960X3aiPRfHdZa/OmfvB1XXHkVPvy7J+MVZxwOSN9DKwcWhkj3n/T9GTRIOqCnOkCW4uynHoG/+NL1uOmmm3Dsscfu9Rrf+973UNUNnvu4Q6DgKOAio2NI1wG+WHNlAoCfi3oyp0hlSwEyupNCNxZqUKOyFvlkhnpAPshJO0VTNN5hAYB3WfB2oVr5KGSAGp+EXaP0MYMWB1UYrjzaokE1rJBP5tDMunqAkRovu9OTeWiSE3TI2g4lgUgin8gyQpFSzXCO1qg0C6E1gyHUSNPeNY4zl5GAgqNKuU4puuZ2Urj5kQdCEv4RV211nx0ZkgTInCdfTD7kqi2dz2Zjj16rbKDFlcMoFPNk5zqaLVEPa2x4xHroVGPrsMD/d+VWLFUNvjU3h3/ds4gnPP7xOPHEE/+LM/X+H0opvP1P/hQXXXQRPvqFz+Pvr9qKDzzzGDyt00HaptCXGMsqpWAtWG+MqCmP/t5UFDEtMgmTaqANWN5EiC+yyROgqLF9boQ57bC/SZC3yVkln8ywvJ3tcqe7KJejiO/GkRRIu+AgwvM7yUl/LARkCDirCcO0Eqjl4gH7bn95Vrz7eCixT/Im+SY0v2gNlENabCzZwEjZXk9iXJ7AuxpvnM6m5K5qkBwwSWCzS8ypAFSvvc1oEfKsKhuiJ5v7/mfZPUuindnYI//WKWJvxXM52dwn5oObhVxRh5+tg6oauEL5aFPdS2B3DvxC4Qa1B8OuqHkRAnQvgcsZNCxzyMmAvg87X3BThIusrmoKEwHIJWDEuk1JRtLKbyRUOxlvqgPChqQsAdsi/SwP5+zYQqqUZrbYhS6KeLGV6GhjArMaazsFmIr1lTzOOWKUGayqWEZxb7ZyQGCH08gLGUAI95Dbxf1CqE0b3ovYa4kUQ684/UwGqBqKwY9rSnpcHQF6Y4BWB1haDIAM8CDZDXcDWmPdVAs/3LWAh8r49re/jYsvvhjvfeaxUBa+kgOAXGCiRinRUwKA73CSIXMg0o8ScGj8Ak4JlDqUmEuWJ0x2w+3WUuQ3MMae+e4Xm4ZAEMBXd+g+VTgvZL7JBkekE95ikCOkywE1bQIBQIPnhG2iuWqDs4rERMvzy8bQNh4wI+8Co0UAaZBmcHXk5S8+Bef9w/dw6aWXem/VX4YxPz+PK668Cuf97il4xVkMbFs5sGeO12ZLa2m/BbW6t/f6A+A71+8AAJ+At3J861vfxGnHb8GmtRPAYAhbNj7vh1JCQ6VMs6wCAEkVqoYqg8s1+cuPasBqaOuQGYV61CDp0MXXVpYASC9oKSXIQkYzqr1fsoQtmIQYUImIFmclAcb1qEFrmiV5bG+oe6mPaY4rfSpNAiMcJ5sKCeE7tqLNmDYISSqGCAVtRcgLVbfhFknaSKRH4cGzAFyVabhhETajmt6Xnsr5aZPgMc5yElfZ8fOHQblUOU0/g57pw+1Zgi0amC2rYLSG3TaHfm5gbp3Dzm1LuOu4afywrPDJy+/Cl664E3Vj0el0cPjhR+D3H/3oB1VD8+TkJF7ykpfg6U9/Oj78oQ/hhX9/KV50zGa8++BNFMmcVRhwK0JM9sctEXEPsTKKLAirBgDNS1tR1LZONGpOvrPW4Zw778ZVC8s4abKHdx+yCfusabHOOASvmdwEiQcHtFhLIFlphZT7PpLJnOYoe8urnI9/Flc8HjgHnYcsOB7rFge8NgqDgb9dgRZRV7DtWFFDtTOoXFM6VieBm4+y3CGsVOIXHCqvcbfmrqFvOhCdlO5lVK5lVsotV7B7RsSw8iLuBhWc5ou96CK1olQhLoWJKwV12FckZRg1cKX1C4idK2AHFcy6LpodAwIKHMkpyU56KodbllIa2H+yBsrggkG7dJqgzY5lCi1hCzjdSeHAHrE8cVE2cBUxJL48FwfVj5gJlQa8iEV2TU0gIkupa1nShbJ03OOoZPZZ5BZ11ATXIIDRGJiO+SNHQFfAhyTTCQsXW2cJIx2zHgABC1cBJoXTCZR/LmaqBSzHjDFAFxKTEkAW+YXXRUcNgVVNDHFVUAlckfQFOrqPrSn5zFuH8XtLDNT0NNxgiG4rRTEa/Vynyi/72LlzJ8774Afw68dsxiuP29czeHKuSbOrXPh9445ngMW+MHgbq5SBwKjwLHOzc0id7o2jzbWwVPFVBOBScU5VDe7od8NhYJoZALuWhvKNe6JFteHfuibWTSoS9zZiDX1d8txxQQYEeIDtfGOo9WyyK4fEHjcNfOBkksKNlmkNjDdoOqErAj/26CP3xemPOQyf+cyn8ZjHPAa/LGl5SZIgy1Lc/NNdAcSVFTH81kKvXuUbw7xlotiNjUbYM7J4zYe+gyc+8YlYu3btXs8/OzuLa6/9ET7y1qf5tUpVVAHTqYZLZZPL57ZRMBs6XtYjBIVq0XyzANBYIlkGFVKtYCvNtmGpLy9LY53JjdcpNxXJ9cTWKpsg4CiBD7IphGFWm0F12k+pma2TQEeafMsMq1ndomtCbojJFV08s+9KYs0BrrsboDcZ5qFUH6yF9+WWqp3lhM+Umd44yVE29Akg0ezEuFehca9soLp8/CIgjyyD0rVntmVttjuXAAB6VZvAcq8LVVYw+64jJnvNDHSrhXrrIt734234wLV3Yf6rRPDsv//+OOsZz8JTn/rUB1RbfH+MqakpvPFNb8JrX/ta/MM1N+EPDt2IdZ0UZqGE1uSKIktYmqqxQqxXM0pzo6yTWsHWja9qiPynGtTIJzM8YbqPqxaW8f35JTz96lvx18duwWN7GXJOACbbQkUWcdGGzyS0sTMsL9J5aAgFQj8U4RYTznP7wF3THsLgeJwp8NulLANGo5DY00mJmQSfiEt00ghw9dICgH1OE+qIB+/IjWL9b3B/ENcKlWrKn18qAD7Izut1GyhugjMbegSYGWiL3RtNCuUb41SqYfeMqMSQUpnQLpb++ZBpr3N0VUPNDEXjDdfpfnXo8s0NM+dk8QYAyDSX5vh74xAQnRtYa0NjE28MlFZQM/difF5WYYMipf9ey/+sxAHCH58GsFVg21Y6WWjtNePej6eJtsDSeCeAM54D0iwni7e4Q7CfsVupOY59j+PhGT1i3xSDYasyaHA53dbw+lClA7viZR4MwP17SH0lAwDQ6gGjJS57AzCGvqu6hEc3vgYWNTB6BpKeu5MoFMVDAxxff/31GBUl3nP6ocCQdO7U6e78ORdLebxDBdu8wRifjgZrWYeqGJxqfyEwa9qwcwUxV3VNjUcAl5RTllFYABkwtwAnVgMsy3IL7IITdfqT9jiyxIrnssyBeBMmmylxVBEtMcANm7WX1zhQsx1/6PAcdRNkPQA145kMPsrcatIbM3j2jzcJfHIeg+y3ve7XcOoz342LL74YZ5555v1yfP+jo9/v43nPez4++A9/j9c99zhsWEugGJ0OybNWrQI6XQJxgyEBNbBMva5x2mv+CQAlm93buPTSS6G1wjOeehxg6XqgAKiUjr/lKHLfBNpY6v847iA0N97OAFl7dyKzuuXDoRzbjKlRDc3yCcOexPWILMkAeIeLxFooPq7iIyvssUQx+3kvUb4i1WM3IQcAtiEv/HUdWrdzA9XO4ePMsyBZUBInniS0CQdC74M0DicI0i/H8jJZW21Fx8ID4wRqMARMAiddYdInkRhgMAyBIeDrqg4uBuSuVMIYFeR0ItfLUuh+CtuQ/En1+0Bi4NbOQO13IJxJcfn3rsbu27fjLz5zNb577V146lOfhtNOOw3OORx88MEPqXRRpRTe9ra34Y1veD1ec+nN+OJTjkExX6DTLsPhifbwwlNZVmWJFt41Dgn3KNWDmnTFfHyayqI904atGvzeUZsxqxz+5tYdmC0rnH3ZzTj3yH3wqsM3+u9V9MoiFZKKh0k1TCchhlg2eoIpGO8oo3w1gtbUB+67fOiCY9+gFTWECePYakFNwTOaYvPmwGB2WEJB+3Q7JwDUOqjlmhroBPy2eSHqOGjQIqgnc69NU9yoZ5dKb8MWdHA07K4hpd/NFb7E6z9Gl7Z14jqhWgZIDaU1yRBJQ0laOFfU3knDDSoqazBgED2c7NIcLzyU+gcgI+Ds9dXCjrSSYHsl+mMpQY9p1fg7z5Jxc0X5O0ciu0h7Kbpj5ZmHqOZTcwdzloZ8VSlNC4scJ92JPVV8Gx08jK0OAGDLsb87ZwFVB0Ytbn4SVwCxaROGui6gdQ1neB7Ekgt5XR8Y4gJIl8fbBoDYd7F2VJhlgIE2N+zZmkJD4vKm6KsBeg4AatUUti9V6E9M4cE+br75ZvzzRZ/FhskOpjV5nUqqpRvV7CShvdZdbBYVzw2xHURpoXokS7FLJbTRUJM5McQSgbs4oIYqruzoFjuj1HyM5LsvS2IppcVbZBerex6MeW/YNAtAGAjPUYNWX9nU1CzHyFrwMeYAzRPx1raWvImdhcq7cE0JV40IIOuEAGA1imQXDLAr0lr61/duKTZsJKX8YOL1MsHJjz0KL3/p6fjoRz+C/fff/5cmYObMM8/EJz7xCbzq/EvwmT9+kv+OVb8f7pRFV34Aqm5wxxC47pYdeN3rXvczP8v3vvcveNyjD8X0+mlqfl1aJtvEVgsYDKCTKjqOobPJ3nEXVCeBsrQZUwL2Rly1WE3AGOwvb4saunE0H1ONTNZkBsyURmbGGWKAqokiCxK5TsqV0Fbmm+08Hcie/noyp+peJw2Wa/zeVbsdrpVJEmRsIuVKW/ANytF3SvPJcsWsoO9c+jMk7q9ugF4Xbn4+pH4mCbC0FBjrahQ1s0cViopfj3tjNHmQeZCs8hZcrwvd69JrTfTo8/S6UJNrcd4nrsVrfvu9AIglPvfct+G44477D862B9eYnJzE2c9/Af7qr/4KZ3/jOrx1y3pMZwpZFZjbvaTgLClXmiQVkrQoMc+2aqBTg4IllbZqfMLdnz5sC/aZbOFtV1ED+J9edyd2Ng3+5Ngt/vmVoXhraboTxtiHzjChKIQkGgvVj4iGlZLKB2A8dMGxlAfk4mQjzZ8wyAzcdJJ4n15bjHzTnW8qY92wYrDY7BjQwewmQLNEZTRr/eOseDo2jmxseSff7BlR0MfEFJWHtIZKa4DdHlQ3JUaC2VrVTalknBtgD7PRnQR2zwh2nlnqTFNjoFHQq1tQIBZb54kv86l57ZP+dD8b7xS2dsw/0g0quLJmv0lu2ABpnAVcj6UcxWBNAK24ScR/A4I0QqJquTta5S1K/IskFn4jI+xxcC8PLIc0QznLDg4Rs2rkZOLbxH84lmOIdGIsVESH5ryVwBpyQfD/88+v6gJQCs5kJLXgx439rlRgC6GDo4Zt4P1qG2Hwovcp7GFFzACcC1KLakSPbWpqrtIJMDWJm3cOsXFDWJweTKOqKmzduhVFUeDct74V+07l+Njzj6PzjLvPvfQIoPNFQgY45Q7xottJYEFzGAD06jYkSlelXMXJGvJ3LYnVg9FBWwyML86JCcCj1QobOpnT1pF8S1wQVp4f7E+9l85drPssVVHgLGmCrSUAzEylMiaS3XAFpimhdEL3cxau4r4AkfNIeE7WjmRH9fjmKu8GdloqKAA++K7fwNe+dR2++c1v/tKA416PeikWR42vNLkdu4C5eS/dUuvWwu2U2xJgZjVqPiY/q9N9aWkJP/rRj/DBP/912qT0CCgqiRjrdmkzL2a5ZemPv5KmtLqhykXJyaqRm8pY1a0hgKencgLL85RUKBpmVYV1SXz0x/yAef77yPTI39YNKqhOSuu9NEuLK4Wfh+TGotqdgI6EMZa0RpFLyKbJimadSYiYjEjE49gG9x6xCCwt1OQkXRtyDbe8TOfHaETvu52RlDGNXDMAconRDP6l2gOM20t02kSu9Pvhvac5ltID8b7/9QYAwL777ov3v//9DymW+N8aJ510EiYn+vjyzTvx5Zt34sIj9seheeb9tKUVI17Wsh7Jd6jJM2FLtxpNUTP7W3tQm7TTsUrGqw7fhFM3rsIbr/gpvr99DttHRN41krbH1RGdUKy4XuGtLUN3UuoZ6aTjzfetnIinsln5Ue+38ZAFx74hT/GuWHbEgyFp83gRA0CAiHVrCvCWTarFXbHd1DO+3rxcKH8p84juLUu9KXp92xy5QewZkoVQJ6WggWHhLYEAMGBl1gDwXqx2riA9rw0MBEDlXz2VU5JQZaH7zqfXeVDPTXRmpk0L76CGWccXa0PA286xHzGDTifx1k3jmQqKjE6gJ1sEisXLWBZu3XiPZaVt0Ggm0SIsQ1i3XjcAG5BWMm7M88A4/h0ImmPWqgGIOqL5+eQ1FQPpMaaWtZgK8Ol5svA7R3NlrAmvCYDWRu9HJBziIiCBDGCQLFILVUM1JQFkub/YwAEEWLI24WzL70mAkrDHXs+McPFxFsqRdy0AbqSixD3k1E1+y12zeOrDf7Z5/C/bGI1G+PznP4+rrrwCN/74xyi5eTZLDL70m6di7TIlVtrKQgNsQZgGFwppBOUEJ8VafsWaf1VZ2D2lT79T7SxcXD0Lp0mvyOe/B8e8qVPdLl3UAa+dVO02gaXBcExn7xYHUKtX0d/Kyge3jF3Uk2QMhAIggCxzwFpKt8u7442dANDq0/wx3PjHloCANLZyA2hdE/MsoLdY3ruqIl7abjE0/YksSBvkrRYOO3Qz9uzZcx8d7ftmHHPM0ZjsLfmNiTIKbliy//USsG03dIebtrIMSmnss6GPVZNdXH755Xj4wx++13NeffXVaBqLpzzpODq3vaf00KcaKtncyzXEWjrO1gIIOnR02lAyf6yDYamE8xu8BoChpDGRX7D7kKuasRCPscAl66Bko240dMoe/A1VQe2AvPzNZB68gbnnxA+WUnhdcMzQiY7YRcQCuCpmeDOWZEEDH+uOAfigGgHJcu2VsbQM1e/TdwYQQM40+SKXVXgvo5G391RcvbV7htAzXTrfpibglum50MqBfp/ew2AZar/9MahmkPcn0e/38e53v/u/DTAGgFarhY99/H/jjjvuwO///u/jJ4nFURldaGJZRZIgAFdhczWto80oOKgAdD8j8d2S+cDyF6UVjlrVxT8/7nDsKCqs0VStFhba5Ak16HGkNxpHvX6SMcGWiK5xNH9TDRXL0ADCafmvGvL+6yPlTvJsxRfcyqFKtprxF4c6lFbrGmpC06JmLR0gmU02NL959lJr+KhhEfAwm5QcnMDes0gNbhN0m5i4AyDphUwGBAbAlxfYrUJxTj1AWmI7F5qPfKoexrWWPmmPfY2VxEELw2YFSNMCaOfZm7NlPMAgPXU61jEsIFTzRFfMmnvRPDAuZZGho++Nf5eGIt8sI6CavY+99m1s0UZ4DWvJTzOLGIv4wi8gJF4UJTlMI9yXtcG+wUnARfy+vTY0aoyT2x1AZXdDr9VUpK9TxEILc+xMBmVrwDUByChNf5fnH1vAwwYPsOOvrQ1t5BIE9obB8+yeeczOLf/MTvxfluGcw49+9CNcddVV+M63v4XZPXvwxMPW4sXPOQYnPnwz8tkBVk+2sClNUQ8XUbO/a7VY0UIrASDxRZ/PBz83pRnWKLYzjI4dy3fI2lB5GZBYvI1VKgQAAbw5s2Fu8obQ7ZyFWkX6VzXZC9If6fSXmCpv7WaBXFNDqY6OvYAKjm52VQFvNwiEDVOa07GP0/Sc5UhyELhxGmMx6jKHbB02efG5KmwyQNPP0fmRZgnqe0sR+AWO6667Hnr/ae5FqL0DEIU4aXhLS89ApUjzFL/14lPw1//7G3j5y1++13NeeeWVOOTA9dh333V0g3P03ca+0okFagPY1FfBFPdDuKam7z9LA7scb7JEQ6wroFB+buqUw5ZSDaQNuQ9ZB5TWS+TEVx+VhYvaCXyKqqXrhff55chsqqKt+C5iQkHWZJnP8TqaBjDieG6Jw49LeEPuangHoNiKEAjrtUyxLCVyZMQx5oldocVPghTDf29VsA8d1FBDOg/1urVw5SJ8Uoqs32UJpDm23XwrbvnRNXjOs591r17WD/WRpikOPPBAHHboIfjgrXfi6IM24QA+Lv7rZmDs5RRae613w17Vhv8uOnjRvMfAGCD8oY3G+lbmPbuzTuJDRLw9YfyYQU3BOdYFnXErgcrTUGGLKsKxi8v9PR664LgmGxlv4icXgCShk80kgAFpOEtNC4TPV2zGS6itkCnvPU9WHiTpbOeh0gxYNQWzbm1glmsuf48KmCkLDIaob9oFvaZNqUE2NLnBKDhY75+suJGD3DW4IVAMtDsJmvkCGFEjiEo1aZh5ty2yCbtYQnEDIAQ8APS8ZSjvkVOF4ecy40w5SKdM8gOE71S+H1nghFVZaRyvVZC6AMReyP1HhWeCVbsdnmdUhIW7bmgTMyr4OyXbLK9LjoFxmrOUgjurvT0Ws3WscVbemUKa4JKgr1uRlOflGRI7LUAmbrhTCtqVsMgIdAvzIkyvxEanqWeaPaPsPZIRNUYh/C5AR2mgLqG0gUtr9lkm7d6Pb58DgF/q6N/du3fjvA9+EJdfcQWmuy08fr/VeMuzHoODN0xQI2llgRnSbtY7BmgGNeoiAmcmDdp8YY25tOyqhiQUeUIBH60EyrB8gu0HnS6gJtqh9G0tWbNpDaxeBbWwGICsgMIy2nDVICa5KmkuliXUhjUkpxA/WIBK7GlUxwSidUQARA2YHD7aHAipilLZENlR2gogQGzXRIKT0ZzwloEyP8UrWYbMIcf/yrkiwDi2M+T3uGHDNK750XX/+QN+H46yLPGd73wHTdPgxKM3cbyZg0psOFbtJDTyxFraNMPRR+yLxcUljEajsUjgoijwvX/5Ls55+RPCueevGymQTBEYrEYsd6nZwozvMxrQPBoM2YM383p02tg7uOGA5kRZQpXVGPECDqihPgyWWQCQJEgAzDbDBzd5+8JUU8Mf+x0r1uVL8ypVOdPxjVndEHtXliRxqNmDP40kFQAU+xq7poYDr5cm5c/Y9nPM1SV9P7LOxemO8QZNNM3WklylXdH3AgBgyVIrpw0Ir/2qk8JxoIm3T52fp7+vS+BDbJwFplZh5Fbjoi/+C8pihKc+9an/len2oB9vfNOb8Wd/+ic4+7qf4v0HbcKpkz0PgHWqvZRCwK+tGp9cZ7h5TkCweG9rBsY6JiZ4mDxB1uO5E7lfxEBamkK9VEz6l6L7xJoPIcpU9sBB1ocuOJaLQVmSL2w8JPRDgFHOPowAHYy49Bk3XoiEQhs6UCYhiYbW3jJoTJOo+YIVlwV0TsEkfNFN162F23EPeahKs4E0Co4KWI6vJk0qN/RxmpfhIBA0xjdcgOOsZRF1y7UvG5PF27JvQISheGw3ahgYB+9k8A5ORuwbqxKMg2EgMBNAuPiLrhsI1kqxpljuK98vP59raqiRhXNDqG43gOG6Co1QADXo9boBMItLhM4IDDdVqB8BYYH2gJbLlHL8pAFF/jPZOKsWA1UPpPl5fRMUPMjRqoZ1CcSxwiKHQgOlbGjc85poF1wslAmfBUCj2jCGy+YrGW9noZwDbE0MPIAbf7IVALBx40b8Mo4f//jH+JO3nYs2LP7+cYfjzC0zaE3lUKKTl2hZrcaYAmGOdapRzpfIJjPvviLzVcJs3KjxemM0FsiMt3xzo4ZjgFnPNmSHmt1zUNNTND+mJqlhSNgrKfNay6EgQyDPx11w4vJ0WY07XgB+Y44shZudJQ0mwA1LSQAkwuwKy1sXYfPmLMkpYvmFl2GAtaIZVF1SmIzWxCCXwjAzWSB2XNJwVQ6DW4UMzayzszjxxCPwkb/50s9MlHsgxtzcHL74xS/iS1/8AubmF/CMUw/FX/7u6VB5DtezdHzkfXuPXma7RGY1HOKYA1cDAD71qU/h13/919Fut3Hdddfh4osvxtLyAC87+ySSWIl7TSRvcsZ6/a3iv7m6pPM3Z4eM/ioCiSVplOkNaCr397q0gSqrsLEC6FokgFUrqFYO0+/QdWB5BFc07CnPoDil/gTHQQoAgF5G92NXJWl28sSNr3iwjtqw5MR/Xywj4c+t0jxU2prKO6M4y17sAH3uJCU2Oc1JlmNrqnY4+a5kHpdAZ5LmWs0ylB6TVVrBJ6DK+WYSOLmN3Sn0VO4b5LG0HI73YJmcNQZDYMMWFE0f0/sdDICqAaeddtr9MSUfFGN6ehrv/Iu/xHve/W78zhVX4G+O3oJTpyci5ndvgKsjUAwEttiwXHUM6GrWgyfEMou3MYxCOV/SYy17caea+kFy0hyP9S81jiRFrcRX7j02ULKm/kpzfB+MqEQp/8YCfr/bHwGWdTZSqpdFSkCuLCjc2KfSDK7gDvAkgc+dl6E1yTnkJPdlIhMYTmsJpK+ehppZTSc159C7TgnUDXSnhL1nnuylOokP3ZDdljBnup9Rw9Gg8sbZZqaD+u6lMZ0Z2cNpz1L7crRR5DMpk53dLeRnlZrw+cdYYZ4+dQ2yJHOB4c2yoCNeqT+Wx8uiXVaUjFc3UP2eP1mVaJO52xmjETXXiDZtoh++TyAwutYCowFCyIIeZ3hkoRYGRECvtfTdyt+UpsdqgzFJA18sG+TQqoJTGbT4L+qEwQwBGQHISlm6r9NwThOjLMy0rWFNG1qXDEZcYK3Fp5V/9o8x/L6ErXYWqiI5yo0378C6tWvGWLFflrFjxw78zz/7UxzWTfHxRx6ImXYG1BbVYoWkIps11ziKbue5pyI9nKSH5RPGyysAwNu6pcY3kwJAs2tIDR6GvjOKjbaw8wXqrYswnAYp1o1YWOQNF7naYGExNAiNCvqbOK44G9aHLNpIeS9XE9aG2LmlrsPtsokeDWgTH8srgHGJgwUBMTneTcOadR3mhNgWJhlt3otlqCSDE517NQpgD2CQDHoOYd78Ro/mprM1TjrhYDjncOONN/5Cuv2///3v46/+6j3QcPiNsx6G333BSThs/zVeuqDabTiWZMk6SmuOCZUqrvodc/gm/NGrn4K/+vBn8eUvfwnr1q3Drbfehk0bVuPP3/RsHHzgBg96VYIgdzIpAcC6CsfI5FCdSWbsxQWEN6tNTWuBMPurWTveoqqmGrATRhGBeiBcf/g21WpBjUZhrvGccEVNTeImVE5Uy7IcowkyDHluYY4z1qmLw0Peos0Wa6yFGUa8OYgsA5Vqh01cVXAjMM+ZJAUaBcWg2pVD3tSp0Fic5mHtkmukVmMbBf/5pYdHizZVkS2qVmh2DWA2r+KmPgbVE5NQvWnMjfbB9CYC0R/9m7/Box71KOR5aPT77zba7Tbe+KY34Z3v+J941dVX44pTjkA3TcZALkDSiIS9ty3L1mLphdxH/hW2GQC0UezXHQiNtJOMBTRJboM8XpxY6M4maOu9jNIClnTuSifekvaBGA9dcCwnqwBhsZApS2BugX5v5QG4Li1D6SyUPgG4oiDgJLY2vqTakBSglftmHbd7NzUGrOxMB/jk5wmT8aLTygOT2Z2k/+oSKIZQc/MEGGtKstOdhJrzVkwMVzQEjOcKSsWbzGkCao367iXoyTw4a7QM6h3L1PRRUPOe0iRH8NY/eUL+nHyb0oq0P36iRguYDPGAFbAv5UutQqOdl1Uo38BCF3vrn0PZTMJ4AhgRhlhYO/lZGpuiruaxBr66GQfMosnVWdSUx8fZOboISrlabNvkAqE0LAjcWnB5ERoJhnBOo3E5GpfAqBRaVcQKJzlQF3BpBuc0rEuhYKHQUIAKGjQ2R6ItnJK/iyRD3l8E5BVgXQg1sC6FUnS7TiooYRSVBopl3HrHLqz7JdUb//WHP4xeU+ETjz0K6ybbaIrasxP1qEGaaqCoyZkCoIYj1k+2AVSDGvWwQrFQIOdABFs0MK0EOhcmlSsihqLT7VIJzZo2n4THUe52z2jsdjQOqBYif800uK8A4WJeVkFCIfMe4Lla+Dh0uk0FK0JfpjdjTgfkv84AWTyuV2yQxkJfLABVE9gFgjtAPJyFMhlcOYjAHMsw6irIgaQCJK4sMpfk56bGYYdswj77bMCll176CwHHn/rk/8VoVGDnd/8Y01OdqCLF1TbdpsCVfgSsuHTvy7MCDp3FO9/4azjnZY/DB//u27jx5rtx/rufjzOeeByUsPciq4qbYkXzLXIB7+qhCfglEREDkVGxtRxLC1xdApPkU42sDUxOQS0v0nxiEAj2+fXNfomhCppmlwfWLytZ47i6oVi6QXIHXreFXa1r7KU79tU9AcIRMJYRNyJLz4VnGnWQmQFEQEh1wpIsSLW6QXYh80vCkoplmutjmnfZAIzgqui1Ab8JoN4XDSxSI6ybnYNat5bY8NWbsOj2weyowj98+MMAgIX5eVxyySV44hOf+B+Zcg+5kaYpXvHK38bLX/5yfH33Ip6x7wwAjIFenWjYmiKkk04yZusmg9wnkjFQDRDbrLmPSgZ5bzuAkxzFccUPM/4cJAdKwjxNEpr7Mp8fOOL4IQyOZfHM28DUBC1GOdsYjQbhopQk9C1MTQTWAaCLW6cTdK1lFS6C8rO1vmyqOpsDQJPnEBN0rzVk9jLhxTfLoiCLDMjTsAAvLALSAMR2c0gM7PYFMkRf0yYAOmA9ckWdykgZEABePwxNC0q6pgO7c0ByCcGOjeNEJxsSihgg65YJF06v29Pj3x0QLkCe5VLUVRo30gHUPAeML4by9/hCZscvMmPAt9cNDLyAFm/3xmx0Kx9fxGMmTjOI8LreEIjgwUfegcQ1N8gJBNsEDgaJKtC4BCO7Fm0zj9pmsC5F43IoEDuslUWSUqOdlfhNlwBIYFThwa0TTR8aaFeiRheJHhLrFDXyyeO1qlHaHoFs7iQ3io6B0QVUnkGVQ2zfsYDVq345wfGPb7wBv71lGpNWcQlOB31bnsCOGjKGZyZMFmWVGujVLeR9i4S1mK6hRDCds46YmWZvUwhihPVkTiBYHGdsiENHmsDOM0C2DnbXIFgMZTX0VCcAMABudoGkFwKM5Vxv5f789g4WaQZXlV6y45aX/d/HGkm9DtTRRi7tBybXNhizDoy0yA4gACz6d5m/sR8twMxxNL9tE55fJwDqIA9JkiC3EKBYFcBoAWed9WhceOGl9+l8+HnHmrXrcMutt6E11QM6+fhGwAT2ygedCOPpw3GE2eTHKY19DpjGu/78JUFKMNZ0lwKIZC7iQW4tgmtMBmjjz1UnlR2xl4SA6IaajpwlOUI1ImCMCfKg7kwCw0V67+UoMKcCsuXYWBdcHoTUiSuiK0JmVJrBceiVX1s7bWLS5dom/u38d+csgWvZLMXfidwORJuOIP9Cyt91UzILLdW2GnDkw41EeemOc44Asjy/ll2fVDUi2V684SkrNHcv0Qa3cdAMjDExA+RdFOUEFRQRJEJr1qz5N2bXf5+xfv16rJro46dD8Su2nj0WdjdtJ2NMsbDDaSf0QwWHiXCN8tr3yGbQe3LHLlPWwWlF86Fh0iwl2ZvqppTY2OtRAmlMuHXagB23frs/x0MXHMtJ1ZJSVjc0uGRt0j0pRbtdsZ5pdejiVJahhCqMTAu0CIiXqVg3Za2wCCdZuABJ2TTevQuLXXPjX1OFXbqANSmTgrQ8LlkmoGytb5pTrYSibtd1oKe7cMOCvAMnWn7RVAtD735hF0vfzOcZOS2x2bQwxlZBqCwtPJWlTn4fm6sjsLxCjwsE32Kx7xFALbqhldZugLc5gg6sfPCZiZwqgCDtiC4cY80lUjpd2cQm5cIsYuVMNs4MecY4hVM6ML7K8tdlUdsUI0dMkIbFcj2DXC/AQcNCw7ocGgm0s7BOI9EcHgON2ubRc7GW2PL3pSwa1YZGBQspZcIrg5RrYF0GreAlGQS26XcHDas0HAzyPnD3zgUcc+wRe3/XvwRjYmICO6saaSeBbRyU1tARUSVg2LE9mzhSKN70OWGARzV5csvvBdkVKtPArOtQ006q4UY1bOOoIXW+gFqMGj9SQ6C5n9H5IemURc1Neg7N9gWqtsQNrDtnofrt8c2wzMMkgZudpdCD4ZD0xbYO8om6hitGVGXqtOG9csvSW4WRVj6ShQnbxhs2KmePaB3J2iTvkhQ8aUKNh0mpvA+MM8JA0H56bWc9fm7xnEU5xGNPPAjnnXchtm/fjvXrH1ibwF6vh4MPWIfuBG8cfpYtlweuDhBJVNxg6N1qdCSXSMJmWm5b0Xxrkfiqj1LWg2EZtWvTuQlAOUv3YZrLat4MQ1PTYDJBVaa6ALIOFB9HsdRzxTJvgrhnoi6Dhr2uoZKoJ8Y6L0nzlTa5valpTex1/dxUec4EQ0QSRBsD0mY3vh8DygagvHLU9biUZGxt5cc1EXj3yaUEqFXG0qSl2eg5mzAf5bwSl4+cjlGzawCzrhOaFIWg6k0DziI3C5hpr8aLfv8N+OzHLsD0zAyOPfbYe58v/82Gcw7DUYGWnhwDwEBwpTAtA6V1kFOwTnjlUEbRpg8Yq0BrSTYUIAyExF3AJ/MCCC4sLeqjUt3WWK8AtI5wWApkv3Kr+E+P888/H+effz4aKTcqTUxgXJ50FmhPhJ+rAj40ImsDbQvky8TcZkk4qdMcyPhCFce2snMAsnak1WJNlWFwKSe6SbgznD105fHlkD1zASQ5ySy0hhoM4UTCwSyV6qQwIFmFagNqskfsgsSALi35hUN1EqhRDcshHq6ooXoZdfsWdWjcs45AMU9sVRILPQZMtQ6hB3HjhDh4AOybybtBubALmJbhwTZ/r7LwCxCOGWdfruadpwfY0cmqNc3ksgJTBh7o+oU/ZtOUAuyI5oVcLPk4eDZXWc8Yi6QBIHmDAFPrNJybQm4WUTV9aFUxi6yhTY2y6SLVQ9Q2pwsjCDQDCazTMKr2r6NV5Z+/cQlSPRwD6JrvJ6Ph92BUjcYlULBokKBccti2bTdOPW3VvZ4fv+hhjEbeT8biQ8WqSueGGATWFCJju6AOh+SkmqKYJ3Nq3uDNnStIfmT3jFhSUVMEdDS8PzkYeI8c1JSB6iZ0TkhDHwNuwHqpUrNjQABaIqq1gt25ROeWXDTm5oNWWapLsimUc0SkQaKZjzX7Ap6ThCpbaRZkD/G6VVcBHIo/cZJR053SQIXA+OqE5rlzzC5nBLzSPMSVG9L8A4hs5rh3oC6DjZnSeNrjj8Tatavx8Y9/HH/0R3/0gPrGXnXVFfit5zw89APEKYLC7K4csTWeAN+mCjp+MKvvHICKCBSTeiCM6ON5oOtSOEebXXoODa1qGFXROeiox8HaFIku/CZZNrFV0/ZrQaILJHpIsiiArgl1CZW1/XXJ1SVtbDJu8JMqpkSZa24cTDNu9DbheDKJ4xw3nrfyQNAk3LSseWOQpLS5kuH7K8BOOHwdlPAPz8jzOi1Ek4k2I+L/nrITSzWCDy6SHoqsDZePgMWF8eZsIJwz1o4RLXoyp781FdS6Ga48hsZHB4NUD7Fl/Rq84YN/g3e9+rewY8eOB3xD98s4lFLYsGE9vrJrDq8s11NSKId0SLCHNNTJmuiv0zFjDPiQpTj7wC1H6boyqgYw2jeK+kRHTt3VnYTkc73Mz1GVM9HXaYfznV4FAHDCCSfAGINzzjkH55xzzv3yXT3kwLF8WfPbr8DUhkfAx/RCWABD4JN3vCrJ4AyXEqXc5TTQnQoXECA0EjQVxuJ9i2HQTXmrsDw8pmEg3c/HdX5xCdRf7AhgKymZtgFs0FCLi7TILS6GXbQVv0wGob0elXoBYo47CVQ7h50b0KRmJwAANDk57c/ri1NaeFzlfEz0mAMFEECrBJ7wxVxxo4NzlnTGOuOdoR3zNQ7Po8YZYa0BHTVJ6hWgQL5LgDYseYv+jfWESUK3S0m03SdAoE3QEHNjnTDDAOBgYF0CCw3nDHIsoGy6BFKRQPMFz7rUN9TRV6gZKKcwmqQWpD2uUbscqRuicQkSFNCqQuNyWJegcS2kegTDYLiybdYiaxhV8Ndjw+shvNfaBfY5Hg4GFTNXF3z0myirBo961KP2ut8veszNzWHrXXfjmKOOQD7Thu6x1VTL+Gh01UposYx9xAGo6enAvFZlYFz5gulmF2A2TfqLq12svHTCy4tkwU4NXFF7q0MYBbtYQema3GUAz4LUdy+Tln/PkBb2yMHFLZZANwW469trioUBFplCxCp7nTFAzVjiHQsApXxeQ+tDGgH8eK5rE0Ay4BvpnDTq2TqAWnltB7imhA+1kWCcuqIuf5Z8BCkTgsSCG6i6nQwfePf/wPNe+le44IIL8MpXvvIBAcjz8/PYvXsWRx7K4KYY0X/xRhoYlwDoFWuHVOsZ5HltbfxYlrhpWSui56xdG40j0CxAFwBXjDLULvPnaaJoXlW2TVKohoBbgwQGvJlVFo1NUNscqR4S02waqCQPvudpi36uRnCDBXpP5XjYjAe/FrT2Ju1xN6BOm+UM/Jg8iicXSYXfRNSBMQe8RliZBGP6dyF1BAzH36FUNZKMvnBZx00Kl+RQo0Wg3Sc5Rc32b2kLaFfALDPIWtN5WJi9m2LLCipPKU2v26LNgEnoOQGgqZDpZYzUJPpZG0c94kQopXDhhRfebyDqwTae/8IX4R3veAdurSocubqPJI9kFHGDnu/7CKBYmjxVnhAoZobYNQ4q1yGAbDQeXib2s8Ikmx771FcN7BJVBVwnCftRkUdKU73MVYYml1122c9MubyvxkMOHPsxpjPlE7ipQlY7D6/FUwqqMwk3XAwXlrwLb6MkZS4BbrKTbidBJuH/ZgKITnXoApfdz0oWu7FB2+csXEa7JZV3gbwLNzEDLM9B7dlNAHlUkGbMWmIQxPtPtJHW0sIB+HhGzYC32TMil4tKA0ZBtzjMg3VBYiaPLJQ+9gKzXjstneBJ6LiW+wvbLcBVLrrizbryoupcKBvHF31vvwZaiIV1l78DEROSBgcKndDx86+ZomENoLUJGmaAY51wZXMslJvoYgWL2raJHYaBYVbYQaNxKRLWDluXoGgm4JyGUhY1a4EFUFe2zW8/AuROo0FgozOzzK8VbnPQXMa1qF3uHxePRJX+oqwUgfjz3v9xPPbkk3/pWJLdu3fjL975DnTyBM88cQuSyRZUP/PzUk2ws4kwB2lGGt0shVq1CpiY9GBR1SW8Np+HAkIVaHkZen4+6P0XFr0dFiRYJ3KYEebYjRqKuW0FtxbdSYJ1FLPV3mau4lRJreDkwtFOvBWjt34UJkxs3UYF+asDQN3A8fmlNJ8v4jdbDsM6IZ7FWo9rhgH4Br1yCInOHdMNs3ey0gmc3C6hIfK8vT6wtDherRkVBJwjj9qzn3EiZj94Dn7n1efjuOOOwyMf+cj/7JT4ucbu3bvx7ne/C/1eC2c8+qBxHaqsSStBcSylMJFfr8gnhDGNHyNWedyA6xtgwRUkt/elkoBxWEc8YObHalWhdrk/P53TsIrunyiqDFVo4/9n77/DbruqsmH8nnOVvfdTTklyQiCUJARCk5aEIiCdQGhKE+m8+ioYfVH5iQjyiorSVBAN0kKTjhQB6SBVQDABI50QMCFAyunPs/dea805vz/GHHOOOfda+3lOCj+/fM7rOtd59t6rr1nucY97jNGZmrxIoWIGUOkpdNFCg0Cp0iUw3yADyLSJwaWYMGEA3JeLvyxi/mLp9eS1rGvR67AuK0rLxvILeOmFHqXrWTsnCQbLfzgtJevhnYVDCTfaCW2mUXrB+5fVQil2NRrDdRsxY8UOD4AtleqmeWGNiBBmvYuKZGgoYJ3D8ccfj0c/7Q/w1pe+AA996EP/W+d+/1k1Lr0+XqlQTSoq5ZwF1zE7zFlCuPS4yuQVoUAY7+PTC6JofbEakwRSkjxNkyfbZxbSnnBQhUplm6X3snPQqy6i0fozaNdecFz4XJwMjLuWGONuDmweJGYYAGYbgQl2XM7X6/PUaBUoa7Jui8oDWeFiAii1TzUKOWbZ0nZcRYhzPXZzPwk1ESD6KF40IhXNJqX5QVnDFRVUPSGGeLQKN1qFWrksBifMZnDYG9xOajQindl4TDlatYaajKCKJmiAOHofrSU3BkB6IJm30HfkJA2QTLkD0KTM98DuPV6kyjK6mAOQrmNuVskIc5MFL/h9CRYjGB6m8wxQEZ+hXOx0Qfk4mXXTOgDjqdnZ01E0GrOaMLczs45SNx44A52toHVHi5kHuy3rCz2TyxrDzrNFxBRrFGIBLb1UonUTjNRBaGU9SK9iTmTASy8ABY3OM868+FqxACs9owXVwbt6Jzh08BBu8N9sATDG4Hl/+qfY3PdTfORpd8exNz8m/KbWJ8COdXKjrUyo36zspEh3XrzZyIH39NQT8dkHZ3FRBp8vWpmGxvNsA5hvUFEPLh0/m5O7lrMBbLaRRQaEJKkil6Bnk+1mFyO7tQ5VJIkV8fllWxsMUmzMaHGpihhAxWOJXd8yfaS1vpRu7WUXIgWi6Ygt5fKpMoMFx1LoArAdXEdjSVXjmHaMi8xwARrTxTkNiADZdjFrgtbkoSkryjbggeRvPPGeeOd7voDXv/71OPXUU1EU18yCdckll+CZf/AMjGvg/S9/AvbsWo2AWAIr6X0TsQOqrFOgLEkJBtH+e6tqGrPWG7CeIZaNx7hFGcCygknGqVad2G8S9mPkSfEDVZBl8PzBsgz+ztgSlZqi1E2MN+wakg/MNiLrv6JjHAbHaVR1Onfmz4efHT8HmaIyaIhtlKJ5cKJYqsPzLf/uc2tDpBmkmxLP3bTQmoC4K0eU6s0HKNK6uwKM59GY9EBfWUuGZNkQYN69O16/VsFb6EwD1VKf1YVFpacYl2uoC41H/NpT8OG3vB5vfetb8fu///vb7H3X3vb1r38dq3WJG+5eidV6ZStYZyyKgMnthOHlgExqQeyvBQCeV1sbUtAqn/84lI8GAmB2MwO1gqCtD1LU0gWPWp966ppq115wbL0kghcdGQxQVpERBqIbiBcM1qR1DQFkD/wcR3f7STUwEUrFpOiKJgDlJxflLJyZheOqyTodx7SAI8YatV/wjI90Bry+cA7ng/bUyk6olR1wugAOXU7b1BVUZygivhJsgG0jA2VtFMdrBbVaAZtdALlclY/OL8pj502LSdgXQAnfg9gE1zZQk5WYecJ67eJoEt0ifI2AsAbFhB2AeIFApDBIUjpOuJyejUFwT3AJL4CdI51fgQ6NFUBLRVbIoYDhlGoAOpumxepsZHdkYQ8OruNAOa26wC4DkUWyQsbBzQT9Yu2lFPHZ0/XQcS20JwfTd8OaZNYwAsCxxx2Hfeye/G/S3v/+9+OCC7+Pzz3rfrjDzY4NgTUcGKTW12MQxmQdqEZQo1UfyGYjyPEFB9RolfSYo1U4AJ2dAA4wqFAXh6E1GaBU6ramMbQyoXN1HS22DFZnMygAij0mQCypznnD+Ua4UtnMQI25S3qPy2YLzGhfdiGGMu08FmTWFWlw8iIA+PFlEUpOy3+8TRHHK51IjJ+QcswSYChqJLmwWbIlK+tx0JX1+5Zdej5jAO1ZQZ8d4y+f/2Sceqf/gw996EN40IMedPV2GJBB9YIXPB97jprgM+c8Ecfu2YEkjoGfofxfAOPkN5mOLX9eAKActG6gtA2aYqUadNYDZj/unCUGmGUTAEknaPwRSDZi3rAooGEAF+cF5+ow7ygRR+CcDhplOKBQGlpbaGehlIFG670LJVxZA02XznOc44rvm4Euy/hkKfohyVrIjlJEMsJ74wIwloVq+HmzkcFaaTbCkiA+DQ70U+wh5PRx1YgIKA6U53zgPpUXnI2ZXQAq0MNjidlsXSJotosYDLljVEIftQu3vONdcPkF3+rta/9fa1/818/jzBOPwdp6HeWVQGSPtSJgLHJLKxl/xB4JBsiG0wcWvshSF4othaJkQPBA6bWKMg21JhJxXn+ceJqtDaXAea76GYY5XIvBsTcxaGHVBLS4VG81Js2TLmJicnY1MhArR4EpVqW38mWeS5nfsRqnky9PSqxrZpkGlytm968vB+xsR2wAMznWxJREZQWYzle7KqHWjgKqMdzsEB1zdSfUlLXIDgpUgclpTexxZ2nB5lK7xqHw5apREKhSvpBCcDu31KkToMyLZ10REJdBE9ZrjX0uTnSGtJkT4caTOuKkmpyQlThL+wZDpkAIvOPnwo2zjnCFJn4/ShPA8oE1xo3Q2RFaN4nAN+h/SR7B7lCtOp+VYhQKdzBja1jaIACqcRWMq6B9BDscIvuLMmGDC88o8aI4txSxThrEEYB5ANLxeug81lWU1tZfP1XYsz4LBgFrpSwOHJzhvy68EHc6/fShUfEzbz/96U/x5jf9A8667ym4488dR31qTBHzajQmgLy6CoxXocYEjN1kZ+grykQJRetWMberMN0ITmnolp5voebB6NjsKBCx1A1QAiujvSjXjgIO76VgtK6BOroDphsx9d/Bw9DHzoitPXiIdM3GQW1WMJdvkvRDK2ClIiZrg8d1EbLAcHU+c8lhyigzKuAaQ/IkAJhNKWg2ZFwR8gDWIgNkYEoXJ48nBoZAKlGSn3neaTaDMRqDzmzQN6uyJoCsdAyS4jRlWgOTVRpX7NFij1lRkpeia3C7n7shnvzkB+DNb34z7nrXu2LXrl1Xa795z3vegwsv/AG++MZfI2CcVHjLmGpOr8aGsQSDxs8nGkikOMwc+xgE5Wws0AMGqw1KNASAvQTCuQKNkD8B8OO8JpAcvEFxHqADUqpHpSys5ZgUhO8AwBra1rgKhWphXInOjTAp9kOVPsWaPezvh48r2PBgRIn+wc+DGWG0qfyk6yKYBZBkRPG535Uuo9SQSQiuLuo10jzXQpGRWmJOGmsfdFy4KaD8czMtGWQFxYaga6GaaTTaugbQc0BTtg3FwYeckajrYgVEIw1OWs8LPUddbGC1PADryFt4wgkn4Kuf+ijatkVVpe/v/0ttPp/jggt/gKefeStoX1FR5taW0okQFM3A2NrM3tFJgQ+3fx7mQ7t35gPzCH+IxYz+LxS0iDNRnAu+rmm+CrURtDfOfZpLiQGu4XbtBcd+wXDtDEqNKVrSmmgZ15OoBV7Q9XlrtBqnuiogsi2BzbIwejU5ta58IFAZ2QEKzrBQBWlc1diGamdqvkFgvKxJV2a6GAxoO/qHEpjuI020B3+0uFY+wNAHFPoIdorq3wG3bz8F6rUGsD4AD6BsFRstpU8Z+VzOnCd23lHU6WhRixSkFLzI+zKoit29QOzUVZaPFEAICIobZ+9BTOyyEhVADLrMPgFEFx9/zwaGs1CaAGvn6sACcZQ4AHR2hLldh/YTuHEVrCoxKg4FqYXU+zqlw0LGC6mDRqH9+/aLI7PLShnBRhNDXWpabEs1D+wzMcNFzGjh9cmBsfLn0Qo+xzH93mHkJRlzWFfia+d+FbPZDKeddhr+O7S9e/fiL/78eTh6rcafPfZU0sFrBayskCF17HUCSwyAxqT/e27XI8tuEaQlAHygYnR7UyEWes6FZ/IVyH290R0DrVqMV1dRrXrJ0uwQsDqP42x1nQoxHN6gKpkA1GwOVR0i/bE3JGEpQJKr74Uc4dbGCpOFIuZkRvnH0VoyFAtFY7A1orCOL/wwHkUphbVw0ykUV4XkrBcAjbn5LKacrMcpi8pBgIUHOH4hcZo8O4rHjbNQ1TgG6LH2GIgZMAAau5zhxXRREuaN+xc874n4p3/6PM4++2w861nPutqC8z772c/iH/7hjXjmE38ed7j1DaLchDW1fL8cWMgkh/ccBZaT5xo5vwMIQWVATNuIFDA5UKYa8hYZz96WfiwWyXaUbcZCGrgAgveIDWK/Q/pZfMdButoH9VpbwIHWgZHTGOlOxGzwesSZKVZTplh66eTfXUNgo5mJObPIPG5FIHeUivO4qyZeElJDa/J02a5CY1eDQc8xHDO3EwoWhZ6jQAelJiE4WZU65HB3ugxyPKVUIIFoLZx62UYZs3RwH9/06+PqOtA1cLqgLB+mgysmGOtD6IoJ1gFUegce8djH4s1nvwQf+tCH8JCHPGTrTnglmnMO+/btw+HDh7G5uYmu63DKKaf8twLj0yk9t7X10aJcwqerDGldGRgDQEsBduGzoYB7t2nhAAqA5sxXM8Iaobz5WMdKu74RBiljIDYD8LKIgXgy1onnqfZ/wPFVb/zOu4YWQWfJVdvOopuRg7f8/66bh/rxAAgYS1bYR0u6chR0pJ0bwZkURHFaLoDc4+yi5wwEgGcSfRogXbYoq4aAT1Wg1oehnbeS2hm5kDnYpp0RM2QMTYhh4q9iIA6n7bl8L9TOHXAcrHF4I5bJhQfIbVx4VQnvuqMS0716JGZtQmUqTUCHATO7x1lfLIMXy1Fk371ezXUNQsU677al+ymJcecW9ouBIUm0tY5MjUWFzo3QdTXmZod/9mX4f6OlwI5KT0nH60oY6wMWnQpR5nI/+o1crlQIhN5/XWwE9tj6wL3OjfyxCy/bqKFVh1LPk0XQ2BGgWTpRejCsg2sXDiHQjrq0DgCcF28Agem+7vVvBADYt28fTjzxxMV39zNs3/72t/GXL34RtJniw3/+QOw4dmfUEa6vAcdeh7wg2nsAfJCmUzrkfwbIsNGwlNVD5I5VMNCamHdemDs3ClUIAaDQc2iQsdLaCazy6fMmExTjKZRpfQ7ZKdx4HVjbgNokkIzVVbiVCdTmFGp9Brd/E+qoMeUNPzAnl+C48NHapCvmwFZmlp11PnVRS0wyFyGZewOWFwu/6DtfbRNlQQZnU0VWOQS1agI2FQegIgJAZv8YBJU14BTZmzLHuB97BHzGpCXu5nE8tfN4HAmsmimwujuMvz17duFVr34mHv6wP8RXvvIVnH41eCw++MEP4hWveAV++b43x5895R7EFnbzmIJMZlzgubqM8QUhQ00IvouxCACBMQa4zpFHkfuV8wYqEIFta2nObt1ESC7sIsAFzfVKWZSYw/p+6pxG4wrAB+pSFiuXZrxwDlopmrOsA1Cj1Crol62jazZ6hGo0RVUfSI0YKRdxNrKvgCceqICJk6nUJNNejRLDiebuOsbfeNKBA4w7V8OYkc/QI8aqz+VufSpKrTp0hn4v1RxzrKPSBM4KNae4DmfomkYUw6K6msaj9RLHdu69Fl1cc0SmJPIMaC9TpMJT2kyhSotJsRfWaUyUxa1vcXM86FGPwbve/W48+MEPvsqGnHMOSinMZjN89atfxbnnnovzvnoefvLjnyTbHXvssTjzzDOxZ88enHbaaVhZWblK572q7Stf+QoA4C4nHE1AGIig1sRcxYEU8+XJUSi/LfU1ribqZgbWV9wFELFFpYFRgWKlCik5WWvMuY2hqfopZQEaUezJ6qrAEHWMqahG1B/nU/ys2rUXHOeaMyAwJrJ6lBOR72p1d2SGvdvJFRHYsivedT5npdebcZBW1JmWAUAVqqWF2evPLC/cPi1YCOjy7jV20RdqDq0sinIOVVmM1jaI9cQGMV/tnNzEsw0BRH0054jydeLYEjh4IOYLtBZaz31+WEXSCQ4eYk1XqGlfxMVI6iM7k5bT5kVL5j4uR2FCpZujXJxOSFGcz/ChAsCNbE7UNtmU+WGAzNkulA6LHWtvrS/p3LkRGrMaJBRAZGx4cZvbNZRqDq07wMIDXmCjHWFUjCKI9e+18OWh2RBKXKhhLiGgbGydvGvqOxQoAhelE4YXFB8Vz3mOueAASTU4+j2yT5xFg6PhHTSuc4OboKpr/OhHP/r/S4lfbl/72tfwf//vc3D7mx+Pf3zhr+AGR/vAJC4lvuso8n6MOLn7KFQidFaHewtp86BR6FhZEEAAvZxpRKsOIxwKLnBuDHzYGHUgo8OqGqoqKTConkD5cr6uGlOO8XZOnh0fvKeua+Hmc2D/ARTjAmbvLLnnEFTSAmq9JqalpWA/cET3CLCHTGBn7GZHrAmAJBd6x6zonIoPcSU+OR7ns2Dc08EJ6AZ2lwt9eC18GG+Fd4vrKki3lA+ocrIUNbswuUgIy8QOXAbsPg7ONlBdg3vc7RQAwGWXXXaV+oy1Fv/wD/+Af/zHf8T/eeSp+KvfvHsktbxenOIXxjTfMVMMRFAMREJDpmITkjdlGl8BroTTWkiZvCuZg+I8uC005y+2gE/JGOIRHJLUbjKgTsFCo/OGsYIVkUTkyVYAHEoPQqxzHhh7ebp10EpDqxVUeoJOj1DquQedu1CXG2Qg+nOFYEGOQSgsSuWBBMsD60nIXe18NokgRWMtMWf2UTSOXLGKzhLRwJ43BsVk1Mf7d06H7B68JgaAr8tgpNL61qJ2G0mOaF1oaNY4c1+sxtQvTetjcgzVGuAMUdw4KG8GoCigmk2M6jmKqgvXcsaZD8D73/YmHDhw4ErLgH7yk5/g3e9+N84971wcPHAQXdehbVucdNL18Yu/dA/8wj3ugOscuxtrO9Yxm87xspe8Hm95y5vRNC3W19dx5pln4hGPeATG4/GW57q6W9M0eM+7/hH3P+U6OH7nJIJWIOh/7ZyCjh00VGOBVY5vcJFIA+Aan5XCpAYiV9rVKxWStHD+GCgUlI8w1es16Yx37UzjmMaSWBPrfzWi9/8zatdecMxzUVFREEA1IolFNY4LitbBFYPxGlw5gvMBcQ4FDSoDtHYc8tcy0GIWUVY/M7YOgIm/Y/BinAJQQCtFk4SbBMaAmwJNGgyqGExzFLSGQaEbrFR7UdZzjHYcQtHsJylGMyUrm9PnMKuyYyctBoc3qJSoz5EMTcm+g46PF10fxKfX6pRtAnzatiLmoF0RwXeHN7wWrI4LNTetfQ7OyPQqkXGADq4jk8EssgTMAGSlKuuokEbbTRJXJ78T4xcJ7XMI83fGqvDMO+vQYIWYHGhY51BpB5JeKziMvJHjrWFVQDsRaAPAuDosEBSsM/cMpo1staL3BxtzoWrVBglAvOZRyJ9M4DmCetI18mKgQ8U+ZpYsCuzdfwCm61DLgis/4+acw1ve/Cacdusb4XP/+NsoyyIyn2XtgzNHQcfPE5+CARS7ZaMsQruYVitUHPTeGS2YO37+tT6cur3Dsy2T7+FZZuNG9G5KoK4PQ3sNJADAkAvazQ4BmwegZnNilDc2UOyaUraYjVnQ9LtDjZdVmLgIaCpS4jZbqJYyCriZCdo+NyMQrVf8GDx4KFQ0cx7gqokfK6xZ5hR1QW9ZAbYJrKEarRIYDmkQTWROOUgPIEBkbfhd1SvkzZlvxPGrC+/O5AIhGji8F5iswx26HG8851MAgBvd6EZXus9ceOGFeM2rX4Xz//PreNFv3BW/98jbQ0/GERBLYMyMp7ME8LWQiyivy+asCUUJKAdo6mfslTCOqliykSmbnEeUT+8I+LkZFiFtHuDnDKDU80CUGFdBOx94BAOtp9CuC8RKZxHAslYqAOI8kxYANMai1HSO1hJIbtSqT/1YB2DMHpKF/bFKkjEYOF2gKFqU4ymUpYJQ/Ew6AWR5XnWuQOfq6CG1o7CWJVIzP2/Sc1D+mUZDgNc7nqMK1VIaTFWiMasoNWXkKNUchZqjUB1VamPWWJdQJf3tTBdjhbiqLfdfZsO5dS3Q7kO5ChTlHJ2rsf+yH6Msy5DK7Eja+eefj1e+8pX44Q9/iKOP3oWH/OI9ccrNTkRVjXCf+/08TrrJiQteBec0XvsWkrhd9F+X4OlPex7e/va346c//Sme/vSnh2MbY/Ca17wGJ5xwAs4444wjvrbttEsuuQR//rw/xU9+8hO85TfvRl4tH4jnZCEw4/zcYoGx9yw37M1VUYbhvWBofKVd/7terwMrnKaHtcl3xZ6Jjz3xZaI59asn8TCqgq49zEHA/2iOr54mcn7oggaW0pSBYrxKbiMdNaquqENeW9IwppXRWDMcf4+udAABwHKTLjcK1uLtnJ8g+fpc0KhrRYAs3gFFMDP73NgCMDXmZh2TYj82umMwKfdidcflUO0U7tAVtCO7i4uS9GRrq1HbOJt59wkAraL+MbC+RQTLQFwQZc7iIK3wYJbXF44uDgGLiLlpWRvIaX/owSQLd7AQZSS1+M6i9JNOQcDYRQ1czmRwk7IIACg1aXY5J/lGa1BphVLT5N751zbrLLRS0KoCQKxOXRQ+/TOB9M4S80MLnc9Q4mUSBpUoCjBP+oTsG8wgt3YSg/78AlSqeQDiDJ5DSjfPhHVuRP3Qlfjcv3wC1tprjDVmV+JQM8bgFa94Bb7xzW/hn9/2dFTHHE8gjdMfcpq9skJIBZUdLywsKMiA0PMQ8CNb7tpWwjvEUh6ndMgXHfTaiVaZgvn4uW90xxCrVfggoqpFpTagJuvA2lE0T1zxI6ijjiZgeugQ5ZjtDOkzvYHoDm1SoEtDJa0p8MXPDayZK7z7clQARUHlrn0uUczmMcXb4Q04zvk6baB4LJYlAdbpFFjfkXhXXDv3OtwyAn0OfOWCDoD3OHlWlYs8GEWBkQCBZJ/OEmqDtuFAXLMPGK/ilx90Ozz92W/ExRdfjFve8pYLfcJaiwMHDmA6nWI+n+P6178+Lr/8cuzfvx9f+cpX8LWvnodvf+e7OPn6R+HDf/lw3Oe0G4WiQgBiYA6XhZdzhEwfGVKO+f7k5xurxwHwyvzmcztKAlwrNfXGFgHOzs/DlZ4GKQH1GZ8rXRjknStCoR/6HNeHwIz6ktNKVehsgc4CjTEoNRvLETTT8en/ubEBSBOuKFEXu1FpRxX21ByFnytymQelnfPHh0Xnau/FjN+F95TEN5AUsPMeT/6erqsMZI8kdmjudJh1xs+byt8XXbvzhbE6W6BVBMaZSKjcFK2iOAytWpS2QV1sUMCWaQBXU0EU05F3Z3Y4ynxkirrpIZL9wINn48elLnD+eefija9+NVZWVlCWw7DHGIPLLrsMSikce+yx6LoO0+kUr33tOVhbr/C6N/4FHviQe2F1dSUYRADQmNTI4kBEuuca173+CTjnzX+PJ/7yU/CJj3wSd7rTnXCXu9wFXdfh7//+7/HRj34Uxx9//DUGjl/3utcC80P40nPuh1usT0Ied1nJTlUFHI+JqoigODwcF9ng1dIb9obe0ypVC1XrKTHjZh0UNLH5WgF1QUWexiOSkNVVjLlYmURswAQfa40Bwgr/I6u4GptpYxBX6V2JrEXTBTqswqCE7SrPFIw9+ztKJkAgBlrKZO0aBtYzXq2dBEaRoo0ju6wVMQHxWNwp1cLECER3VGsn3jJ33tUGNMahMTtQaoXGrGKzPQZr1aUY7d6BwhwGZodpYTOt1/Nq4KhdUJfvhavrIMdWDIB5IQYQRPF8wzIIBqCOXNVRS1wUscOOR7RP0A1Tpg1VCVeoj4bmCHF/IfH4cpFXOkzW/Cw4128o4+rZfAbGLGfQMD6bhIpsvVXorEapFepCYdZZVFpRNhvj9Zz+3Uw76zPaKLRWvpcCnUgDNeucZ328i9SOknMmAXzQ0JqYnFI3QgfNcooIqAPAhoZWrLO1YREOGS28DMdah09+8J9xwxvdCHv27MHV3Q4ePIjf+PVfxwMf9CDc7373w9FHH72Q3/btb387Pvaxj+KcV/0eznzkAwBrqK9JJg8Inhsu1R1ArgO0TuVGvJ12Iv+qaQloey1l2n+8wWUNlC6gdAnnKw4G13ko2U3Grg5BfFSCm6UXrZugw4hY6hFJndz1j0VhNyhX+s4O7vBen1e5A44mPaSazamAyWwGfXjDA99xcEGGQjuaciQrgAKGW+uDXixltmDJUuMLNGgNt7FBnp9dnLPbLxjsMeIAKgaO7EIXzTkbi6YAfkGq47PkFHqTHTSPFBXFN3BRkrnP7NG0OG6kYK3Dy1/+ctzlLnfB2toaptMpvv3tb+Mb3/gG/uVfPoGf/OTScO5Caxgxfh748zfGnz7mgXjw7Y7HaG0SKVTORgAgBObwtQp9dZAHlKPUa6YLOF36cs0R4BkfVAcgeGg0OsxdhVpveAMqFvZoHQE5noM6S/2B9bcsp3DQQU8LwBcJKtHY1WBEa9URIMSKvy2a8+k3FRhYujYClcavCZ11sH6eaa1DpRVImzxCqRVKbTDSh0NsA6WOK0LcgvWpLLVqUXnGmeUR+Xwq51oCwixZE9fiXCB46Jrpb2K6HebGBpKRGHKFzhLhUBf+WMWIpCeG5IcsvTCamN5abwBYRVVMoeE1yPWEjNWuBUxDAeqmDWuQO3Bp9E5xajjT4PXnvAvf/fZ3eqvkWWvxn//5n/jbv30ZLr30MljfP1dWVjCdTuGcQ11X+OBHXoU73/VUGFeitVWIBWEjIpdzFaqFTPU5GlV407vehKc88Sn467/+axx//PF44QtfiIsvvhgAcPzxxy9c29XRvv71r+OLX/wSXv20u+NWJx0Nu38OeGM9SCV8eWcAyAt9hObzvYc8xQCKPSsx7dtqFnjIlXeTz0Uwdl1RoEGBMY9za6O+2JeMVj75geN4q1Hmcb4G27UXHHMu4vEOmjD5gU92wiAG7oTSnq6Ec0XIBMDR70Gn6rfnUr8MghmIcQYCSi3VovMgqdQGxi6ybTpjzDobmWStlJ+gSJfGrS5UMnnSJFWjscQIjIpDqIsNjCYHMRpdDkwPkWvWl0XFjnUS1V9xhY8AnsYFGIjub5lrUOu4YJW+Yxuf+D0EAjF74/MaMyBnPSNnrdAFaT0l8CnqwAxG3R+51OEAWbLVIFad44nc2DpM3vxcCHcUnm2xcFD+d5rUN/yEMCnTfTprMCoi69GYVAdonfUTvQ0LFLtG60KjMfId0nfw+XeLLEgzDfQb+TLSqfuR3ZcAUGIeGFXqP1SWttJTzO0arrjsUnz0n96Dxz3usQt97epoBw4cwOGNDbz97W/H29/+dhx77B7c6173hjEGN7zhDfHpT38aX/nKV/B///iJeOKvPRzAHCg0ld2tKEtHgXkAZBYx+wSzalDAAhtsu8WL4XRTCgjpoZyNwVYiVaBMzyWNWgbj1D98jmkFFK6DQQl4YFCozoMoG+aEFhOUq+sEmld2AvPNyGb5jDHqqF0UUb85hT5mCuzdR+yxcVCrXnoBcmm61oY8oQB83lFLMg5Zipor7DUtyZjqKqSAg1ZA6cdrNYoBsmUVA1t9YR1nvASja8GpIsklrX0WnzYYNKr0AVJ83NmGf4KzkEnjdx57J7z0zV/E4x//OJx55gPx2c98Gvv2H8DqpMYj73UK7n/HO2Ln6ghrkwrnX3AZjt4xQekMbn39nThxR02u1rGXSIzG3ovlokwLoPupijj3mBawRHgoXdKcYy2gbJSbaMo20doqzB0hbkTM653PttDaidf0tkFqUOsNGEcFORq3CqUsGktueQK/tZ8bHLQag+KOnJc+jLDZGsy9UaSVwmq1gnHRQCsKvuP5hYgOBsqA9VIuApgq0SRTYaK436jQsK6AdbsAENtd6WlSjp7TVBIrTPOllEnwPMpB5p0gBPxICn/JNLedtYF8lOROul0kdqKnTWHadZiUGnUR11wNg86NPLlEkqfWTQKzr2ChdRtqA+jxOgFg76Fy7Zz+7uaUncqzyqfd8li8oSrx2te+FrPZDHe7291QVRW+/e1v49WvfhUuueTHuOvP3wJ/8kePxA1PuC5ap3D++Rdh99HHYm3HLtzk5jfHTW92CjY7bzgFVngUjAgauouZKbg8eKFaVGWHX37CY/FP73ofnv/856Np5njYo34R737He/Fv//Zv+MQnPoF73/veC8e4Ku0tb34TTr/1DfDEM24B/Hg/ZcypdAyuG5dwsw56VBKjXMSgPDvvoNdrYpFZSuH1x6wZpjzFRQykkxVx+X8OomRDt67xqy/+GN7wz+fjb5/zUJz15LuDi7EFSSW8IV9PyNBxFqo7cLU+m2VNOfezrDnys2sH930dO4+6FQ789O3YcfRuQOkQZJADLgBJMBS3EBSFNPBCBt7JXLZygDCj0NoYcMEtqcosGOPUrYawn1YqTJrc8m1LrYj58LrLcXEIK/pSYPMAsT+H98bKVwcPw003Y5oolkwcPBQlFlz6llNbVTUtznUVpRN008DmYfqb81Cu7aRFFiAdo68iyAF0/Kz4+bJ8hbMN0D2lOX/5nbH+jbXeDCalzotZDn5GZETEd9DayMSzO9ATeYHV4KAYZu3DMy5SjZ1W8IuTC4G9zI7w+2X2gHR1kU2Qaf74fuheFQrFi988yCsqX2EvZGewFKQzMzvwV3/+ArziJX+J17/udVdKU7ed9r73vQ+vfvWrAQB3ufPN8dWvXYjVtQkuvXQfTjnlBDzzj/43HvmoB0DpErXeSLJHcNBNIjcSBic/D26ShZHbcL8wrhTMr13w8gRALPoEnTOVtDAbDyDRMDOb37k6SDwAoFLTwLYVoPdYFxuUx3VjP2l2WdbgLDHMzQzYu5/Y5M1NoDNwh+fEErMmD0BIts/Pp/LjkMdcWUJNJqTfV5rG43hE43g8ikF7O48iho11/SZWm3QMinURU1XK/O2ycRAUN69jdu2M7qtrQvaAb3/rR3jR6z+PD3z+e7j3qTfEs59wR5xy3DoKDqqRxUyAOFcEqVYZg4K1pvmGP0tJl6zQxrl3gXgvLL3wkjmrvJTAEUA2doTGrgY5Dc/lgdjQTeir3H8rPU3mKGNrdN6g3TdrgzHN88ukLDAuNQ43HeYeOTbGBiP6OqvR9cysM89FfBw5Z0lwPetsYGD5O55nRgV5wSalxri0Qc7Fsg4Jlo0wFBgYyzHB18RAnOdHCYh5fuT7k9uUYrGKMR7WH5OueaUqwm8kbaO/aS1roFWXAEseqzxPlKpJ0jcWek4yDIC0yQDcbCP0v70H53jOn78drzjnY5Cw5173uDV+9cn3x8MeckeM19eDxJKkbiUauya8uGWYUyQQzsmZ1JhRmJRevqOnMO0Mf/tXf4eXv/Tvcb8HPgDveuvbw3Hucpe74JnPfCaurvajH/0IT33qU/GaZ5+JJ933ZnA/vRzuUAOzbwY0Fmq1jMF2hSIQXGtihn06NwA+zZsOumS9XkOt1wSM1ydRFgHEvOz0QOAfgs880wVZ2Avedi6e/TcfBQDc/MbH4ZV//nDc9U43iXKKeoXSfHJKxtEqDuw/hF3HPBwHDhzAjh07rrbn1NeuveB4//exc/eNcWDve7DDuyANKNLYSqYRMWIZ8HpSxwFPozAQpes+ic71E02uPwaidS/dZfxZTh7yN55ceNKR28VtVNhWuuOkDm1S7kWtNzCyl8MdvIys64P7aafZnLSRBw5GhqmuiOmSlYlEnsEAjsuCrHZeoKwvqMBJ2lcmBIx9NLmqV+iz1kA5inlFMwubwQ49D5F9AlFawZO5ZH8YHMsSywyAWFcsn7F1LoDjqCuOwS8MlnlRkAwKg2eWx4y9/ESyIQBN9HURtYSljgE67HqVBUYY5JNEJE6odKxp2IelNpwuiZ/BtKlwp5vdBKfe7na9bsOrs51//vl41rOehQ+8/y9w7wfcH9YqXPKT/dhz7B4ozVk1Yoo1CYhzgJx7ZbjJz0Fm4jOR2GxBsmIcSkCcNynP6TunVm2ofMZZANgIy4P5GChLAF/rDdTFBuUsb6ZRA2s7cvUeOADMfFDRwUP095TTbbkAlGXeUTXxIEqkMQvBeVrHLDQMHJuGgPKu3cBoNS4s1hCQ9LnTOVhPcV5p6f0pMubLiDzLPkWc6+akA2TGrmloTuGUdOypalr6TYvj59XtWP/JXirPPIWKn+MR3afcvhpFaZyXywVZhbW0uPqsC3O3E3O7I7w3Y0eYm3UYR/l5a70RSA4ZDM3FgThIjOcn4z1XxtaYmRqNsZh2FqNCBSA8KhRKrTHtTJh3Nr3LutQKR08qb0AbQajE986G8bRL5yvABxEbnifpM4FMBBJlVOgAxnjMsedK5mGXTDH1+wiK5ZrFc2FrHQqFcJ88V/L2ffOlXKt4XZPgma8dQPDEscxkXDQhyJlz0TPg53thKYzM2kH3YkiK0Xkm2VnSIAP41rcvwgUXXop557C2cw33PeOOod8bS0ZP6yYhliU8p0DKxOcj75HeSUrEyPl8XGqU2oR7cM7hP756Pp7x27+HO9z5NJzzciIeTj3tNDzt//wf7N69G1e1PeMZv4928wr8+2sfj7VSAQcPwR2coruYcr6rlYrST4ocxYFVFuA4ZrWg4mB654jA8eqYgPF4FMeslGGK+CXXNuF3NRphrkv8/GNeifO+cTEmkzHucvsb4qNv+HWSbbKhO1n38xjJK/Yf2MDuYx72MwHH1zpZxdlnn42zzz4bxsRB1LlJwhbTd2nELZCySjwgNGIqGN4uyC5UrHbEAUORaYyDhCeCubGJK54bTzoEdOm7yEYgHJe3ZSvbOpJaMMBzoBy90MC0OwqNXoMpRlhZacgpttMHLZQU5at27qD0VBxkw0yNB8tKaXJrcN5kBssMjLmMKQNsHhRK04Q0XqVO7iygKIjN2miY8DOXbID1gZDM2LGxwmBZ6nIlMKZnQ5KHPAsIMy952mbp7mNGpLMI7Ia0/pn1AeLCFBesONkzMGb3qgSGIXcyCtSegWQvA7sy47W7EDzYWXIzxn5qUPgCI4Vq8blPfAKX/fSn11gwh2xHHXUUyrLEHzzrjfj43R6M8WQFu/bsQQeLwgmNHRaBqnRnh/+tDvfP9xYCm4S+E4hGp0ytByAsnKn0KUp0cqDNx8kjywsfNc+/VWrqgUUHCxtZa280y9RZU0OMZFk00CuUSaNQc+huA6qewO3YAKaHoNo5sHoA7sABKvQBUKq4zsBN/bllR619gZ4xyQ2c6eIi1BnP0vhJYmUN0JvEVIdSvgisMR3PM8oc5NI1wGiFwAHnGU+K7FSAtjFyvKyhWmJwHedVHzWAPgAu1qAYJM84kEYAYna9JmyBcMFaTfdb+MBfriSoXYyFkFpzXcSgPF0C4CJNI0zNUaEAUKWnMB7cMACu9QZKPYfCFI1dxdyuBUO0tRPKS44CSjUoVRMIkkK1MKihFTD2soDGWKzX9H9rHYyXYDGQZWO5sxb7Zp3wOtGY3zECDjcU3zDrbJjXiQXWONR4A9uvE5K13WxNmH+0AjaVgp54mQxGYX2gvs8Sjsjidl661FrnvTCpR1I2BsYSpNO2DhIQSnBtXJSg8bzKxATNqyrI0iQL3tnKkwtj1LpBoRsYRFmeVp2Xo9GzISJhBucokL1xa1DawNXRQ6lVixNvdyPc8LYjFGqOxq5iw1mYhoLwpX64savhWskzaNFZk7wDDkCU71iuP7y2aOWCETUuV4IG/da3uy0+8JnPo1SbuNd974tvfv2beOXLzsYLXvACvOAFL7hK+Zh/9KMf4Zvf/Bb+8VX/G2srVHnOrXSwPzoAvV5T+jafPYINSy5wpIoiAGRmjNXIB935iqABGK9MKD8xG7pMrLHM0s9zioP9x5QVZ1TW+OfX/Rp+/uF/ix9cfAU+/vnv4AWv/Bc89mF3wA1ueGycg5op4IOElZfMnX766SiKAmedddY1Rghde5njA9/Czl03x979H8X6+noAxxx1LNlLZuJkflW2rIEon+Acjby4NsYmg0BaztJiJH2VCe731sbJh1tOELNVKieV6OJKj837xwlSoVScE7PDank5Jt1FFDzki4pgPgMOHgaXfk7SRJUFLcCBrSlCcQLUIgG/Lsh9xQstuzyNAUYTsviY5aknsKoOwEcaIgBCdhAjqlBFiUQKjq0rRaAiPevG2CBrMC59/pLt4EbvymKzNWJSi78zG8ygubUOo0IHo4QlFrwtv6tS02LGbE3OYnJUOYNIdtHOzRizLi6k7AXgNG4c8MOpmfg5XvzD/8IjHvwE2K7Fy/7mb67SZLrd9slPfhIveclL8JEv/htudstbgfNzy2AgZqz4/UnDNAeW7Nq1gp3lFtMlujBGYp9P9cgcB8CNWTL+W25js+sJwEe41ekcLUrdJGyx9DzF8uJVCLTMDYCVYm/IT+6aKU3200PEIEu2dXNKTCuDZsGkBqaYvTgMMFlyMWZdP7OnfowWFUkOgDg2mUXmiH6OCZCssagTa1UNjY6CaDmrhS/FTRt4qcXsMDDb9CwysdiBRbYuYZHCguxTRyaBwJryogfZBf/j+2Q5hayKV45iwKfSMKOjfIafVQqGKg5hs9sNluDwe+JCTCy3k++W+zGzxxaUCo5zqANI8oxLfTH3034QSX8n87VWyRrCMoW6UFipimB087EONSYY75GRZUaXrn/HqAxALpIvtP4wEOV1hq4tXqP8n1updUIkAEjOz2N0blwgheS2fPxxqcO6RrIRh1ER51Papgieukmpw7MqlAsMf5hLfVCyHM8sIclTfMoAQ2BRI0wMMT3/uTcg+Jnn8jxek/kec6mLfLcSMJee3Fqvy+BZlH3xg+/7ZzzlMb+Mv/u7v7tKKRI/8pGP4OUvfzkO/vStWNn4MWW+uexy2IsvgzvUwM06oCoorZrIY2wP+ZSZnGHHa43VSkWBd+MSam3kU7GtEjBmfAAQ+OVKtcFwFQa3DKAG8F8XXoIT7vCHyXeXnvs8HHOdo4HxOmUX856iA4em2HX0/zDHV61x0ncv8OfcqCHIRlGVns5OYGyZgDHWFynhWoUHI60hVk+6VaQ2FVgoFoOpX+zmxoqgrQjAAJ6kouaMgS9Jfxw4GAyAj1SOEyJ9r5KAsFKTjmxcHMSmOQqqshiva7iNfcT2jH2lnr37oaqaqugpDcfJuOXCxOnbqhqhNC1ATJMxtLDyggxQ1S6lkRTtAD+TeQKUWjsJWkBmkNloKTS9n85OwjuUoEZOyjwhSy2wdSyXUAvvSz5zyQ5HN1kqXSn8fvGdLb671jqUmgBTXWx4tirNg2xB1bKYDZ7bNXS2EOwEUGpHblavKWadm/EZLcbFQczMBLXewF8+/yX4/ne/g6c97Wk/E2C8ubmJd7zznbjpLW6J3Tc8GRut84tmAa1qlD61HD27biFYRRZWYVey9KJI3WJ0xZqFscX78fsYFRqtLbBaxZSJckGVjdhCG6VVtozA3NawKvZXiuonwC+zETDgD+5qb7AYQ0FEyue6tT4X9bgoUa+UMOPjCCg3U7jyIgKTANTmFFillIvB/dgZApnsmbEOqiqFTEHoeDsOks3SIjoL12zSZ+OzOsw3/GJTEyvsg+86vSMtHAHA6nEkBBxg1YQMhfE6VOe11e0cShdwXJSkngOHDwHjMWXdaHwwqtQcMwvupSNBRy1bXoCI71OXgGs9M+6g1o6Ca6bEiBfk0SrsBqwqwZUUrSuxXl0aDG32SOQeDvZY8Tul/tP5d6qhtYV2bUhDVtgYODsux0FqwFII6XaXxjxLIqg/KjRGzvtxTaGUb8SurtVl6N9MsPC8QXa1CuOmMRb7Z+0C81sXGrPOYq0uAliVgJyvUwI9+h+YdV249pBxwu9vxL0FVrW1tHxohVljoTVlNpk1BnWlg3yi1CRJmRsTQCWx5xqltuhslI2s1wVaSxUEZ7YIAJuzA8l3yYW26L1GPb00KuR6QM+0S2QrjbFhe77X2OJ982d5jjj/qAD0oweS7nG9LjAuC/9cLQCNO939XijKEh/96Efxa7/2a4Pzel9qzbZt8b3vfQ/z+Rwf+tCHsHPXOlaOuQ55iff/BGo8gm4auOoQZa2wDm6jpQC7XSMaj8bCHm7iQQtFwHhcxjLQdUXjmwNotfIe50k0ymWWMCAGAIeHROP3Rjc9ES/440fjmX/ytvDT37zqE/jdp9wH5//gv/BPH/9PPPiBd8a97nNqYrRf0+3aC47FpKdgoBQxVaVqFuQRAFuMo/C38dkSWqsQsxS4YCHygGxMBMVpyi8CtnNhddKxpespyiPkICVQ4BJ3PZAy1SKbCuSglJMroOCwCytqL2ZmJ1RpMFrXFHluOgK6nrlSIHZJJYBYZKzgPKNS6wcg5Iq2MROBzFYBzYF7xJYaxFyg9D+xyeRCp+5Yagq0mHW7AktfqjkMKhhbYaN1sC66Avn58qOSGrhSK7+AxOdbF9GQSAEusR6caUK6L3N2hc8T0zBFRqDSm5gU+6FViwZrgK2hdEztR29MY2p2efemDYspMyosyQjpAD2DXKq5qD7VYnV1ghve6Ea4z33ug2u6XXTRRXj961+PvXv34UVveS9aaByYd+G+yR2qAXAwYo2N1ogFxYCqflEWEe6/csEptcr6dtrXC5WOM953oyV9J7PvvBjxM00N1iqwYHL80aGKALgZVHNfnRT7Ueoo1+JIfyDVcrYupnQ0zpcydyNUljIhlGqEom5RXW8VOHS5L7pxKBxXtV47PJtR8F7bhCCXkIaNx2R4EAaU2aOKzCoD5aJEKJQRCoN0IWgJ1QgwDUq9H3N9DOVo1RYF5sQY+xzbsnW2BtQqNCxUtQpUIGA9OkQFG6pRDOhzNmbcCCDXUEo6/081IC+WIyM95D7lxkyzFhKR+ZT28cGPzhqolR3EylcjVPYSVKG61hhoOYuFl27UEzidZrEoEUFB8ChCw/oy5rGSKaVEK1SLwjUwtqb0urpCYwoANOcYB4xLXjvo78ONCdKGCGwZiMqxoNFZ6+eIdO5hgqSqi8AEc8BbzuYa51B4ENpZMjQPN5HRBZB4M+nYDHJ5bqJXUPj1h/XUcdykbHLTMhj2OeO1osKOrUWhFaZzA2sdqlKjZA9ckaa2m3tiY7M1Xq8bZSoyEBGgcR1zRnOA4yhs25joVcrXDLk2SCAMRHC86HkcBmnS6OD7oRR3FsZFeQU3Zs7Z46lHE/zOHz0Xf/XcP8J/XXQR7nTHO+KEE07AySefjNFohEsvvRTPe97zcOGFF+IVr3gFjj/+eBhj8O53vxvvf//7sW/fPgDAeDzGc//8dzE3O1AWI5S7NYHXpgVwMYpKw/x005dzLmEPNVSAZVwCB7zEa+QlaFoRMJ6MIhAui/g3V670lXFD+tayhitHZEhX1uOOkgzyQvtc1SUe8qA745l/8jb86Z/+KT7zmU/j+a/6JP78Ff8SntHnvvgdfPk+py6wztdku9aCY45vYKaYigFwIILPmOD1qiESNXHls+vJBVDKLqlKI/lfNtZxAS4AMOlykewmn4O+l0A4bpsHOPBk2iEHEtHVkwO4abcLrvD5XDUwWtOUC1lpYDeAfXtpIWoawIpFqCwAFNElUhQpMFYqahuLIuoS+Tdd0CJcUKolp3TQmJZqjpldD8AYYIam9dkbaGEvRD7gxqwGJrgx0T0YmQDn2QZifrVy2GxNYCe4yX3k+6s0LQaTskBjbJJxIk6kKbsjGeb4fktfNEbkXzaxxLh0yXNfkWwN3SPLe6TBVKIDgrZXK4vrHX88Lr30UrRti6paTCN0pO3cc8/F3r17sWPHDmxsbOCKK67A3r178Y1vfhMXfO97WNuxA8966d/j+ifd2N8/GXmdVZ4ZifrHxthk7NDvAC28/LxSg3EouEV+n8sr+mRGBBJoQWwU5bWutFyQKA8rBd+Sblx6G+Lx4Flx4IA5OmSFqYsNyMIPMmUcZcGJRpBzmoLAbA2WnhRqjk6NMFovoVemwNoMbvOAv3Gv/Z1S4J5iYNm0SVBL/McP0yKkQSrjOI0V5HSUVvi4Abexj373lUJH7cVwK7spMAkkHaiwgULNMTc7/D2R14LGaYwVmLqdUNWxqOopyvUGxexyykPL9wPQotjOgdJLuQ5veMuUx4S4L9YbA34useS9AjzQ9+B68xD9P58SSz5apTSA0q3btZTCjit1carJaoxCNSh0iUJFWYXxcQ90b5V/p2SQcqPxGd87y3zqAih1GebuWWdQFKSjnXrWluYrmqe4z7VCfsAAVTKTre0CUGT2sS40RoXCalUEA5FJHAn6aEzYcCz2ZvJnOYeORqUHg3QcPpeUhyTeHoETSw/6ap91pWFXvXXojEPBXk/roLXCvDWYtwZloTH3j7bQCmWhYa1DWdLcHWNt7MJcEIOfLcQQD+NX3mN+/XKNpedlkzUhMv2C9Q8SebqHvFmbzudWAw1MYNHp/HTdh5sOpdaoCwbQtN/jznoa9lz3enjXG1+HV7/61TDGoK5r3OY2t8H3vve9AICf/exn47GPfSw++9nP4T/+42v4lSc9Cb/yxMfhmGN24dhjJtixY4TGNuhUjbEGyjUFdbymYmBNCzXyxr6xIehOrXCGKh3jHwpNBcM4OHY88oW9dByjvkInScAmgTjr7ISmo/lG9FrxOyzoeOee/18AgOtc5zp42tN+B7/yK4/Bt771LQDAa197Du56t1vDlSNAzxae9zXVrr3gOCxORaI9DBkmLAvwR0lAEE0QbAHT/3ND7nIeIGxFtjZ1z8hBSKBgcWDy/zkoXhYBy5/5WPSZB266bWdtYMUYmGilMDM7ou6ysKhWK8p53M2BHTu9e7eOM11ZxnRuWvtOz5q+YHn4m+NFKGeOxe+l0HgKl7aCDe5q40Elvz8urAKQNpcb37N0W0p3XpRSxGfLbrpZFyfQmGCfFw0dvm+7RUDGBlEEyzLPsTx3Da0mUCotNx7fkcOhpkvYHWY5i0IFtkSmjQPoHEAZylMbV+Fu97wH/uTZf45vfOMbuM1tboOr2l704hdj4/Dh8Hltxw4cfexxOOGUW+AJz/i/uNM974t6PE76Il8fM+mFAqadTbR65JbtZ1tiX6YFmVvOSNG2afWt9PsUAMRjc1XK1FVM5zDBCJI6yPQaUsNlpiaoixXUugljSkpGlACMAIJkg6U0FMMwQqNsyOFajuaoqnEEj0rRYjCZi9zCgNrYSKQJUqebLFJcXjnsqBEqiVU+y4yzUGtHE3vTTIHJOsWzmQYlGsxwDAFARXr3Us8xNbsAAG1HOYHhkOQft67E1D+DsjgO5Y4pVkuq4ElJ/gmguulBX/K3FCw5/Hyj0uBglmbUVczZzgw0AExWQ6lhOEfMtXMeUJv4TABitUKxFK9Zdg6wHZTPt8vGuvaFMmTsA8vt6D3qwDozsUIxARYWBkoT0I5uc2C1YvmVywLjqOcYx8A2NRK53zbOYsPPDZyTfa0uA6BbrQrMjcWsQ8K0Ut+NfZoBH7PCUiPMY5fmH4S5SGZGCtdm2Y5hD6pOa0bVdIzOOLStDYDSOheWC2sdSTb8b+NRibbr0BmL1XGFDg6ADYCbM3Lw9fJ6nXv3ctyas+n8XZ+hDsR5q7POOzsW56/prEvmDDm3hOMYYs7LQqMqNLRW6IyFrYFGkYyuswqACRmTGgPc/SEPxz0f+gg0syl++sPv4wuf+iQ+/aEP4HZ3vBNe+Hdn4ytf+jLe8cbX42UvexmO3rMHr3nXP+Eu97gXOBNKqeYwbg5rKpR6jk1XYVJoVKsAbnASlNbQ9aVkoGZN7xzBbnaAJo1xeNlAzCbDlToTi6QM8iZbTALRpaxFaQjQJ9JM0+H5f/luPOuP/wE3u9kpuO51rwsA2L17N77whX/Ft775TVxxxV486Qn3o0vAVSeAttuuteDYyjypQvPIJTQ5kXsM8oo6MF6Mo6uJ3VZpFCoxy2n0LW/PkgveNmfI5KBsRBzRouWbAgH6LgJBspo5lRu5ZrTXxPLkMessxqUmgAwfDKU1RiuUicKpvTTDNTPv/hQXx0yxLkJVwahnFIFDPG846xccv0CzllNpaEe5Ky2oMpOGRYeoPaa8kpw2x7ujfI7fg3OgtW24Z9YAMxCm55o+OzIUlA+uoAm21Aobnk3mibIL70kGXvCEheTYFNFNz5fBGJ0rvmPjANQrYd+cmeDr69NIG784FYqkAtyI1QZKTf24VBSs9/GPfgZaa+zatQtXR7vZKafA1SM85+/Owc5dOzFZWU30iHQfKdNLtVTis+JgobTPL7IwUnM/HxgrOZPM3zGYBcidym5TaVzKhY5crqkGlI/J97aSVYZKXbdR9sKSKFvUyfE6y1kQmljIxWe2CN4PS0V7xsUmFZQwqyGTyUo5QVHNUY2m0K4h16SzwEpD0gvAF/igsrix2IVghrk5G5ELA+PgASrT7SbrATi78TqU7eB0icpNMTfr2OiOQakbVGqKWm9gbtZB1URJf8zlho0vRgRESYJxFWbdLtTFBspiDlVYlHqOanKANIjzDTjbxSBFDkiczaNshCUkm1Ngx3pqwGsv9VrxQYf8THIphy6pGAq7fkUVPWU7WFVj3u0IMi/qGzF3Nt8L3x+/M67ECXhCxqWZWmS6v9zoonlLo9Ts+reAjXKKnJ3V2nmynOasTf9opp0NQWusQeb1RxIv0oMmgTldT7FwjVLmIccTA/DUg2ZhrcJ03iVSiQ7xmlfHJZqWQG5nHOatgTUOdVVg5oO/rHU4cHiOQivUVYFp06HrLOqqQFkqH9sZDXEAXsqVtiHvEs2xkSUPBrhxAeQzG8wMcMtSrez78DccrFx8AJRlek1NY9BpC4xKcNzwoc0WWiuMqgIzzQy4DeC/Mf67aoQbnHJL3OSWP4ffeNrvhvd37wc+GHd/wIPww+99B7uP2YPJjl3YaI1/HgVQjOCs9iCZAlHnahVKW5Rrc7gbFFBlCbdvHz2Yw4dp3FkLtV5D++fKGSxSb5UOWa1Q14E8U6NVoF6BKdZABdE6aDWF7jYoxqHzqSE1aI0sLc772vcBAHVd48ILL8RJJ52E73znO/jc5z6PB97/NDz+CU/BbW5zYx+nkuVivwbbtRYcS/uNo5BbN0FjVgPDwWxPY4qgLQKQBHfFwc8aU4S/tVKYdRHAMLDmqGIewMwus/aLrsmBc/+z1V7pVP9IE1oKEnjQj0vpgieXNoN5yuedRsc2xqFQlJ6GK60VRYey9mViA7PSAW0T3bIiApwrZrmw8BYEitmd0jUItdFZc2wMoG1kcAodpBTWs8Od/9zaSWDu64J+O9iUgeEAgPW6xNynS9KKIp8Pzrvw7ggoSSDHoMcEPXFqpLgw2VmrMK51eG655KL1EzNpUR200gkY4/fBur8YkIGEoeGFMLreIhMtr12+P8l+WlcCPlL7H855A+573/tepahm2X7wgx/gwY9+LE660Q0w6yxmnUkS/MtGGTzYo8Gax0VvCS9E/Azo3gBYJNH9EvzKdyQDl+JvMa/stOsWGButos6SriFlfVifKN2oMQgojTZnUKGT92iDkcVAnQNuGlX5/UcYlz5rBSy4rHhjLA7bCUYFASj2WrV2QhpWn1JuVB5CqeYo6w2qVFeUxLYaQ/KBdh6NUAY1ojogrEFC4wFRjsDjlI1ZIBbo0WUo7c6V1hq7Gg4xKg5RFggQeOTMDRxwGnTxmhhmANjsjoJxNA+tVBar5WUoVIeVtb3EMqspsLGP5FfGs+fTQ3FeqcfAhKQflA5OZNcoSoRy9tUIqhoT4AaIxdJiLiuqYJx3dgTlLAXhOY3GrqExq8hz/9YF5UJu7SSwqHVBpEsrgm1jPy4X5xg/9vv08lFnS/MJGfNkCDfGBuNdspfWOBhLc1dXOdgRV85M1wmAdMKcTg3gwDDpFVNB08vr1bjUnlgxSV5jJgWOnlR+O1o7DzUk+aj8AslAsyw0dAFov16tjIhFX6kL1KVC0/n5tyqITbUOFViSYWCsw+qkQtMaNK2B1go712oipFrnVUWR6Mi9tTw+eV2wzqHrpJFCz1E227pwLRIQ031RUGEunZh7IqMqaf2YzbtgIPB+nbGYzbtwjLLUsIbOVRYajab/61IF5l6y18k9WYMrLrsMx133ONz4pqcEiaB1kcApdUmZPLT18r4SjQFK1aAY+2q517k+lM+R7gAaW5tTKG2h9ozhDk2JPc61xpxFp/YVcIPGuIIrR+hMHbxiSlnYctVjjiip+MLn/gM/uewgzjnn93HGGXfAi/7yHXjxi1+Ev/u7s/GpT30KdV3i71/9h7je8ccFSVOe5eqabNdicMzs79gn9J4EdtI5jZkh1mduqsA6cediOQK7aLlDyuAJ1ltG5jCyUrMulheem9TNS1rkqMMEops+1RenFi8P8pEIFJNyCwnGeDJmt1tjLMZlgdYCSsXyy6Waw2mNauygnIPDgcgMa+GGBeJneA1jwbIVfuCKFiyZUxWIi7dndEyxFtyWtL+ObBMs6sL6gBZgo3VJfk8u9zwqdKKZ44k8BtxF+UQM0mD2Tz7btM9ozc9Vsp0uPGP2FlhH7i/t32NdqJDvkoEsf5YTd/4umUkuxaKVsqbx2oqCGUsqht25EQ7vvRw/uui/8OhHPRJXpn3605/G617/etz0JjfBbW5zG1x66aU4vLGBH/3whxh5dj7qb9N9JdDlxtcu75W/T4NVPevvbHBZxncAvyjxtotSB9limqj0d60UOji0lp61NEor4W5mg9U454NlTIgup3dLzF4Mmko9OsHLoNWCMaWVwuFGBYOapVkMQKYdUOoivP/WabSYkAtSkYSh1oexWl6OalT7xcXrAcerIS1jqHondbbMFlsLKOHB4YcMeDY5lt9mBlVmkyn1FMp20AU/ZwLEFCDrg5z1nORpTtPYLUao9DQATA2DuaF3NS41OlvgQHMcAOBAc3yQbNT1Rsg9XOop1MwHKbZzhKIqSkUwzN4Z/0zmdh2yGIT2zD1LIgBg3u0IRTwsilCumIFv9HrMwnielBMfLEWeq7mxWK9LAFEuUQqjhHOtc580LgU3st/0GZ0EUgsvQyIg3hhLQW7ScPTH2Ji1mLcGI8+u0m9I2MwIyFQgU+h6EIy7yGbTnMr/+DCkj1UhwKzUbLBqzLo0j/F07q/Hs6AyTVu8X9JJzzqLsqQgvaY1KEuNpvHzZ0HXPRdetM4H+DE729WL9ymfNbU0zSMbFvJZaq3Q+YEeJBTpbmjndO5CKxjrYARrbC2xzFGHHCc2BuDGGlQslxPgu+ssylJj3hp0vnJgVzqxNhDWWKkK1IXGXz/nD/HWV70cO3btwgN+8eHQVYV/++xncPjgAZx0ys3wune/33uMR4Ch2B0NA6cKtHZMRm9BTK/bczww36AA2aaFK0uSOVlLuYybhl7aygoQslkxSPaGajWCKmqgGnvJ6ggFOhg3wrzbgZE+6F/5Ggq7gRe/6K14xrNeBwB48pPOwGRS4wbHH41zv/pD/Ou//is+/OEP4+V/+1u4wXVWADcNxm8hisBc0+1aC441iBmZmR2w7XU8K0mdjoK0nAeucYKi0pwpSI3ZKaQbif7moh7MfhGzGOUVrY0SDSAFW3mQXrjuTFaRp+KJ4E17DSozzS5ZwPn8hc+8wJYo53BuXAnFAEFVGK8o0gO2ovPlzDFLLBgAK43geEsYKqF/5G1tR9alb1JzTEn1q+CKrbTF/rnB4aZLAk8YPM4N5SfmCG0J1EhaQh82Wotxmb5LCeg4uCIAYEuMpNXSU8CBmOI5G0BXDnMDTLyVz+CbwDO/hwgU84hvCdw55yjAuU11Apr5XY78/RunAFfga/9xIQDgxBNPxJVpH/jAP6PtOly2/yBe9apX4djrXg/3e9gj8bRnP9enHYyFAmTrxPPmMQAg6OUoG4gdzNTCx5QsTmRkgLJUQcPI7Iu1jvImiGfC0e0LrlPvgg7vWUh0tVJoBNMvDd/cYB15N2dnI3McpRap7IKM6riYsT4dkPKr6BXi2+D5hj/To9IoNRUKmJt1WFdhXO5HqRp0IPCqtEE5aVBM5oHlDYFvHNjHjfX/1gKqA3QdDV+lKTAXiMfxzUGjMasY6UOo3KGQ2YFzVDObbH1BpFm3g/Jz++pyMqUfZQhRODDvwrNlAFnqCawboy52BylVpac+6LGDKkzIM9wYYqAqPUVnPPudVMKsQrxCqedozGoSBDs3YzTGhWwL1jkcbgwas7GwHrCEa6eP2JfGt+zD1Pd5Lk4LdEgvUZwXYr/IJUqyX5JhRusU6+Kbju6uMxZVqQnkqQiS60waxCAOxmtfW4Wq1NgEsbIM1DgAjpnU1XEZgDBAc9ZKVSwY7jP/HNfqEqXWmJaRtKi0SoqkLHp3yBM4KR02fCET66UXcX6wmHrJxcjfW2dsYFy1Umg8gVJ6OUduKOeBc9JoIIlK7PcMbqtCo2WWXmyjlQoMNl9jXRVoWsq+oZ1CCZ2ctxCgWzYDh8LFd85sM+9XGo1RVXhmnfTJZMBYzH16xDvf8Y741If+GUor3OqWt8Tll9f4wr98Am3bYjKqg6HIOaG5KFJrJ1ClgS4bGvfVOHiP1cYhYP9BuKmvmMvAWGtK3cZSisp7b8oR6fjrCVD4yrXQ0KCxX8H6DD4WJRpc+IOf4vkv/kf8wi/8As477zy87vUfSZ7LS1/6Ejz4QXfCU576ULiiirnVuzng0tz212S71oJj5y2Mzk6gPDBm2cNcDFSpC5Z5Hvl/mZWC0sXEv60jvRdPZNJlzuwQH1sygqxv6nMjy0wVqc5YeTYqMo4xFZwKpYzzxZdZCzkxySIFxpddNsUqinqDkvYXIoWbCORJ8pDKhN45EOZzhXzIXmOk04kbQOKOpEISxsssDMZlEZ4jMxvEykaQy//zu2sMPDvlwve5m17G80h3WWe80CMD0BxFHN6NWPzmxoaFXop5ODCSrzFnilbFQhPPEd2w8t44ICyCS+oT53/tPwEA733ve3HnO98Zp59++hHlOj722D04PJ3i7mc8ADc+5Wb481ecg6IovMEnQVv6/IxYfMhYkVUbU32yXBStQ3QRS8bFOVgT+zpHuAORwWHmRS4mDbM9Ngbr8PsBVIic5wpy1tLf/A4kQOXriPcVPQSUdolTv0Ugw1rR/nemQlEaecxU8BXbItuuwvhu7CpKO4coLEnPxIyg1SSWz9UkT3A7j6PUSbPDUW6xRb+QlULjNfiy7SihQYtogTk6N4HzpZUBCno2JlYuY3lIPA5XDyWvD7OF0V1swhxKjGyFlaqAcRUqPU0KNrDXqw0GC+Wf5qxDvJ1SFq7TkGWZDzUGs64NRt+0o+vojFsItjIeEK2OK2hlEhJEuvCljIHJjDzDUK5vBThQeNFYN07OJ9QKxSnONFBaAsj8PvmVWRpTErjJxjIBaKQsLH8PYDIqw30f2GhQFhqr4zL092lnMCkLGOcw8XMzjQm6vnHJ8100GFgKFVNjpqlQeQxOSo15CBh0YKbfWodRVQTjWWuFWhMY1UqhNZQajsmNDjaAVSAysrN5h9oDTckuz1ticuV182c2LLRW0F6GorWCdv5cMn7EEkjujA2gnZloq1PjO9cnQwtjXbDQ3KpCo2mJXac+qPHY3/pdfPTdb4dSCq95zavDth/96Efx1a9+FZfv24c9xxyDTmtMyhFKNaesLKJwkoNnfk1H88R4HZgdokqbdQ21dx8BZLrxKKfgKpeSPNOU1copDWt9YCsqIMiTDJTq0DYznPmgP8JoNMHjHvc4PP7xj8d5552Hl7/85fjNpzwYH/rIV/CkJ9wHT/+dh1E1PNtF+Vg1ghrwIF4T7VoLjtl1v9ntRtVqzE2ckDlogQMYuOUSCCmZAACrKAUOBwAxmOHjMMAutcLML/BlIXRV0kIXjGUIevATJlu5kYz1oEhH19XEa5uYkZr564qTFe2nlRIsliJWJSS8H0HbCYqiw9TsxNpkDtW1xCRZE0GxZIyBGNzjHwrhJw2HBjxbK11GMCyq45Dl6icf73bhxkVXGkN5IDkbCGV3IGZhszXhmQPw5VZNshjETBIuJJ/nd8G6PdPGtELGOj/uY6Q1GxmAXAxj55g1FisjTslkAyMsGaSN1iRMtgT00y4GXkSAFc8njSnWJBOoo2AwrRT+/UtfBAB8/OMfx8c//nG89KUvxY1vfGNstz30oQ/Fc57zHJzz0r8EADzgFx+G+z3kFzHtLA43XbhWXvj5GgrFKQT9YsKuQTF4ZH5jrRDyngLp4szvQH6fM7kLwS7CEJX7MkvDY6yD6KbeEIqOnMVJVrqk4fftrEFjFOrCBV0xkIJkCYok68gAqi60l29Iz1Fq8AAx1kE2rRRWqxGMu45f4NrAhMqKYM5p1MUGjKugYVDqKcpRg1pvYFQcjNkiAJFJxoZx6YTsQgJl5zQ2zVEhGE85G4rztG4CzlLB1eToWGkVspjyL2puOc0fg2JmYw/4+IG5KVDpMawbCeOUMynQeaKRMgIx9/xym8BoUrrHqDXl9GEMYA4cnkf9pyV9a9dZbMzoHAcOzbF7xzhhHptRfFbcB2M6MfqevUPWITEm+bfG5Pm649rDa4n0HPI8wgA5yPs6BIBcIrrrm47kCcyqU57hyLLmzToXNL5yXtyctklwGQeQHS4M6jJdi4glJkkIr4VxvGg/p6vgSZGyRZaSdJZkTvPCJnPAdN4FrS+A4FEqXJQ3FI5ALMsuWHrRdTbMCfI+AGKjc8OI5xIAAWTLd50fg/eXhgY0YPk+xe/0fruE4c8B+8gTJ1GLTPcycgWtX9ah2r0H9/vlx+PT73k7jDEoigIHDhzAa845B7c+/U4Yre/CtLMg76XCvNiBcUmZocYFSRw2u91YKYGC0xqWFVCUFPyrC2CXhVpb9QGysxgAywXCyirmNPaSSsreUnlPDxuT9NmYBr/32y/Bd75zEV760pfiute9Li644AK87nWvwwMfeCe87G9+MyETMN8Icio72klf/b8pW8ULXvAC/OEf/iGe9rSn4aUvfSkA4ElPehLe8IY3JNudccYZ+PCHPxw+X3zxxXjUox6F73znO3juc5+L3/qt3wq/fe1rX8NznvMcfPGLX8TBgwdx3HHH4Y53vCP+9m//Fscee+y2rmtmSI827ywOz1rMOtLr7J+1CeAF0qwUuYUv3UjMgs06k7CONizA1Mk5ty5A7Ba7qgDSHAVrWAw261yQKMXBEq+DmGaeSGiRIFASS2vKhVcWN+BFmRhYF1IOAcSsd77oxqY5Biv1BlXKM5RPVXHhDwmIAYSCIKoLNKwq6lRvzPSsrJaHqAc3oJzThZrToq46OEwA0KR6qGmTXMN8D3yfpIcj0HJgRvezUhfYbExgDctSBTddYx1aP4HRIqKSgIuylMFX8d1ohcTYAfxxRCS6ZFd5H+so4XswrhigOYe60p4djNpwfl+SaWaQzywL5/zUCvjqV74czlcUBa53vevhSNrGxgaKokQ9GuG+D304bn7q6TjUmCwokP7nvic/s1aX9fU8fhgYWxsBb6FVCCLi581ARTZmZPr0kqF/s3GbRanzgiijy4NbWRw/34cbX0pgkqz/XZMBZj3I5cwh9Hxinyl1NHKkwcPGE28fMwBEoyl/zvR3/H/aKazXK34eSoEm5WpWqPTO8IzqYicq7TAuDmJUrEOrFnW5gUJ1KPScXJW+yfzMUp8rAXPrJmjaVayWl1MJZa8xBsjgtShDcG2hWhRokyqI0vCT0gEGtQFTiP4PsFyBiA2WQnCQGvchnpZYitN2Fk1rMJ13iTuczzlvaf6u/BzRstdCIwRMxQ4C7Ds4S8CUDKriPlQWOmRpCG7xYlErL4NK5TXJOYL+9yypn5M6kTWC5wLrvFyAyMSFMcPjRKuoz2UjgI2DsL2/pkKMH8AHu3opBkCAmTNLhOIdpca6z407LotQ4ILy97IUIGaxaQwZLGl1udSwGPkA83FN+9eVRtfRvMsglEGvVqlsQr4bvr78OzaIACQyiBBQ6FlpZoDDmuEBr05QHMJ7HMp7LP8OqewAwIr5zW/HaxQdywYDp2kMViYVOkP97Y5nPBjvPefv8Za3vAW3v/3t8fznvwDTzU084NGPgwOEd0Z7WV8BYBXTYg2TcgfGxUFMzU5UqkahO6jSohjPQz5iJ1NLrq4Sw8yDrRrFnMZKecml8l6sFo1dBVe6BYCvnPc9PP23noPzzv0mzjrrLJx44ok4//zz8Rd/8Re4+S1uhLe95dkEjK2lWIqQWYf0zA4FNrvdONRm7rNrsF0lcPzlL38Zr3zlK3HrW9964bf73//+eN3rXhc+j0ZpCo5nPvOZOPPMM/Hyl78cj3zkI3H/+98fJ598Mi677DLc+973xoMe9CB85CMfwa5du/CDH/wA73vf+7CxsZiPb6g1hh7ipZtzWEO3udGaYM3KssIsheBGaXlVkD8AZDzRgIqTcFh02R1khU5SR9dcWejE3ZUMDtFY48RAAogTELuEI+tEDCIv1ixBkAArLs5+UnUOrdVQRUFaX7T+Wa1iVByknJ7jdSiloVpF7LEWRUD6yrvCB8coH3SnhHRC6dQV7CyghJTCAdqzxbLyGzPx63URWCMCXZyhogjvcK0usdka7J7EXJ8rdYFZZ/w29J7qEbvOVFjcOmuxOkktUV68mAlm1melLnBw2qIz9E4qcWM5i8n9alRozA1p5qTrjtxw3nAqLLTSIX8yAQMb/mZAGrOkREnAne5xb3zl85/BDy/4Hm5y05tiMpks9KmhZq3FC1/4QtzidqfiL1/7D1jZuZuyKHjGONe6x3ul/yUbzlIKCQi7Lg2iyUFpZ2jSl6xJl4ByFcaYbeOi01obFrB8gc+BEJCmVFpwZ4ptgntcqzAO6UYR3LYA55/VCVtunA1eA6lDjvci+0cEOfR/fK5y7PJ3xEAbz6y2MSOGSMNF/UPDFiy9omtQymJmKACt0lNsYA9Wyr2AAbRqUYAKkgRj1ZUhGI9ZXysY5FI1aOyaL4hB+mPOBJTrekttPJNcIaY9TDOPjMsieH1SL4lFY9LsPhveY8T9KhgOAlR0xmLuARgDYJlZgJ976G8dku/D+8qAL/e9qoh609B3/LhOKr0x6MqMt0Ir8q0p5b0U/rq7aARJ5hYWaDpA69h/EnYS6dwzqgoCjzKi3//Z+EIb0YumyYPmzyfvP2iZxXPt5Pi2LjmObhU2pm3w2vB44swNbMDE/ZEcoy65AAb15Vb0azYmGSBzGjipjw7MbKbrZQaZ+0kOoPl/+Z5kv2KWnJ/TkGHNfSxnlsP7GehPwUiCSvooz4UldDBWtFZobRqIeKNb3BYPe8rv4B0v/2u84x3vwK5j9uABj3kSfv6+ZwrPlQfY0ABMiG8p9Rir1SrGpQ0a/1LPoVWHutpAPToM3Rzy2XFauK6hipfWimIfJXmIhWfZoQi1JZyZ4TOfOhdvfuM78I63/jNudMIJ+Iu/eD5OOeUUfPazn8WLXvQiAMDrXvU0rE1Kn8PcSzwKys5jCsogY1Bi3/wEXDbdt/CMr6l2pcHx4cOH8djHPhavfvWr8bznPW/h99FohOOOO25w/3PPPRevetWrsLKygl/6pV/Ceeedh5NPPhmf//znyT3wmteg9HkuTzzxRNzznvc8outjbePhxmJcL5aEbFoLVDqAjzy6twkTAzO+Lmh/ZKRromf1A6ozFk0bJ5HGpzFjzVel4+QjtVQAEqarEL/x5AIsgrHWOqCLCwYnnad75eIR/rlYSvECILDHcMDc7MC43I9ZtwuTYg5gTAF6IVtFGZlgbsq7WDhNm/bFBYDIIHMaJa6AJRLoy6ZUDBxoXCz4QZkgXEhVt9kCB33JYk5fw+5uLs/JWS0YwNgqLkBGPPPVSRUnKMHo+CuCdL1rBV/FyYBXHA7YkWBaMoT8niajQkSHe/DIzFeZapwZHGnFEh2d/M661el0ig+9i1xqo9EYP3erW+FI2t69e7GxsYHHPOW3sLbrKB/gKF2ZcXFi40SWmI33ErdvOgtO4RSCWYQsQkZmJ0xwtvjkaZQAQAuWnxu7UwHhxWHwVdLC0rSLbuQ8ddO8NQEgyMULEABH+7S5XmrBEf8kMaHtWZMqs11EXaVLDI70OadMKv/NTUp3ZFpJjqGgr+I1cRBUDHorYNyav7bjEVzgukGlp+CqfTwfcNCObAoGBiXl8RX5f+nOtI9foBzllMWngHVF6CNRApFrcvPPnj2zUdNpHbAx6zDzTDCDBQmGylIHCcCBww3mjQnFF+S52JsARBAs59u8bxSiP3Sd6gFYxoPmeExmarngQ85a1lUR2H7pyeL74cCy4Jq3wluVjQt68wRwmfmtijIwrNyYabXZs2cSh48BxPEhx0IYXyYdZ42NpE8HYC6iI1PDQgdgJyURWinMGgcglb/xLZJUUIV+0VlH3jtPXhlrghe2LcS8ItbOvmcWAK1fr/Pf5bUHtr1LY5K4yee2wBLz2Ea8lvy8bHCF+Q/+PRXAXMhgeDv5Xh/ylKfjdvc8A4f2XoFTbnd77Nx9FMZVkVUzJO8VeSpdMLqp2mKBUo9QqBHWayr2M9frGBf7UZcTVLXPud7OCQ8YKlMfyrxTnr4ks5VzGpdfdgBPfvwz8KlPfB579uzBU5/6VJxxxhnQWuMZz/h9fOtb38bJN7kh1lYq7FwbR2CsNDAeA0WFFuuYG2KgN9pjsHdmcHhAFnRNtCsNjs866yw88IEPxH3uc59ecPypT30Kxx57LHbv3o173eteeN7znoejjz46/H7iiSfife97Hx70oAfhX/7lX/Arv/IrAIDjjjsOXdfhPe95Dx7xiEdsO8BoPp9jPo+ZFvYfJF1NYyw673KXrEFZKuGai6wvEDt5VergYqpKHSYnAKFj80IuB2An3LpsvbIbR6vUbcX7hdQwYnAZD6atcSjh80VmrBSDGL7XUmuvPxaBS4ZKmraWsivQfZawrgxFNuAAY0ewqsKonECrWQqGi3QAAPBSC185z1lAW8AH3yQFCoCoOXZcrVCjQAerOmivVSoU6Z0LVSUBjit+sFdaYdeYmGLOSVsXXCyDPAElmLVlyQKdd9bahMlkl/6oKiI774EnnTdObAxw1kYU0MH7dR1txzmneVsAPtOADUw3578mvTDQaZcwfzoBVBIEycC+yDB9++vnY3NjAw984APx4x//GHe/+92xnXbJJZfgQx/6EC655BJorXHTW93aP08T+pLsOzyZsh6fgY7sf7PGBANOMnthQc6MSNnyMSVbMq40ggtyvkQ3mR8rXxzldSTjSAAmboUYv7yos67RalpwWE5RhOcV2X7AJQaTdKPI62ByO77f/HPUL6e502MWFPY2cNQ/BUiy5pfeHeel5nK1NH7WfKot2depuAWnf9KqSwJnZWELBsbOZ6cg9pg9ILGvtKE/RwNAZn/gxTzmmE+lBkwUtJ0NXgdmhznoCgA2Zk2Y59kzkfchbn1gRjKj5OFJDTvuF/I7CZp5HBRtZPik0cX30Rkdrk2+a61UKBjRB04BQIug1Pza+FiFVih0EWVMygW2WJ5XSgTy8Sm1u7yWlYWGVW5h+76xbU30+vB6aq0L95a3PC9wVWiqVqxiZp+xIBNK7VCXcdzJZ9UaG7MXFunzy42G3mcsDdmsD+gsqJbPJ5lq3n5hX0HCJNckDCMueiK9FF1r0RqLsrMUS+Hff10XuN5NbhmY+llj0BkX5DhAXFMmZdSC0zMjDy17cVrrMCknGJer6OwIdbFBWWP0BoqqRVHPoe0MsAaq82XsBfHllIazGl/92vfwyF/8bRw+tInnPve5uP3tbx9w3Lnnnotvfevb+Kf3/hke8OD7odRTqM19CNmslIYrR2jMKuZ2BzbaY3wKXoXGtOHaD3p8x200Gi2oE65qu1Lg+G1vexvOPfdcfPnLX+79/f73vz8e9rCH4cQTT8QFF1yAZz3rWXjAAx6AL3zhCygK6rF/9md/hvvf//543OMeh6c+9am43e1uBwC4053uhGc961l4zGMeg6c85Sm4wx3ugHvd6154whOegOtc5zqD1/T85z8ff/Inf7LwvYWDEW4JgIBu0wpGGDqI8Nn9ZB0FAfCA5UlZAl4ObpD6oWTgcZnp1iZpaGQLwUMSJIhBxoCa3EcKdRWZSm6FmCgpl2jhJxBaiCh4iNgHmQ+ZSsMW3k1KBUKoAhaJ95XqCPAC1GlFcQBO5WZBrlgoQGtfPtdZsiil/jhU1aP8o3R1BTRaFB4cc/ol60o4PRIltjkbCGAcgcW1WmPTA4E9K3UoxpJHirfeeqdgPB1YBWDYzR51o8zkuhDsOK51WAC1AESBpRaM4KQsvE4yZqPQnmHmCGX+LIE5F22he2Brnyc6usZvfPVclGWJX/3VX0VVbS9I4XOf+xxe8tKXYrK6jqOOPRaP/PXfQr17DzZbA+NlIH3AOGYCiYGsnXUhq4SxFAwznxmSrIj92W0rDU/J0HVdZLj4fRgXA+e0BzRhETOL703mKl5oLHmKyROCvcbjRmuKQJfb07OOwICj2uu6CN+z9k9rjsxPvQO5QcDsrxy/tFiJ4MPMiyAbAVxmgBA0m5ENskESRunqBHvv+16lCy8vU9DKBBaZ7xeIxR4m5SSk5+PMEMtaAMmKrkUWWIkZQRZTKhKwh8+QQr/1uc4DULM+lVmh0YL6JOtgAYRMKDm7bK0jBk+ASwlgAkBlhk8cJ+kXEgAJwoXBL7vq560Nc0Wh4vfcn5Y1JlOKJefi/0thdBVZp5GfcwZ1SJ7BYJnHqfTGpBeJZH9rXVg7c6OjsSasrfwMJaucX18JDaudyOwkx04EfbFFoNxZoCyLxBtsdSyakpIhsSV9IZtjpEGRfC+Owc+nj6lm6YR8VrnkQ2ILKRWR18V5oKfzDqO6oHzQ4lkChFWsLRaY+EjGKL9exflm1hl0Plhy2tHYX6lGGOnD6HSN0hcmKtQcZdmgKOdxfReM8d59h/HIX/w/GNUT/OlfPy+JEbPW4o1vfANOO/0WuM+ZD4JWmxT74EExdAGjJrC2xNzuwKHmOOyfF5gbWkM3W4Op9wDf4AY3SJ7vH//xH+O5z33uwvu5Kk0517eiDLeLLroIp512Gj72sY8FrfE97nEP3Pa2tw0BeXn7/ve/jxvf+Mb4+Mc/jnvf+97h+7ZtcejQIRx11FEL+1xxxRX45Cc/iS996Ut4z3veg7179+Izn/kMfu7nfq73HDlzfP5FP8Vdb3VTvOoL38bajh2eZVhkAVgPzGlY5MTDv3cZu8XfSbdYVWrMm1SPJi1mOSnwxMetaUyiN84DinhSLEuNSV2GRO5aKcjSmTIwT5bCJYCsvKuVrEQCZS64U53ToTpXrQ/7ZPxNALJAjzvVp4KTTStLTJNfTZSyUM6SVSkqSVlXobNUqpVSt9Xh2MbWITF/eEZBIx7BWoxEj+eXDKhMoTTtDGaN38+n/2H37CjLDcqeBbqfCEoZIC8WbIm/SaaQgQVfE2/LvyUTq1bi/fE5bLIQcOqkH3z3u/jdRz8Ue47ajRe84AXYTrPW4td/4zdw3ZvcEr/3N6/GUbt2JJk0GBjHinIRcAGs+aS+1bQ2GItdZxN3b+5i7GOIuDWtQdfZBAwPGSwL7l2xoKT3mbI/krnOFyPehsGLPE8fAMrHI4Md1lbyuMzfbXIfwiACoj49GmN5YCdtNzcx53QfY9dZF+Qs1qbprxhgAhGIyHssCy52ETPgSBAvjR0pI+LrK1QKVFJGOzLBfI+yfzPID8VUbOwLLLfhfsYMHZMJDDCpdoEJzLJ8LgtubvF+87lettwb2Psue8CNlG7wZ96W2FC1cIz8GhaA1UD/y7eVrGQO/mVfkC03HhiY5frZvvUpf17J5+w8+f3J6+P1MQff8j6TdbCQYzOyynzehXM7MU5EvFBOSvXhg9yY2AogA1GW0guws365VR+z1oX5UfadMsw5OjwbZp3HozLMSda5JHez1hTkKYuyMLHDY5+9SqtVgXGpMSpmoVJmpade/kjMMec05/a3L3s3nv67L8SDH/xg3PKWt8Qd7nAHVFWFn/zkJzjnnHPwxS9+ER/99Ftx15+/eTiWTC3buRE2u92YmV04MK9xYN6J1IsW+/bvx+NvfzIuuugi7NixI5z3mmCOjxgcv/e978Uv/dIvBQYYAIwxUEpBa435fJ78xm3Pnj143vOeh9/4jd844otsmga3u93tcNpppy1kwRhq5/7gxzj1xOvhpZ/8T0zW1gMgAtJBxy24jcqY8oX/5n98DNn5Gehy3kV5PGsdRnV8FonwX0xYG7M2AG5mFvJrDW5dvyDLDi7LTkswVQd3WZxExqUOi2ClHQrVJoxQqck6HBeHqPP6JOJ5HlTjqhCYk5dPLVUT0jpZp1FkblnWNFpXorMjylphCSRzTlTKZToJgT4MSCVzyffJBSt4EsxzxnJVKwZ7PGdxcEie7ovfn/yemWLJrMo8l+FdCeAjQTO/h9ylL0GG3C6/Bz73oQP78St3uT0O7NuLxz3ucbjPfe6TyJWG2uHDh/HEJz4R1z3xZPze35yDE29ycpADyIpZ0aiI13rIB+k1XjLBmQAS96wAqnJc9E3+vCgRsE7BS76A56BLAjW5jVzUeUFIWDcxnvK2DCjnwAdAsiBJDw+Pz3FdLLC+C+fM+sxWLVngEwYqbiPBpDxHAJd+/7oqUgDgt2MjscxkQrxpXegQ8AfElJHSqEq9DhEgx/tIpRY8JhkUS8+ajO2Q4JjvRRpW/B0D5CFwnLc+cJzsI/pnkB8YF/7uA1J94I/BSw664/+pB7APMPWB7b4mA+P67lseJ5cF9oFB6fWR3yfPTLChgx65DCDzNeZstzQG5PXKAD9+ZrkBlzeWI/H4kUa4NBr7gGtukC+7n77n1Ld9zh4vO24IehbAWAapSgOdW/6MJJYJxnyhkzEuiba6kJ4iwhDrdYlx0YSy9nE/wgq8thfo8N3v/BCPfPjTcfF/XYLNzU0cffRROProY3DhhRdiz56j8Kcv+r945KPuE1hoehZ+jffr/2Z3FA7Ma0w7GyR/lLLVYt/e/fjVO94UBw4cSMDxNdGOWFZx73vfG+eff37y3ZOf/GTc7GY3wx/8wR/0AuOLL74YV1xxBa573eteqYus6xo3vvGNjyhbhWyRYYjaMytcmcwey1yIEhRzx2erWQ4EmXBcupY6Q7ohabWF4/p9p9MuTPLcwiSlFOq6CAtunDCYXaKBz3lyOXgs1XdKICFToQGAo7RNKJJ65Z0dwSmNVnUBGCvH9xcj0m3QGvpSs36cK0WaKAbMnathHLHJsTCASOumLLSzcKpFgVjBD6CSl8pZXwyAtIydXVxsgZhnlGQjUtfIQIF0v5Uugju6sxZWzqmaJ3e/b1K9zbtrJSsT2LUU4ET3fkx63wvKlkzqfG+pG5FStv38fe+Pz3zoA3jTm96E7373u/ijP/qjwWNwW1tbw4tf/GK88IUvwtPuf2ccf+KN8cTf/yPc+0EPFcAmA17O+VyZCptz8m7M5h02pm2i6WVtppz4tVLBYMwBNI+XeZuOpT5Gqi9ICuhf8OX5aZtFAGNFTnG655QpsxawLKOTbmPrQlAN39eoLsI2IVjHMhurIaU3vEDHNiSlWAT/4RrEtnIhl0wYz0HymefbpCVuY5t52Qia9BmXBVXqmsFiXOtg8HHRBgnqePGWh+drzlNmSllSyD/eAziGmN1wbh0NsWnnU7FZ3w+XAJwc5Ekg3NcYFEuQLIEyN63TVF3sUczze2utkuISsgKkduL+4OcU8bwXwPUC4ZMG7y08Bx33YWKIf18GcPlYfLw8Q8QQu8pNgrvORCMmB/2s7Q/3pH3+YcHMU9YVBojpfBp0tQIML7DsoP3CdfeMOempWeibOo5BukRvTGca7r75SyvCIXmTz46lYlURPXxSPta2JDlhUk+r6A2VGbQAYv5DtT3rMBcyMwbUTWkDOI5B7hpaGTSmxLisUepJWNsrN0Wp5mDvcqE6nHCTm+HfvvYhGFfiG//5Lbz5De/G5sYB/MoTfxG/9tQnYbKyhs4aKhYSZGgUyGtchcasYtqN0VoTGGOudyCNmZ9FO2JwvL6+jltlkfGrq6s4+uijcatb3QqHDx/Gn/zJn+DhD384jjvuOFxwwQV4xjOegZNPPhlnnHHGlsf/wAc+gLe97W149KMfjZve9KZwzuH9738/PvjBDyap4bZqK77XW+fC7Gyd88E0NKAMKANF7kZaAMiuf8KWpW0ly8QMMCAmJefChG2si4C6Z9CytjEHxqwdiosqW8VxoU2ZtkUAJhcm3l+6NaCIFe7cCAXm0CJHK4FpBrACKIsAHc1oQYFAr9cz835atZTqJS9aD0QwDityqMby0pxlg+djGyYMqgQIMBNLxgLdv0qeEaF4+r/UOklEn7fwnJIBSYuxBDFDANe4eK7FdxN/o+veGiRbB6ytr+OO9z4DH37nW3HrW98GT33qUwf3y9tJJ52El770JfjkJz+JV7ziFbj4gu8m4D4Cr/R5hOBAzjEqgFlgbBkku5iBYsg7AyDLN73I8vH5eL9loBGggJUFlpq7pV0cZ3Ibax2gM/esBaBpIZf7hoWwoIWIF5bwm+bF0PrFnT0TGXvuDdS+7/rGbLpdysDZHva973P+d84SAoCdpxIUALA6es06vrfE4OP5tf96+btUDiIA/oALftl9c8t15iMpjxPABlh0kS8DgRIAJ+cTAFfx4h7yJC9qgkOWApNKHcIiL/poui9/nRuB/p5z0CcCVpPnlW2X9GMdf5NtIbAse445A8zPWoK9/JjyPRnnYEw2tvhaTFyj+fjMLNdVEe5zIU+z2A5IAalMjSc9gvFakRhUfM983GAk63hcBtcAqHy3Xhx3ff0v9AubriFDhowEw/Jvm/EI3NdK6MG+zenhGNQDi+kGI5EWiSbyyvp3UHhJJsizSwH0JZSysK6ClGHe4lY3w/Ne/Jxk3Q/vx5Vwlsq8cwrJ1k5gXSm8Tjn2Aooeg+Kaald7hbyiKPAf//EfeMMb3oD9+/fjete7Hu53v/vhz/7sz7alCbnFLW6BlZUVPP3pT8dFF12E0WiEm9zkJnjNa16Dxz/+8du+Ds7XahxBuISpsLEzslBfF5FhkS4nmXeSo+QlK8OBIEFILyYWGUVtrAt5CqXWUmoA66rAqCpQV0XUYXlALBfuCAApiEUmXYe4V1mhiL8HlJcYKGgfjEeZggqKNvfyidZOSE+k2oQlps7rBfhZTlQFAppKVVCooYWUQhMvAgofsZBdz4pZNcgxUEI7wCoPmkHsslYEknOtcSRvCHAymGSADPD2vkgKOF80P7O4gFsPpmVyeAmUIzPk37Nd1OjJSTbKLZJNfKAkX188HjH8Pbo+56AAvOGvX4Db3va2+OM//uNeT81QM8bgS1/6Ej74oQ+hKEuc+chHB8Mn3n+a3cA60hhPG2KMZ/Mu6g9VyjgtXK91oY+XBaXaAgQT7xcFNhbDsYyDE/efs6A5yFVaw1kbwIpk8hL3bBblX2QLiBy70lugM+AR3r9Fks2GmS5rqZRsmFNYlrMwsS9m8JAV/PL0jXSN/cCRv+sz6IE0hWEwSET1NACJNCwweMLYJ/YzlZPIZym9W+m98HnS+8xBcB/7nbur5e/LtOrh+gSw4e22wxjL71zPWOTvlbiprrW9LHL8wAfPADIQAgXpRjNwBqCZHkY9XgGnYIgeEBDQZJbVRGNS9v1CgDw5npJ7zsAdb5MHkfM6N2jULDE85KMMwF8UYZEtlw9MfbCv7JvJdYnfJDkl13Aej/n9ACwn4u0Wf4dFf/EPnTLEyXPMjH/JOIdgR39crRUB7cwQkd4DHv+xKd93aDtOrbcQQOrnqhDcmoxh7UkC771QniwIayTL7KJciwz4AlavBq8wEVkl+gJ3ZSVceCmm9X2ZvcXGVZh28OXlKSczSyabjhIkzP7fkMpNtk996lPh78lkgo985CNX+lgnnXQSXvWqV13la8r1Q2Uma+A668z+MKNb6FgEgAc5SymAmGuQgTFP0KNKh/04ICcvWcn758URWK9IqWsKTEZlCJJhl3eRgS/+fm5sKA8tdYxhMbUu0QPy5bTWoXYOxikUftY2EAyyBRq1ikpPAQdYIaFg2YNlSYVoJJMQwXieDWZArcV3OXscxPkwxGiTo8oz2gbGp3vTMFBF5ZlfB87fSI2tbfqb71mrqFvmuA+tYglTZp+5yIb1wJxBsEzfEybOYIEvghw6vgQxeaBV3D3KJ/oYtpRx/uEFF+DCb38Tz3nOc44IGAPAO97xDrzlLW/B6Xe/N37/b16F6xx/g3AOaRwAnLrN0aTUUI5ZBsZdRwGLeVYJ+XfKcjh0sMGluuHlRFK33PnMFy6rjieBTN+CG4rlVBqaF52OtlVaR1YvC2aRDEuuH+5rzNZJNzZfXwA6Qooxm3foSp1UwpQyHWCRFZRgkQwxYJFddgvuXf5+aBu5nWSae8FLACwI22ul0Pr0W0P5oCW7L1sCYvwxc5Cb3Ie4nxxs5PvQ8WO/k0A6l+MMscj5M2Ag3GeE9X1nOpN9jn1vaD+ZMUP+lhQAEUzqd//tU3jLc38Tu487Hje85ak4+fZ3wW3v81B/NN6e2FgAAShrpyLQLnUosRyex5JxBfj5wDPeebwNM6fymfd5IrjxfJCnyk+fr78Vf+9SgtHnvZHjF/CB1v6e+ww9OXbzeTUxIvg7sf+QQQEgkVNYl2bjCfU3svNYDzpRpH2dz5UbJNzKUgXDsjNWGOQIcrDQD8SzK0udGEeV/xxlMbHSIAf+yv+ZyCFplEOhIs7RqkCpCRRbVyKVUPKzJMDsuLS800mMkXUugGEZ89JZF4BxZ+yW0qers13tzPF/m8ZzhlZJkB2ApK45L2w80OYiPRu7h7nNWxMT/osKTGWpfNoeigZP3GqCQbQuVm6SVjIHG9R14aPMCfyyQJ62gb+udCGMKcRSIJUvBtyowyEEtdH2cR8Gu8ZVaO0k6JEdyykEW8yftV/1OF8ygDQ/qv+uUC2MQwKOmV329m//q/SguVAtnZ+ZalWgUFzyNh1QzMTmz4BZ99R9FgFsWJQC4CbvgnzucTH2i3MvMI6GSA5w6PgIvzOrzBb64vXGNhqP6VkeITC+4oor8K53vxsP//Xfxq/+4R+Hcs/8LKKLO2ZG6IzDoc1GMMYRmKR6bGI0otsQaRos7dD6ojgzQ1kF5q1B0xgYkUUmcfvn7uzOQmkF53/nv5U/J7N2yeKjJfAghlkXKgR3aS3TYFlop1Ij2jrUNX0uesBzLxgQLDOfhzNaxI3735FkSE1Pn5HnGnJzAwgu7j5w2dfagYUYIKAFCICxhLnjcyfAOWOnc+ZW3rP8Pdxbdp/8GxtmOXBOrr0HAOfvjI2FnB0eMpRyY22oBZANLTwILuuf0aMBRMCas53f+NePAwA29l6Gr33iffjev38Ot77XQ7Lj8HsSsgXx7oLBpeN3obGWWS8Gm8vWyPUlW994H/n8mN2n3/qf5dDn4H1dkJXE++zLMJMDYr6/vpR4crsQg5Rdg6xWSxe5eB/L+gGfb0gOtsyYy2UqebrKIK8U1x4NgbQsOjhntZ/jgiQMYlxqB2sVtAfsxFdw6kj+R+euC+Urg0aPdanLoBS0rgzkGIBAbClloWFggjzThbSlU88Wc4BuZxef2dBzvCbatRYcc7+qtE4mSZY59KawEWmArE1d261xIWKUgXDMbkHbFGJwsTuKhfAxKpjPSf+PRhqjuhCSCh2iwiltEg0CTvlE17f4dwTPKUAORIRnJTnfLluBVOwiHcAMIJ0Ax0A/QAYAi7Kn0xbQahQtRq9r1jDJoOGKeXTGdOaR58sbMdTwkhDaWytifudCckGDL5baleMt6prEVWcTUMKq9k7wi+5A3o+epUo+59sE+UfP/MrvQTLgq7t2AwAOHTq0uENPM8bgPe95Dz7wz/+M0WQFj/2t3xMFK1JQzCm1OuswaygjxaGNBhuzbmGxjEDYJfo/mZdTAmeubMbGZjM3MF2sDLWVm1v5jqzEQqSyRYnZ5+QaBWAuywiS+ffOECi2NmWW+P4Y9FNnEyAvY5KBFCBLjWkH2xuHsFWwYW+fGQAUfcA3BztDx4zH4ucWvwtjQeq2TXrfOdjoCSdYeh/y2obuJwfDC8dwi/vlzzYwhhlTqgsFeGOT+55kkPO2TI6Qs4zO2kQ2JgP4ehllAca4T67sOAoAZW1SWuNuv/yU9D2Kd2Nln9Tpu+u79vw9hkMOSSYyzwL/lsyZg8zwcmAjn93Qtsn66p8XSwpyg0MG9EmWWY7dpA8hfY99sQpstMpsUgt9dcDA65vr5Lo7NB8MPSt/0WEel9eSyFc8qwzOGe0NdhkAzQYUnb8Qa5OC8cAYoCq0hCWkZ9uG37UnrIgUozEVyDMBmK0rPX6g602LTNH/htdnBv1uuXfv6m7XWnDMTXY8bn011/Pa8QAWrF5mzrRWaJp08tRaBUutKigyuescRlVM38NJ4Xm/URWDebQmi261KgJYjZkOYpEBBsLcr6mjQrDAEXTxcfLnIXPZFspC5mWNgIyf3WqiIWJQnAIrORFI0GdD+U/ahgaPvEY+Z6Gcl1+QW8YFXbMOrLJklxv/vij/oUnAL19Pkwy4CIJ7i0UgAmO5z3Za32IQdJeenR1ufF3RwKFnwmwQ7c+GWjkaY33XLnz5y1/G3e9+9y0rSL7xjW/Ee//pn3Cfhz8aj/qN38Zqlv6GQTHfc2cdNmYdNqct5q3BxqwLRqMEeIEBdFE+FMaQAMaSGWQDsc3SwC0wn4VakEPkAVI5SGIwW1Z6ASTztjFhvoOzKmYfYLbFpQsdG610DMJPC4up0HiG58KsFYNkv0h33vhjppAzW8ixMMTELWPZ8v36ZBd9bdCd3mcEssaVz2MWU48F4NxzL7krW4LZfL6V15H3ka0MhiE5Sd9zzY24HBjzNn3n2Qq8yaYzo18aaLxPvh4xWIEF7vaY38bpD30CqtEYVT2G0jpNVSiD2TKCRmu1oE9O5E99Vnl+/eLagPQ9yT7QFyTW17YCycByI442kB9UiA3gQD/axCWsvDw399Ui67OJkYf0/XOTwDhvWlGgf2IkCVwx5BnJGXu5PdC/Zsn51VoHzkDSJ8mIhgLpmjuK6smCh+P9cDpAWgcVrNPgiqAAp3EkQEz3CFhnQm5/raKczFLdWvIA2yp4gpk1zuNr5Hpt3fYN7qu7XWvBsRJSAamZaj3bK5njHBhb69AGK2txMDetNJvpP+nuMc6haVIgLKslNY1FXae15kdVgXEVK5QR+ENgj2OqMjpf6QcCg0TJEDOLGnWs4nJ9Z2c2svWDg5+VdYCcDwhMF/H5OIDqtPewCzzQ/TYA0FoV9Ly5xESeKwLo1oNeE1hfdttwhTZO6zLlkptaBWA7KqLEImWAhwEv95E4KN3CM1vWhgAGv++lbF3yU8rccAnrDhFUVFrhyf+/Z+Nlf/T7eMQjHoETTzxx6bV997vfw23ufDf8zgv/Jl3IMlDMz7XrqNrYxqzFjy68AJ95x+tRjsbouhbf//fP4vQHPQanP+hXoIsyMqNSo18sfieBcf4vb33MlgQu+ffhfhgAz9NCPDkAHjqfZH2ZCW27GIgGIMwJcgEO52EWK2FvvIvcaz11JlGQTFWuP06uCYt9LF9U+/YbApTLwMlWwCVhpKQ0zLmFBUy6veUCKCsaLiv+suz6h0DwwjF6QPHQc2WjrKiKBChvB8z1nnvAgFN9ria+hp7+CKUwWttFsjfEWYLTdvGzKLxBBiwymswqp9eHACqXNWPyZy3+XiL/GWp9koy8bdkPBYPNxFIgdUSAWjypMB6cS4Jn+d1UpUbb2hCom7+LPuDMx8tjmhKjK+sHfYas7Jv5c5EkXUIqSCJPjIm8vLk8t9YqyG5CKykgT2qQQxpMq4CS1nOSY+rAHEvAzN5tgIP9CWPIeCfNHmZXJMWBKNbHJesQpW+LucvpnfwPc3z1tEx7NNJFSC/V1/Fl6inujF3Xb+UlA1cjBPfxBMS/c6Jtqf2ZzSlQgKvVcL7LWgBj0gOnBSQYBEd5hQosMAfdpexvZJUXGUkGZSkIrKTGSQBZGdSXg0wJeuNxeRtAq0WAyi0W8oi/SeAcK+F1CRvMlcD4XcgKhLMilhnl/aPhg/Ae5N90a+K6TD9wo+0XJ5z8mcUv6Fhh34ydWFi8BMPDz4ueTfQEABr79u1DWZY47rjjeq9RtlNPvT3e8ra34ZIffB/H3eikhWvkd9C0NBFtTFtsTFt89r1vx3v++tkLx/vwK5+Ptd3H4OfufmYyHvieeBy1mfG5HRYpL1ogAa18ZjlQZu2x3I6bdGHn5+UAPl2okCQf8H3XL6SHNhtMRqWQDkQ9H/ejkJNWSjOYsfKLDPteylIHxqqPeRpqWzHDveCyb77K9ssX3vx3eZwFL5R4T30ZADrLOVcXr6PDsLa49zqX3OfQ/kNsex/YSa6lh0HO2xDrvmx7bnxcDhhlTfLQOWS/5OvLA0LlPTLr3Ofyz6+F9tn2bWT7bX3/23nHR3K8vtZ2diFH+kImix62N51fFrNjJPur/uMBSOIJWCueBFeKlksugP5nRASdRZ/3RG7Hx+TfpbGUXjD9x4GKZakDgxzWF28gWEvyU57LS0PkXVlEbzat1wSEO6sCk8x/E2GFAJR5jZUYgIGxXNsbs0hOJbdxBHPmVW3XYnBM/8lFT9toWefuCwAhvRr/zU12RHZnyejSLtfusCVa6FDogKUY1jqUHoWWvoBHWWiMy8IH4FF982ihIWOBU7DF4y933/cB4sX78Qu3jSyyzjS3xPIAjYnbyOOknV1+n36X75M/V/k9XQcCQLYuLY0r3xlAmQF4gMtKQF0GjqU2kluiHXPDScYZzORs1jLmLnGteQaRdlo8f7rgCTDIOWQ9w9NZg5ve/o44as+xeNnLXoZHP/rRuNGNbrRwDZdccgne+ta34rOf/SyMMfi1e94Br/vC+Tj62OOS98blVAFgOu/oX9Phku+cv3DMoqxw0m3vjJve4R7hHUj2Kg+AWhhf7eIqnAMUybDJY/HffcZJHzCWWuP8XPJ8rAntYFHXxeIiqChItyp10DHG/dN3HTSQGrBtXHDleblPsjGXl8zdqg0Cvh6GdOgzsCgZW3bM8DnXZPL2PQbglQXt4fqsO2LNdH79Q59zRq/f4Fqc07e65rzlUgn5NweH5uftI25yI3qIPVuYi2TXEh7O3DjIzz90H8u263vG8n9rl0uH8muXZej7PsvvloHRrZjGhNSS0oy8gJBg5OV+QEzn2Nev+s/VP77472VjM99vQd8szpGsPfI5et3xPOCUdA7WmjztIc1szcemynpaEdlsHIKsPco8WYYR19UyewcSGyRrkV0ExrRm+/Vxi3d5dbdrLzjOWl8lH/l/ZCJjKdIFDfLAQJZaOj4PZ7AA4gALxRI8eAusdlWg0iqwwEFfqnUoAZ03yRTTNfT/loLnoRZZ6KHMCgSM00A3/m1IijAUbWrdouRBul2DftjGXNPS3TQ4+TiHQxtNMjFvFaXctFsPulzHtx32ylontKXx777jBo8F3AIITxttd8qpd8KT/ujP8aKznozPfe5zuO1tb4vf+Z3fCWWkX/HKV+KfP/AB2sP3y2OuezzKeoJZGxl3maWgM5bySM47WAvc938/E0dd/ySccNufh+06NNMNHHfyLVD7XOU5U5OwguK4wQhlICq2k+Wlc7a4b8LPgbQuSGPcxwSGZ9wDpvPrdV5Q3DQmFN/Jn39etEECfa0FyMn0i1ZHZhmI7FRjTfKsomu4v+XPKXy/hWwgXP+CNCE99tB+y64nbzqbk7azj1y0FyqQLbnX7R5/qCUgZavKeNt8PkPzSF+/dNYG7XFeZW8IaMrfZaXHoM8Xhl2Rjanw7AYA19J5rcco3Wr/I2GM+1q+xvaREMu2kYb6EGss/6e/F69jYdxJ76ogNHjbZN8+b0bmRekDw/lcuqzfyfUtrJE2mxeSIkFxftWajH0bUv/5/uJUKBaiVU7+FYB2nuElaQWlkXXh4ZQ6YiKtUg8xEMk4iRtCoSnrFuYqrYnYZC33z6pda8HxSgikSa20oRyb3DGlnjgXtucDpRKLe7DWhP6IA/+0jsVCAArYk8CNrTEGmhwgVxfRJRHGphzgXvKwFRAeclPE/ZyXIbgFKy+ehzN5LO6fd/D8eyDVt+bXJSUPEgT3RZsDCGnCGDjnCemHgAq5eGMQRn7chfvuA1kiUn8Zq5kv7n1sTV/1LDqFsPLFNcrj3fT2dwx/f/WrX8X/+l//C6eeeirue9/7BmD8yEc+Eo961KNw3nnn4S/+4i/wqfe9C/d4xON7r4XzGBs/Lqp6hDs99PFhkpMaYvk/Z3tYuk1rYTqTLlSh7+uFZzf07OWkzq3w4zxP1zi0oBRAr9vctIa0ptbBqsV9+X3mIKDKrp+BS3ivGQPVIXv2Xp4BEw3r/Bn1abv72gKgT4Dz8LbLmKxl+ZLzNsT+LduGj7kMAFwZULxddnSZhGI7/Wm75+w7htYqnDePoWJmue868nmG5zYOmJXbD4Hk3DDf6v7yqnnb2QfoWYsGznkkmRq2cw0J6WC9ZlWCqyVad25bAei+9I5DxxwKAs8B8XbGYt88lMgVe3BLuA+Ri73v/sL5lUvSvfG8OG9NDNZTTGpRdi2WXMRaAWmQOSDWcJcSbZI0Y0wTAPNC4ZOfTbvWguOpB7lhQfWWSqFVsghJKcXQQJGui2WDUXbQprHBipMtd+3LBVCCYKprHquvsR5ZqwiKgWih9TG+4dpEJ8ybdHXxcRfBIp9r8TjS4pNyCm5GdPI+djm5ziWLsmTfeVsGxkMtt+r7JhWto9yBfy+yCTRnNofOeaSLqQR7ObPDrrDAgAjvhNYKO3YfjXO+8E285Hd+Df/5pc/DWosvf/nL+PKXvwwAOP744/HYxz4WRVHgzne+M8444wy89s/+EOd/4XM480lPxUm3uk24jqY1mDcmGIn5BC0rReZuvF5WS/zetba32h2wKCnoc/nmiyn/LUHx0LXkLRzbu7O5st7SffIxY9P4BC7o0yfZoR/ifsMtGn0y4C/0DckSbgMYb4clzj9vBYyH9pMtv/8rA3aSc/Qxbz39o69tB/RtFxhvtx0pqzUEpmVZ6mUV9xaMKJ0Ghy09N3tG+4z2AQB7JM9k2bs70md7JCAeGDbcir75dumJ+77kZy3ySWdSxHjd2zjFACiW3/VW3tumgUzXmoFfjbRwiTQknKPf/P8ymwUA1DqmVZXedRpKLF+UGCJbHzJyjLFBkLOG58DXG7ftM7avyaacuxI+j//G7eyzz8bZZ5+NWdPiwgu+h7/7zDcwWlmDtc5X92IGNLp7ObsEfw+kbuM+C40nIJnyhFtdFZjNO7LoVZpaZTwqMao0xqOSKuLVBdZXKqwEaUXMbQwA41KkexPfR41OankxwJZAu0//O2TR5b/xseR54zlpG2aV27xji+tqMje0fLZS69vHrsjt+J3x+wy/SUC2pKhBH0vJLdeBJmBMD+vagEVtXN6k7lne19DffS5AqY8Ok1nX4QOvezne86qXYdPnPr7tbW+LP/iDP8Da2lo4Rtu2+OhHP4pXvOIVAIDJ6hrGa+t4wrOfjxuf+gu+cmPMKCGfITMIrC+Wrr7k/Yh3yAu7ac3C7/yMh961bAwUZKleqoYXn+Ns2oWcydVo0d7Pj9t1sdIS78etqIokToGvnfcDkGpsZSq4jOnP39vQNS0L9lnm9j2SRXkrUJxvMzQelxnYQ2277NqV0RMfye85Y7wdULxsPpKft9Pk+FjoL3p5FgsAiY5+u3OGbMVQv8rmpsHr3yZUWEYybWe7ra4hZjbaug2NLclU9/099Kzy4/Z5vPL33Pc/N55n+zT2S43hgXHb9zm/PsYllZ/neK7SOuqPS487ylInwcO8NnJhI9qGg/Dot0rHeSnP6sOt7Zt/BKZgb3KODzYOHMBT7npz3PSmN0VRFDjrrLNw1llnDd7vVWnXOnDM7d8vvASnnXQ8Xv65b6Iaryba1U6AZM5dLMHXVguDHBhVzn4JnTJ3PHZFAASOJyPKnyrB8bgsQuEPjvbMAStpidNrsQ6hBHLehuQUfU3eWyBOXTy3zCIhGWLrFlOC8d/0DBGArFxQcjagj7ni5xcYzM4m2/a9T25bubHiObaufpZ8vw0JRt/npbkxB/ZdBrbyyfnwgf34yJvPwYffdA4O7L0Ca+vruMENboB73fOeuPnNb46qqvDJT34Sb3/725NzPeYZz8NpD3p0qNzIEpUh1kL+Jr9PthHAg9lZCY57FxvxXR9I6Auw6ztvot3016G0DkGwch9ZnY9bURYLqd+spdzMQ41d4JyWUT4jnY1j/q7PSBtKwRT2G1iQl7Vli+yRyCjk9suubeH77NxbXcuytl3gO/S7NGqGgPEy5u3KAOKha8mPPXROKa84EoN66HiybcXE9XmFttuuTKq9q6ttx+hOft/GnLxdsHwk9zOENXpZ+wF5Sk5KbHUfcm3JAfLC777ir9YxRqrKCCQGyfR7ih+G+mMerxTvKd6b3E4+o42DB/Cbd7sFDhw4gB1Zzv6ru11rwfFXLrwEp590PF72qa+jmqwuaFNZTsF6FmbDlgFj/o4ZxipjmBjATOddSNGWM8crE2KMmTkuS43VcYVxrTEpiwQYMyjlY8uONCRoz4Ftfg+yyWPEbRZZZfmbER07PJfM4pMaIbb6pPGxcNweoMCtyyOBHWmhGBQT8O6P2OX95HGS++9lYWiQ9rF0+bZ9bWEyUv1sTzjuwMLWxyzlx8v31Vqhmc/wtc9+Ej/6/nfxnfO+jHM//QlYP+tUoxFuc5d7YGV9B4x1uP5Nb4k7PORxaLqYMkiywomcaEB7rLVaYFQZDPexyvk9WhurVobKdYVa2IafR74QLOQ/zoqFOBsNp9GkCttZ4xIddN5nGJRI5jvsy/1ZFCrh62NwLZnxIVAz1JYxgNthtoaYta3ATa/7duA4y+7jSNItXRU9MTAMtuV2fZkolgGKoWP3gZat3udW17/VMfr65LL5ZBlw2862221Hyvpemf4otxsyKPuafG9DXosjGYdy26EUcX3nWNa2khwNGZDL+tMQSZC3wB4zztAi/klU8WSAXHgGWTLMhdiXwXMltqFjDN3b8nsEho2rzUMH8dS73vxnAo6vtZpjZyPo1Zl1xYCKW75455YL/x4WaOGCygdaI7TOfH5+0RWXmvaVtlqfPorB5dyQuB2QTDFfl8uAr78/KwPyVAKKpQtqaBHKJRcm6IewcCz5v/ydc/lKF5EsKcz7LQxim2ZMkL/x3/l+XDggZzi5aa16JTE8oBNgwcF1A8F5chHiIMC+SToJ0rDp/kmVJrt4bG6cqYKvYRBMOocFLZyokFVWI5x+nzNxuv/p4N7LccmFF2Dj0CGccrvTMV5bR9tZzETaNplcPmeM5UIjMyssMPU9IFJ+HgIx/H1R+lKjWVoh/nsrYMzyCM4AIIEt78uV+aq6XDgHv9MYeJQW8cnvTWarSMGSoQwaddlbJng7gED2MdumoL0zfM7tA55lZaqXgUtgEWjnz6IXfGQetr52pEzx0DXmQYxstMjyz9u5T259/Uwef+jz0HUvayFzwRIwk167TfqCZJXza0uub0itcWXzG/ewmMuavJbtGAu95+wz3Abez7J+s9U4TI7D93kEzymXR/S1rYxPud1272vh85IMLP2VQVU672VBe9Y6yGA++jF+DtU/tULh1zvTZka8mEuWzUfyfnIMlhRgu4bbtRcc+/85+j4J5MoeeO6mTwLOsslXtpzB6YwN+sxwrl5gbpOMFqOqEMUiLFqrMCrSXIEpAE4HVwS2NugQCxULhvTpkRf3jWBbHjv+3TcpRgDbClZegiwegLkkInne4v0EhlgA4fyYU6/nzlOGDTUe7AtASAD4nI2lCxw4FoaBLlcN0/lEAqAv72gy4flnFYByT4YBrRWWBnnpdGJc2XkUbnq7owODP28NmsbQ/5ke2DqXGBeFViHgTD7/pLqkz0QhrzF/LvlzXzQuhllz2R+SZ5EFUxVlEf521qLtBOD2D4qv21lLmSmy2b7vOhls9TVmnpP7KAt0rYW1baJdNkjlIn2gpu88eYEIubAl5w0Xtfg8u4H1JPdq9bWtgMsyFu5IXczb/b6vL7AnIP/9SICx3H7Lcy4x+voAWP4OF45v0r6Xp3bLi4WUpQasgenSc8rUcEPvJVyzrHCYGTKDZMoSwDbUlr3bIzUyjsQLcyTM+JVh0YeMqL5CH8l+PXKmoXvt83guPfYWaQkHj+OnwoXUcFYB2oXcz2WpQxYeK+boqkiz9kRycNuXE66/77n0Petrul17wTED3s4u5ApMorczViRnKuUiGV58PpHYRWtIAuMF8G1VSIE1bw3KkmucF9F9zZabirpbqeeJ15/eSyw1TB066f+KXCQSEEvQm8sj5LMasnb7SgMn25i4XW6MyOfMEpf8HSzk0/W/2XZ7LBAf34qKXJItDpkA3GLARP6+eeIDMg1aKJcpmwrZB9o2puvK5RnJpGzTBUOyyRII5yBZ5jldNnlQ0J0JfyfGRyab4FLr4flJA6+HmZPXBixmosj3z1sRjIrhxS8cQ+wvA/bkZ91jOUgAwXrjML5NvC6ZOznNEZped1EW4NjttiFWWlsHCz/vtDYED5KBJpjsQTovNs6FmzdjF+22XJLS27+ydqSlf/sMn2XfD+17JOfJP+dG0VD/uyqtD+zmxtqycw39HgzDTBefb9P3PZB6K/I825EJdMGDgiydaX6O5Jp7MqJcWVB7JG1LUuNqPPZ2gfjCvDPgKe4bYwt990oGzi306y3kY7JtlYEHACx0YmxZS/I2DrpmiQT9TvIK4xzgf2ePKG8jn0tf9c+tPC+DhmTGNHfbuLerq11rwfECSEMEYX3WiOkBxkB/fse89Wn1evMpqzQLgHTHV76iW1nohc5Fk17cNk+/lv/NJZtzYKyVCnlWczkFEOUR8tkN6RBl4KHMAJLce89EIb+Xyc/7LGQz8A7zdiSs08K9MCMjXJsLA1cAaGDROMqlGnwMCaq56l1fuq5wTsEIS+DWxyYHhklOzD1J0mU/5mwtneG/h9/3PJNJSLCYAxPJbMnzLrz/Pma053qXMXdD6bd4P/l9X9ERIGV8+5i/zmftkGWp8+ayfcqSgYgFLKBFOq5eJs+mWTK2lVZuKQjMwJIwBHIN99XRtgOUl+0rDddlTGuuY++7hq3O2TtnCL24vIf8Xvq8H9zyfYbAcN91hD4xJKfoKbmdHzvfno8Zsu14D5tsbBgOSTL6jt17fUsA8lb7SwN62f0tG6P5uXJiY+hatmPIhd+HAsp6wNtW2/X9tqxfcsuN/ny77QDhvPUGPAtvAqd5YzY5WWs8iwyQhJC/Zza5zzMIIEgtgGESp+9Z9lUA/Fm1ay04lo+QO6LUGTNIbYW7P2G3tvkShoAxgOSY/FkyoWWpAwgBADt3icgdiCBLdrZcx5Pf9aKVRiB4K8u1Dwz3SSXye8rBcJ+RAfTnX82PFQwV63pBcd+7MZkOKQGc22jM4kkNILDo+u5jNSUI5sb5NANYzQBtDpT9RcRJRTDCMlk//y+r6OWLQt8CwaCYgiVtYIzZbScNNtbLMYuclFEXwJiNxnDeQgU2q49l420SneU22E35u7XRkGEwmQds5vuxtCLPmMHH6zs/X5sVx8m31ZVe2I8X+9xw6ZtL8n2HFkH527KWg8nYd82CEaW1Cnmir2pbBlyWtaRfDRhR0gjr23a75x1yNedp+GT/2C4zLoFAHxju6wfcj/Nry8+9DIAuvd/seuVxpPRIei8kQbAV8M3n9iO5Rq2iQcAyLvlbsq006pYEmMnf5bGXnXtom8FrGTDY87ga/r6vLTOUZOsb70cyBwy1vA9I9li+88aakJ2CEwtI8BuOpd3iupV5mvO+z0TPkdzL/4Djq7v5Z9h1DsrEaPxeILwNYJxMcs4llYj4ewYZJXTvosHWGCfW5uMUKkb3J51SMI+yU3YGC8xy0pbofJYF5yxITLJJUOYVTu4Ji4BYssLyufcF+JjsXSTHFefidFp9z3XZotYLbsR2fayU1mrBpW17WDhpYcf9RPEOCZKB4clDkxHD2q5wfAGwBxdw8b6lhS5T3fF+UkrBgLn0XotEFuRckoWCWS4LAqULQMC4wKYmMRvLALL8HjboJXOmlf9fqIIHjapOZRVAmj4OdjFdW2LYLGEGOf1bDn7y4EG5zdBn2frOBQyDOAayXN0vf/7LABr/vvAMQpn7xUwhfSnxtgInffe35XZbuIS3WiDldeUgk/uiZP61Vkm/WnbN+TOVbTtsMv89ZJCFbbYBAIeeZ3KugX2bxizMf+GzkPnwXMcBrX330vd37+dtZivZDgA9kpa8hz7QmoHhK5Ofe+n2fDxZYnobY0HOiUBqIG8HFPdJyfrOm68buYEUrkcvCfxO5H1x3QrrWua9DAQPx0wNBBIPtYU4pyOIY7iq7VoLjld9RLpxDlXP733Vn4aAcd5yt0ChFRqZM1WQMlsxESg8o+o7F1ttHJxVZFZbYqXx8XI90rIo8b5B05OVQwJaYGupRN/x+wyS+YBCP5dcMAuZs0dbWZF9i8CydzC0sPUBKp5MeoOpsshmubjKEq55EIzcri+FXLgWEWi3LDVTbvjkwDhPs8dtYb9c671EZyz1j4CXHFzVhc7Ed5YDlQQYGgZBKSCWrW+R01olUoY+cMnb1KNFlnVLtlstHi8sEFeBfdOFAooC1jgUuh9MLmOs821DiXtENglYnFOGALKcL7bbthM01AuGe4BlHyi21kKLgZQbOfJY8tq71i7kuA7nEv2Qg4vlNtJwS4iFAc36EbGtff13IEvFkCEoW37tQL/HIt9n2fHkb0cKQvPjJMfquZ9lxkjvPgPBhlKKuOx6tsOi58e+utp25FbA1iB6u/0tuR8BesMaJjydct1iuUVV6IX3kycQuDItjq0rtfuVatdacHxo1gKgh9mXy1guWMmk1qNFkwtyXyebtwbN3CydhOVAo0hj+r1pDTqjQjWaqlDoQMF6EAxxWWoK6NJqy9RjeeubNOR3EgQDCMdPArJ65BVDhTY4sA6Iz7cVgZGLjOPyvKN9DFsKKgV4Kobd2csqnOXHWdZk8FbOcErLOTKkwtAZOEcEGRAaZ/q/yEG0P0dyH5nBlhsbfbmguc2zYhhJFhBRSU7umy/+gTVmecAShmAZeywbB5715XftZ/VilpitWtyvPx/x8PZYyB7D7za9dv/u5H4QwLtQsHpxblhmoMlrie8g3nOfDnEZCO8nBRbThW3VesfsFgCYi7P0SSe29XkJKNZao+AiBZV4PvkcDJoTutZiPCkxnpQLRmEf+ysraHIL2XgGJFl8rKF72goAD0pDsnl86Lh91y+DWbfKi7udxiz1wrmx/D6Tc6n+uZvbdgyMLc+RlzTehkExeKwcrIu54UgMxr62FeDl8dO33XbvQcq58owodCAsAGTJLMd1ic43txacGxk4smeQz019fXer7DpXZ7vWguMw1QvwJ4GxTO2WL/R97lJ+Wbm7RisqX9u7re6f3EKQn0+7xEF45MqWOrhYva0zduH88pz530NNBo4t6IszFijJGhGeF7GbUi+cM2MBPNvUpZ+zwbztEGjLn5tssuJZPrHnTGa+zTL3ZR94TdhEpNpQKQOQzy8Hb33HDouGyLec6svZTed4h+R6kmMNSGLyLCByGwALKfg4xSC3Pq1uOIYH3JRjWMNKeUpngDIyrgv67Sx4b2nzKasYrA0FToZjb2McbAcI5+Ax7MOacM+kWOOSlEX8DnNANsho6X52jbNm9Oon+8CUAPx8fSzF0EsW0GVARPb3pfl4l4DhoUX+SDXFvSnQBDDmxlIKOSYZEDLz28zJICwrjfGElkHJCPc9E943Z/6HMglIUkUeI7/+5HgS9EsDfAuwncxRA31DXjMXq2H9fn4+oM8jZJcaS8vGZe9326hMd1XbVobvdgzjoX16fxsAxn0AvO+dH0mA3ZUJxgO29hIA2fX6ID2WC0qA3B9cp5Zmwhk6vcnGUf4ZwBHm17lq7doLjgNAiwFHg2nHMiYRWAS6siV5kI0NeVObuYEuFOqq8FKL9FWyWz7XVHawsELsLqvrSaAsU4GFazSLfw81rRSahaC4rLsZAW69PnUBwPZohocAcnjWmVu+7//ttDyoi/ffanLre8fckrRIxeIi03t9wnVPjHF/6jL+PYBlFws5SNYxt5RTK5omlMDUL5kTh95dH8BizwAH3/GEJ9+ZNGyGgFFRFimzzIusdTGorVgECvn2Q+8xBZqLMoit8gf3taUL3DZYq63agtFqFvtT8s5FMG3OnvVJNIB4rxzYlAPLxKPRw+SH93QEVQ6OJI/q0uNsAYiHztP3DpRW0ND0v7/nxHjmeTbT0JcVVShl9liOeznP5H2XgTVnM8n/531ykmQ7xlzfNkMAXB7fWjdYlVF+DuOoiwYnGbhxW/l9bogvpExcQiTJ75aVPu8rY30kFeS2Y+geye9Dx+6du4QHaRljnBvLRwqItzP3DN0rM8zbAsVSxy/O2cH2EnPhnoXkr+/59RkHQ/ew7F7bbaRwvbratRYc82LTdhYQ1dTkwp+3oRr28nNeeCJfrPL8rgASYCgjRfsCj3ghy4Ey0D/BxH2391xkk/pTeU/h70xP3KvTHgigS44jpBPLJg65/VDLgXG+/xCDkvy+5N0PTQ7y2L0D2INlZ9XCwjF0z32/ScmEloF5kjXOZBfJ/hmjnxsyfN48+E4CCGvdQgaQvmwUfB6XvXMGFl3IlKAX3ld+77lLTwITqQUN95VMwGkaM/l/fp58u/y5XF2Nn14fcE/mE9b8ZvNMvh1Ac0GffIPdxFaJ59kj2VgAzwxsilRTfVUB8LLFvnd+uBKAmM5D3yutEmAsnzOzxBLwliLTSNOY0K/ZwHNaL4BFPj4BSdPbT8nQpWfZtTZcX1GmOWXlGiEZbb7XHMzn98T/9811Q/14qJhE19lBj2kOvuVv8p7zewrbqcU+3df6JEhbtSMdr/kYX3Yu/i2vdrcwhjIt7VZscV8/7ktJuWx7bkcC6nPsIVsfYB5cA72XLI+VKUTfWwigG5pLBqaIZQYIQGTnz6pde8GxX506Y6F6JBSybZUHtI+RkZPjQg5CP6jCxNKjr4TPcypBcsI0eKZJZqYYGtw5eN6qLbNwh2qa9+238H9mDS/TFPZ9t51FmRe4IUCz1Xm2ssC3wzxudQxpAIVr7gHVCRDue3dZSrfF6xi+tqF3zKkLk9NIDa2OILjPmHRWsMGBDSGtp/WDThowBEYAdPGGcoASrr3PbS7Yaz5n3zb5veYAPDEolyTQH2pXVpOpdTw/29Wc9SSAIA/+uaKeDBSS47yvNO1QXwyFg46QoUlAs/x+G89qCBT3AuFtjv3B+9MqpNNL5mMxL0jpBJ8jKfBiHdo59dmui3pZ/nvIOLbGLYDNkvt20yWkB42LFEAzywvETCR5FpKiLFDVcf4MmuaBZynPmX9Orj17/tLgHZKthOetyfDvG7v5Pvn46wt+GwqIG3ru+XdD19nX+r7P17p8rPH/EiwnBkkusewz+rO1cbtgeKv72er37bZlKSSHziflFFqncovttmU69Pw4Uv45/5/y0Vdf6zoL1RN5v1UbYp7ySYYbJ1YniYQLFWaYsQgLYz5xeJDMKXSk1bbABgjX7BBo5tY30PM2aAVvZ3v598DA70uR1teSZ70koMUal0ggcuaSG7s2Tfbe+wo6bAXQhrSx25m02EDoe6f8d987zLVdgHg/yyQVAhDnbMxCMvUBd2DX2YX+CgD7f/JDfO+LH8Ghy3+M4065PW5yxwcETXHlM8NYm7qn+b5ovBhYa0nrqFUi0+h7B3m/YZe1tf0ZCeS7ZQAvtag8pWrPCtoeIKcHFgh53r7z9QHvcC8ZuPA3CNMh6D55H5ZlBSbZpV4kYNF7s4whlwWC8usc2ndw3rsSBsUgEF4CjPP5sW9sO9+/mdGV1x3ytopsJ5LFlcfk/sqtL1ZBXpOcb/I+3nXWG4Ap61pWgBbCFZ4Lus4GgDyftmE/rVWQKTGrmxMCfeOrr3WtDesPj7eh4ywcM5uzqlHZ23+Svp8ZZMym9hq0vk+ynEs+/wUW3ab7hOeoUvC/VZN9vG9NHCKGeo3IgWB0+Wz75rWcMMqNFIDWre2sVX0SQP77SNddKbvom8MWgL+Pk8if0ZUB68vwybLUrtd0u9aB47PPPhtnn302NmYNgEXLLW9Hmluy7/scFGpNZaFHVdE7iBIrzIoJpAcoy2tMrFatFgO6RJO61uTcSzTHQ38n2/eJ5DMQfCTPjr+XEy0zjkPb5wsmN6UVuUZtBDpDFc7ya1oGcML3SxnC9Lehcr4LoCQDvfxbbo0vgOTs+nmfvMnvQn9bklqnrgtyRXcNfvyd/8DeS36A//rav+LCr34W8Nt+70sfwtpR18X1Trn94jkGjInA7jZdAADyt76AyRSM2PBeJbANLF8mERp6V12XstvynNZuj5WQCzZAbnhu0gWfHjtlv7Xvq0MyHDkmhioz8rZbLUh9OV/7mPWtFp78mQ2xYIOgWLprl0mnBoze/DsJjJnN7DqLdk6GGIPkZayjZIAl6M2vVfbPzmd3yd9TWRYoKx3HQ6k9KJXvXYM9KF1nAyPMemHSP7fJNbVNBFP8nmQVxxzY8nMpyzR4VY4dJ+bHPCd06BOFQl0XKKoi3OPCXC36nwxyLz05JINXO2vDfcq1TxoKeX9m2REHCi9jmuU1SZkD77+MRNpK4xzO1TP/9vX33Ivat+bn/VMa87on/7C8RqX7Afiy1mdIDMVr9L3fhXtAT5KCIwSvkqSRn/Pjyb4CAKeffjqKosBZZ52Fs84664jOud2mnPsZZlX+GbaPnfd93O/2N8ZvvOYzGK2sbbn9QjT9EotUvjBmjPuKEFSjEnVVoDOke5Natb6WTzz8/7KAoy0XxqyTJ2A8ux9gGPz23f9W3+W/LVjSYp8h1iZPESabnOz7WMStGMa+a9pKVjHUL/omnNyt2XucgVR8uSZu2bM7kuCVocpRm9M5fvTNr+KnP7gYF33tY/jh1z7fe6zx6k6ccPqZuPMjz0r6JYMR2d94Ae9bTMtKoygLOOtC2q38WuU+eSq5fJv8fiXj1tfyNFl9/XdobOXBWkPba617GeYhzxABm36jqo+l6722HpA7VHwnbwv79cwLQ0U65OeckNjOgpmDXjl25RgvPLiSUgl5fmcdus4E6UMOHnmfPpAus60sM+RNu0iGqIIBMmuINaoRV2eMc25VE4Cu6jJJM8eAHoBfK+zCc5TXmZ4/ynSKPu2vTouf9B1XykCKUodgRZZRlNkYlX2xaWhs1nWRgJxexhFY0DlL4M371Cw3EccZ0jAPnUc2yVDnYyEPeN2OFzV5jjZNoyhT5OXgmeca7u/SWO9bJ/m7ISkLzzFDc0vcbnEeWkbgbKf1YZUr07aai2R/nW8exit/7Rdw4MAB7Nix40qfczvtWsccLzSfBzCvOtNXqzxnvfosJv6fv+tL0s2f23mHrrWoRzQh6kKhnXdLO0Nfp9BaBlLFfSS73NdkEJX8jo+9nYjZI7UEl+3XJ4kI15U968SK9rlL80VBAl+5rekpNMILLrvnhwbzAovZM6n0XbdMMTa03xC4GQLFV2XCAXrASw8wNp3Blz7wVnzln16Pjf2XJr+t77khTjj1DDgozA5cihvc5h445oRboaqrXmAMINEr5/eQswDIjEVp1ABSa2ZFGWgHSiWYApl8oZfZCsL5JRAaYBTza5Tf9bIp2TET3aeoQKa1FvcjAG/uFRLeIwBBnyzPqXVk5IbuMWdili34fXNOfrxluZOT7wQgHvLw5AYrt3yMcnBbVZbJ/fdpiFk6wfNADuyTNINAkEDI1nUWbmA+7ToL25ree1IeHCutYUeFf5cGjfeSxPdGTHIExXR/pqM8+W0TgVLuSYnzjEJZxrJWATiK7Zu5SVhj+f0QkKx9UKYSxyqqYjCnc84kA/3V+Ph/ZvW5yfEZAGCZssNBk8zss3MLxlCYX2xkruuqWOjHzDxbl7rqtV7MuDAE5PtIoiEGmH5fZPytcTDGomta6KIU+6bb5k0aQEoaYYVDWRaw6J9rASy8J6A/28iQMbasyXtbBsiH+t2ggZ2RG+HZb9O7d3W0ay043rmS1sUbArDJIj+gdx1k/bSC1Q7Wphot+UKdtZhP6VzVqCSdYQ9DkS/MsuXsW/xumEEjtm3x+7YnQ0W+37Lv+5gpec95G2Lv5HWZzqLtZGq0PMBxWfR7rmteXHgZdKmBwSqvVV5b33vPjSq5cOX3loPhPhC8HcDM1xv+zlJ3ySCtwffXA4yvuPhC/Os7X4VvffaDuMc97oGyvA0+9rGP4fa/+DSccOoZqCcrC4we54Tlz070i3AuM6xp5O0SQ4kD9JACbtnyXLZkMIWtF/pYnp6v7xrytgz0yUBE/r7PAGJJECDyFHugnC8iTusAlGUBoXTRFym3lrDKC/eyJCPoVkwN3wfdd3/6xeS7DFQMgVS+VjZ0c8M2SoskkUH6YpkiTZ6TvRN9XjnT2SCB6LuHvsVaFQpOAB3TGZhZBMXc53Wl0WweQrO5H6aZodATVPUOVOtrKFdonkelsbJae7kXXYvWGqNJGbwmmxtNwhrz9dgmXqPWCmVVoKoLmiubFOQasV9Ras+e26RUr2SiW7HO5Ux2yn6ngJYz2EjDsa+SZv5MkxR3Our3JcPP308PXoFvfertKKoa17v5HXDdm9wGVV1hNCkxWa1hbZSgtHOycDjjiOlMMD5CUS47w8qOHRiv1As66r6+KeeNXBu8zAjLPRXJei6etzUO3/rUW/D1j52DU+7+WNzsXk8Mz1AaZvwdv3+Vjfmy1CirIngKtFZeKpN6LSV2GNQUD6zdWwXrycDrvkwnfeNsmWHOx2LG3WQG7FUli460XWvB8f5NXyFvQMMSJhavWdyOGH3ouwiSM3BdFMmEwgO3awGN5Qnn8za0oPVN8EMW5FbH2+45w9/bsDCHIqBz61oulH1guM8S5r+3GjRDgVR9rEsvsNX9uXS3ww7z5xwMb6eyoWxDk/l2vsvbRd84D194x9/jB1/7IlZW1/Dbv/3buNvd7oZ3vOMdAIBqvIp6sgIgBcDSZSwbB9m1Dc1mEoRsd0KjfXqYGWYQbFp9i92ROZvYtTYEIoXjmMVJertN9p0+bWYvmzwwrvPnInXUfM35QpMeIGa4yY97ZRaOrYzcrfKS90ka6G8BKjJGnw0F+t0sXLd0E8sAMnl9fZIK+ZkWVrsA1JaB4nDP4pjttIWdUZ92zuHwvh/gih9+BQd+/A1sXPFDzDcPLOw/muzAUSecjpPv+jis7zku00UTwK3qEvNpu6iPXmaAiGsnVl2FgGMG3WWl0XpQKI25BV0yM8MllUbn8RvZ6eitkGDlm5/9AI66/k2w+3onL4DfvA0ZRznbz/ewuXEY3/vX9+Hbn3oT1QkoCnzjE2/C2tHXw8l3exxu+vNnYDypMRpXCevMx7DWYvPQJn5w7idw8de/gHa+ifnBS3Hw0v/CZH03bnzH++O2Zz4Zk/UdS6VQcs7pWgtrLLpmDocy9iu/TdN0C2OdDCuDZvNyuG4K2/nYJ9NhfngfpgevwA/P/TAA4NuffjN23+hUrB97k7CvfKbOOO+RUNBeZiIZ+/AMtAoumQDvAABiFElEQVQl7iUYlv/yd8/voq9th0jsa1vNe0PfDRniy/b5WbRrLTh2wnXSt1j3PejB6Pke4JnvLxOwJ79VBaxxKDS9/HZOWmRrFZRJC2MssG09Uo+89QL1Hgttu21owTzSaHW5WMbvtscA5+5pbsvcNkPnyfXGQxNFftytAPHCpCOAcJW5B3sZxr7+t8SoWTbB5JrjIYB88LIf45Ov+yt85wsfxQknnoinP/3pOPnkk/EP//AmvOxlLwvbHb78osD2yHfYNiYEz9SjMtmm9cydZI1z4Mr5ZiXzxI0Zm/GkCowSENkWZlLyfKoy+p63T9J19TBZQ6z2Vs9PnieXfuQBMn33GI5f5McnkOyERnlI5kX/82IimEEsLl5HOmaXBdb2AWIGJXKbZTrjhblKPIf4vl1gwfLCJRzF38z7XasB0PTM+VsZEAxMZgfnUIWCbS1p2AqNZnM/vvWJl+GKi87DysoqbnWrW+LGdz0T17/+9XH00UdjNBphY2MD+/btww9/+EP84z/+Iw5d9n3c+sG/h8nqLby+XmN1Qt7M6UaD+bRLQLH1YCh8bjlvun8ulcZ82gUWUYIkWnssZlMaX83cl9EWXjtrLepRSTmXtcJokoJMyRg7a3HoUIvWy0KaeYtvfvo9OO+faI447VHPwbE3uROKsoRpLQo/3liLXfjjtnODystMAMC0FqMJgUz+DbbDNz/9Tnz/X98G085x5pkPwOMe9zisrKzgG9/4Bt797vfgy+99Eb7zyTfi+rd+KNauc0MAGsWoxHjcYn7oChy+4ifY2HsxLvnmv6GdT3Gzm98cu/fswo4b3xw3u9kv4Yc//CE++MF349uffS923/DnoGCwetR1cf2fuweOusHNMF5dIya7pf5sugY/+s/P4PtfeBcO/PQHsKbDZP0ojNePRTnehXa6H7NDPwWsRTGaYLzjGIzXjwEUcPgnF+DQFZdgKLi3qmscc8we/Mlf/RVe/vevwHnvfC6ud8ovYu3oG8C6FrZrYNsOKAFrpzDNDNAKUIDzxyzqGuVojNHqDqzs3oPrnnxbaD1GPdIBJLNeXI4N2Y5EOhHGSLZ+b6eoCNBvIPUdU47boTE7lCXkmmjX2oC8D//7BXjAaSfjyWd/AuPV9eS3ZDEV/w+Vpt2KcRoGaQKgZ9ZR3rFcNlGGY2RSj6XM0hbXs+welh1rqHPn30sgtRX723ce3iZ3f0m98XbfQV+2iiGgEs+TsidDv+egd1mhlivrCuoFJtvIVNG3rbUOl190Ed75x09AXSg8/nGPxT3ucQ+8853vxDvf+U5UKztw83s9Dut7boDd17sxxutHJeeRKfEmqxVp3HwarDzQJ2f6wgIuAHL+XPpYqKoukt+26rd8nqTwgj9HDqxz4CoB1LBOtt9oDWA/+27LfjdgpHF/5zakj++bA/rmrPz3rYyuvrknfy75WJQ686HnJ+8HWHS197VlQUjpNaeZKQLDJ4Bhcr1eW0zbGfq769DNZ0Bb4tDlF+DH3/oYLv/Bv8JaA2taKKX/n/buPD6q6m78+OfOPtn3lSQQIKyyLwIigihQ9yKorVZxqz5Y6+NCbStCtbbU6lP7WFr7WBWrP4taFbG10IogiIDKJrKGnYTsezKZ/f7+mCWzJmFNgO/79cormXvPvffcmZuZ75z7Pedw//33ccUVV6DTtd+mtHbtWv78fy/T2FBPcu4AEnOKiElKJ2/weBIye6LVafzjK6tuF6W7N+ByukjM6ofGmAiqN1AOGIIu8PnT6DX+HGff86cN6DTnu4Y8Xxa8neWMOmLjjSjeVkaNxjPU5JEdX3Jk+xfUHtuLojWiM8VibayhtaESS301ikaDomiwW1uYPHkyLS0tfPnll+gMZsxx6Wi0BuyWWlwuBwZjPBqdAQBrSzUarR5TfBaxSb2xt9bitLd4nn9bHTZLLcaYFJz2FqwtdVxzzdVcd911ZGRkhD2fxcXFvPPOO2zcuDHitZCckkZWVibDhw1l4sSJ5OTkhJWrqanh448/5uDBgxgMBvbt2091dSWKoiE2KQt9bBIajRm3w0JL3THs1mZGjBjJ6NGjMJlMlJeXU1VVRW1tHYmJCeTk5KDT6bBYLFRXV1NdXYPL5aJ370Ly8/PJzs4mLi4Og8HzfGi1WpKTk4mJiUHxfkY0NDTwwgu/Z+vWrbhCcwgAnU6P0WjCO8cPOq3nunM4HdjtNv+oJnHJmVw17yWSMnP8Y6UHXzOdnyEscH6CSAMN+MqELusoJok00k17DRTR2FubeOX+y89Kh7zzNzj++gAzRvfh9v9dhcEcC7TfUuj7HRgkt9eCHLpdNJE+3CP1bPU9PpkAuTMBc2d0NLxSpA/XE7ml1pFIX0JCb8tG28bzd/Th2wJbFsOWhfz2Dcwfus4XGLcXFAeWDT4+uN1tv6OJ1urf2SHc3O7g3tZOp5vq8iYq9m1g5Yvz+O1vf0v//v15/fXX+fvf/87gK3/AwMnfQ2c0R6wLBL/GvhzFSOUCW3F9vz1pRG5/Pl7gMFO+XEPfPgIDVN8+9QbvuMjOtkAhdMreQL78TsCfjxe4ne+4oSkSofuOJPS6DO1YFun/03+tBAxh1RkajRK2j8DrO/C5DPw72r6itchEeo3DO76GtwhH+nIT6UtOezoKjgPr7/9bG556Ei19InRoNqf37obL4cZhd9JaV0XN4a1UH/yahrId2K3NaHVGXE4bKalpXHnFVBITE4mNjWXYsGEkJyd3eE4+TqeTTZs28Z//fEJZeTk11dXYbFYSM/qSnD8CnS4eh7OO6v0baKk/7t9OZzARk5iBMT4DnSkZvTEOBQ2GmBRyBlyBonjuLije9yhF6wmWtTqtP0DWBQyTqNNr/AGxOdaA6nZTfWw/5cXb2b/hX1Qe2klKahr9+xXhdDppam4hJTmJ9PR00tLSUFUVm83GsGHDKCoqwu12s3v3bvbs2UNNTQ1Wq9Xfet7Y2IjNZkNVVdLT03E4HBw8eIgDBw6SkZFOUlIiAElJSaSmplJXV4fb7ebqq68mPz+/w+e0rq4Oi8WCy+UJ+GNjY0lKSkIbMsNjZ7jdbo4dO8a+ffs4fPgwjY2NWCwWYmJiyMjIYPLkyfTo0eOE93sy7HY71dXVGI1GTCYTWq0nrUSv17e7nc1m480332TZsmVkF42gaMI1ZPYZTFJWHlqtJuLn0MnqbIAd+P/ZXgux7/eJNDi63Sr21mZem3t2guPzNq0CogeloQLz2Norq9EoYR2J/OvayWMKXeZvbSI4MFY0gcPF469/WAfBkKGkIt0+PdFgOfADJlorWsdDhoUPceTbZ+CyaP8QkZZ3NFRWtGMHbtOZ4e+i3TkIfIPxBcaROtNFPnbkx6EBstuteoYa6mCGw9DnJ1InvNAWY7dbJTHZjK7/SAD27NlD//79qa9vACClR390RmO7Lbm+x74Ar6M7CYGtd4FBsS+o0wYExoHBsKrxDEXlCGipdtjD81L95+90Bw1dpbrbhsTS6bU4vJNq+Oqo0bYFvr7rPFKQHNgSHKk1tm2ygpAOZQTe4fCs9+WD+v+vICiwjxYghj7PnvXu6P8PIZd/4N2WwBu8roDf7X15Dc0nDvw72v9w4PL2vmSEHStCwB2tXMmuTdSVHiAhowfphcMwmOPC6uMfJsv7OtntThqrjlF98BuaKo/RXH4Ea1MJlqZKFEWhsLAPl19/Nbm5udTW1pKfn8/w4cNPKujy0el0TJgwgQkTJgDgcDjYuHEjn322lm93fkyrpYW4uARGDR/Ktdc+QnJyMvv27aOiooKqqioqKiqoqTlEY1UTbreb2poq7E31FAybiaLTorhVFL0WTcDnlupSvbfjPV/aAoeSc9osbPzny+z7fDmtzfVotTqK+vXjwV/+kiFDhvhbMzui1WoZPHgwgwcPPunn5mQlJyef0BeU9mg0GgoKCigoKDgt+zsVBoMhYmt3R4xGI7fddhuJiYn855NVfPbaLwCIiU8mrecAEjLyiE/PJSE9m7jULJIyc/130d1uF0e2r8ccl0R234tQaT+QDm1JjjRKF7SNpuUK+XyL9L4R+MXVJ7TzdKDAzs5nw3kbHKtu32816owzEDl4am9ms9B8xojjR4Z9qEU5bsDFEhgoR/vWFLR9hICvvcAzmkgfgqEzy52IaAFrW8eqjuvX0a3UtgCkreUucFKJ9lqJIx3Ht220FItIHetC+YJSnf8bu2e5VlFwqar/t28ZGtUfILvdqn+oId/j0H37X5v2AhpVpfzALjb/401qjh0kp/8wJtzyIKCy9vVfo9XqGDp0KAB33jmHquoq1r76M4wx8eQMmsiASTeSlNM76v59nXyCppx1R+61Dm3BtG9bX4Ds69TSNmtd2xBFroBe2r7nIjCAdAW0+mp1mqBr1TcygU6nbQtKff+jvtxlhwudtx9A4P9vYP0I+UIa+nr4R6EIGILJdx0GBqVO/wgsbn+w7L/GAp5TT4ngxxB8B8R3nh21EPt+B3Z2i9Zi3ZkW39DAVdEo/iHHIh2/veA57EMwZN/Rrm1PIO/iq3ef4/DmFRgMRux2GwlpufSZOJuE9B5YmxqJz+yFKTHbv13V/q84un0tjSU7aa4vRVE0pKamkd8jl55jxtOnTx+GDRtGYmJixOOeTnq9nokTJzJx4kSgrU9MYFCanp4edfu//e1vvPXWWySkDyA5dyCq2zO2i+oLjr2f5L5ARKcDl9PzZdNmaWLtXx6lqfII35kxndGjR9O/f3+MRuMZOVdx9hgMBm688UZuvPFGmpqa2Lt3L3v27GH//gMc3/05ez6rwOlsS9mIiU9Gb4rFYWvF0lgDQGxSGn3HX0PPEZeRktsHRdOWuwwBn52BedSdHFIt8H0g8DOivYZKCL4LdzLpF6fD+RschwxlFDF3S9M2OHpoSkWo0G85vgDa5XCFDbEUabv2guSg8r6hWKLME9xeq2vobZSTSWsIDEY6EjkPMbze0W7vREtTiSTSfv1Bbcht1472FWkfneWbuc5Tp7bXNnSWII1G8QTBBAfIbWWCXzP/9RVlPNrQ1AnfcQNnpTq8YzvLnrmH9PQ0+hf1Zf1//k5rYx1oDBze+hk/+ck8evXqBUB8fDxPP/UUe/fu5auvvuKTVZ+yYsu/mXD7U+QOHB923oHBpMt7bHeEOwOAP98z8DXT6bWeoDPktj6ETwATGHAGdehxtuU5A55OU2HbBKcgOJ0u/5BW/mXewNl3Xr5tIg7D5A4fcSHwb423dTHoi7RW690mdLQHFziD6+jfX4Qv1IGt2krAdRJYPzXkGglNa/H9dkQIcl0Op6euAWUD00Xs3tFHXA43ilbxd7jyBF8aXA4bLpcVt9OBoijejkJxaLVa/zZqyF0FjSa4Q1ngOQc+H/bWZhrKDtBce5zW+iqO7/qchvKDPPTQQ0yZMoVjx47xxz+9xLYPf+9/7RVFQ0bfcaT2Goml9jCHv/6I3Jxcxo0eyJgxdzBs2DBMJhPdQWdbagFcLhdDhw7lrbfeorWxhOS8wZ6WeZeKy+LwvpYO7C2lOKx1uJ02b+5sBc01pdQe3QlOK7999jf07h39y684t8XHxzNq1ChGjRrlX+Z2u2loaKCqqorjx49TVlaG1WoFYPz48TidTj777DM+/XQp2z5+Da3eiCk2EUNsEubENJKyezF0xg+IiU/wNApESeHsTGpn2OgyUeITjSZ8+Fc1wvvdmXbe5hx/vKmYqy4u4nvPrcRgjvW/EDqdNigw9iWwdzaxHIID5GhOZla70BSQaLNRnXDrcAetOO2V66iTXaDw28yR9x8pQAosE/2LhCYscA/8gtNZoR/MkV77wM4t/u06OTSbb7kxSocifyuwquJ0BuRFev8VA9MKQp8Pt1vFYnGE3bFwOVxs+dc7fPHWc7z77jsYjUbWrFnD888/T0xiGsMH9+Pxxx+P+pw4HA5+9atfsXPvAWY8+hp6c2JQXSOmGUXI/ww6z0gBcpRy7e0jbFmEAfM7l2ITPJGM77FOH/wlK/CcQ/el07cF65FmdQyc9SxwjN3OiJY21NH/XahILcI+tpZGao/u4uiWf1J14GuSewxEdbtx2S3kjZxJRu9LwOnG5bBx7JuPqCvZiqW+BI1Wj84YR2r+OOpLt2JpPI7D1hLhHHQYYxPRG2MxxGdQOPo24jIKPO8jCqBx4XZ4AjpVdeN2uXC7W2ltqMDeXEVrYyV1R7ZSW7LXv8+4+AQGDRrItddcw5AhQ4KO19LSQkNDAzExMXz++ed8/K8VHDt6hLT0DC6ZMJ45c+YEXRvnErfbzYcffsj7739AfX0dRnMiQ6/6NeaEdP+4t4pGwd7awI5//ZLGqoP+bT0d1VLJyc4mPz+Pa6+99qRu3YsLg91uZ8+ePRw8eJDGxkbq6+upra1lx7c70RljSSscQmaf4fQePQ1DjGeYz+Avt5H/x6LNSBvpy32k8r4y/tkjW1t469Fp0iHvVPxz0z6uvrgfsxf9C4PJ0yHP0/vZM6xOYIux3uhpQI/W6hr6AR1pso9A0YZUCloW4VhhOTlREtpD6xfpceAxQj8sO+p41xntfYPr7Id5e+kn0SbyCEyZCL6N3PE5RWvBDq1DYIfMwEDe10IcaR/aCHXx/R1j1vlTKHynFZpzDAS1AkP4beagW1TeKckDe/1u+eebfPXeH3n//ff8LVNvv/02b775Joqi4ZlnfslFF10U9fxra2v50YM/xq01M/HORSRmFQSlLYT2/g9//oLvuoQGl4HBXmCg6ktzCBVtSL5I68PXtf8lK3AfvlSMaBPWRJquNzC1oqMvB2H/f74Phij/hy21x3FYmzDFp2FOSotyDhE6x3pvQaqqm9aGGlrrK2htrKSx7ABNFXux1JVga20EIDMziyuumMq+ffswm81YLBa++uorUnKHYozJoLFyJzZLNSNHjKCoqC9Op5MdO77l2293MHzECIZcdBEpKSmYzWYMBgOqqmK326mrq6Ouro6WlhY2b95CeXkZCel9sbfW0tpUA+1MTqIoColJyQzo348xY8bQt29fcnJyOuyYFMrXsepcV1JSwv333w+AzhjH8O/8kpjkXH9gDNBUeYTiDX8CWzWPPfoIBQUFGI1GjEbjKeVMCwFw7NgxVq5cyd59xezbuwed0UxiZiGmxEzPRDaqG63ehE5vAkUFVcUQE4sxJoG4lAziUnNIyS1EqzeEvYdHvsscOcj2pajZW1tYOm+6dMg7Fb7nPfJoCuG5xKHBarsBZMi60BEnAkX94IwSsAUFV4HbaoMDiMC0i0iBgNutRswDba8ubefQuQD5RFu0AgXOcBba6z9aJyVNhNuxgcn+wXVT/WOeajRKUIte6G1z/zb+jpnuiIFVpBzO9u4Q+LS0OsL258sxbj+VJLhF2Rkwy5XN0kzxxlUc3bGRqgM7catuWhvrGDZ8WNAt21mzZvHlV19z+GgJz/zqVzz3299G7YWdkpLCc799loW/eIpNS3/N5Q/8Mfj8fS3FIS2w/uA+ILct0heR4G2Cc4Uj0URo0Q/6HwgJcoNGjtB07voMug4j5ru5g/5XfcfyzYoZvK+2Idzacqc9+3eGTGlefXAHpd+uwmmzoDPGkFl0MZl9x3Jk67/YtfIP/veRgpHXkJDVB3BjqSujtaECW1MtjtZ6FEWL1hRP3ohrsTfXU3NoG611R2lpKMMdMDRUYlIygwYOoHDKCLKysujbty/Z2dlht/Y3bdrEq68uQdNax4ghvfnudx+jT58+/vUul4u6ujrS0iIH7KGsViurVq1ix44dZGcPISMjA5PJ5B8KTVEUtFotZrOZrKws0tLSOhwmrTPOh8AYIDc3l+nTp7NixQqctmZcLs/tcLfNicvu4MDm1ygvXk1Kaio/m/8E/fr16+Iai/NNXl4ed999NwAVFRV8+umnlJSUUFFZimJXUBQFa72VZpvNM+yfCtWtFpqbm7DbbABodXpSehSRktePjN4XkT/kUrR6Q9ixPJ/L4XcFA+OxU+kPdaLO2+A4Odbz5EcLnoICrNBcmAhj8gWKNKyJLwe5M0OedJQCEDXdIOBDO3DMwWgtp4F5hJH2F1z/jlvMTlWkb4WKRkFDcLpEZ4LiwLK+19inLXcycp6u261yfM8Wlv/mv0jJKcCclIHJ21HBGBNHbHIGcSnp2Foacbuc9L/kahStPvLr5m4bt9flDh4Lsr1rwR6wXegbQTS+dZaGBpY8+B3sVgu9+/Th0otHYLVaqays5I7bbw/aRlVV0lJTqKhrQdFo+N3vXuD555+Leozs7GyuveZq/u//Xsbtcvlzaj2nGtxyHC3VJyhfN+R/L9KXUc/jtkA4UmtscE5vhOsgIPc3dJ8nnJYQ1BE3PLj17dfXehx6LoEzxbV11PPm0trs7FvzJge+WEpGZhaZGRnU1R5m87sr0Gh1uF1ORo4cyW233cbatWv56KN/cGTzRyiKQkpqmieI7JVCYmIvXC4Xe/ftY/uyZ1AUDX2Lihh18UXk5k4nMzOTjIwMMjIyiI2N7dR5jx07lrFjx0Zdr9VqOx0YA5hMJq666iquuuqqTm8j2iiKQkuLBYAeg64hNrknLoedlupjHPnmbzRU7OS++37IlVdeecKt60KcqMzMTG655ZZOlVVVlebmZkpLS9m3bx979+5l/4Ev2bvufeKSM7n2ib+h0eoidnwOTFsLnsgmPM3tTDrvguPFixezePFimlo931r8QzN5f0zm8BZEDe7g0SJCWqpCB7+OlGscbcDsQJ25pe8/rju8VSpovcvXWUfTboDsO5/2Zq6K1lJ8IsFFR3l90XKPPA/Cg55oQZSPr2U40nLPlOC+USzaUiTcLiele3YQm5RO8YaVqKqbmtJDUHqo3bpvfGcxt/7PiqjBri/vGUBRXBzeupbMwoHEpXgGtFfdnk5sWm/6QKQ3hNAW7Wjn7nK4QNH6rzWb3cHKlf/Gam0F4IEHHuD666/n6NGjHD1WQm1NNW63m/Hfe5zS3V9yZPcGVFVtt0NQ3759cbtdFG/4iL7jrgtLNwhtOQ4UsUW+U8G/N9Wig44d7U1/GjisYchW3mN3fFcnNAjXhFybvvW+IeQ0oecU8GXU1ur0DGWnummuOkxl8SbKd35CS305t956K7Nnz0ZRFFRVZfPmzVRUVOBwOBg/fjwZGRn07t2bOXPm4HJ5hqeLFAA5HA527dpFr169zvhtRnH2+V7zkp0fUbr7Y1DdqKpKfHwiT86fz4gRI7q4hkKEUxSF+Ph4+vfvT//+/QHYu3cvjz76qLeDsacxI9JneGhnbV9rseez3fPeOnr0aLRaLXPnzmXu3Lln5hzO15zjZWt3c8OkgVy/8CP0plj/LU+dToPRO41nYGcujUbxj1wQaXIOn/Zye0OdTKe8joLkwOWRcp7DWlDbyZ1ub/8nMoi/f1xRu42m6lKSsgvQaNu+d/m/CdqtaPVG9AYtvh7v7QVOkXKm3S4Vhz18NqFIAZve4M0l9wZ3lYf38OEzd0Q9H41Gi1ZvQKs3oqoqcckZ1JQUozfFcOPT76FoY3C53Niaa2muKcfaXItWbyKtYCBavRmXw8rW5X/g4Jcfo9Mb6TnqSlJ79MYYl4CtuQGNVke/S76DVh8TNFSO77kAaG2qp3TPFvIGjcEYExf2nPies6bqcvZ8/g8sDbXEpWZTcXAXh77+BJ3BhNEchzE+GYM5DhQFp62VxsqjOKyt3HnnHK6//vqoz4HPU089xe5DZUyY8wd0JlP4tM3tvG5Rv6RFuTvQUb65Z5/uDr+AtSc0jSrw/6e9nOHAoRt9X160Ok3QWMW+znfgSX1xOZ2U7d5I7bGdNFceoLG8GIfNgtFkZuzYMdw4c6Z/1BAhOlJRUcH+/ftpaGhAq9WSm5tLYWHheZM+Is5/e/bsYf6TT5KQ1ZtLbn+SmKQsHHZX2OdIYGpa4Hjlvs92h7WFfzxzveQcnwpfyG+3OVG0ruDOQq0OdHoNDu+LodV5eqBrAqeZbWccv84EyKGtjGH5jB21DLcTFPu3idIiHNr6GJjDHClPMtJ20XqNRnrsGZ/WwV9/PDlofd6QiRSOmkrfi6/EUl/OW/NuACCtRx9i03KIT83G0lDNwa9XMfbGBxgy7daweqhulaaa41Qe2kN20Wg0elPQeYXmbrd92fEENZ7gxXMuqT36YoyJw2G18N///d/YvDlRZrMZt9tNRUUFBw8eYuu2bTjsdmJSe5CUW4QKrHllIdaGSppqynDarcFPjOLJvVLdbjRaLf/1X/9FVVUVGzZuYvNX/8LpdGIwGnE5XWxe9ifi0vLQ6jy9/81JqcQkZZKUVYjd2sKu/7xGc205OoOJ5OxeOKzNOO02knIKyeo7gr4TrkHR6GlttJKY1Qe7dTdHtq2l6vBuwPMFxGm30tJQjclsJjU1jR5ZmfSfeAPjxo3r1ExUADNmzOCrp56ivmw/yT0GeibUiNKyHfhFJ5BGowR1HurUNa3xjNEbWfCYwqHa6/gaqUXbl4ceeq2rIf9Lof+bvql/28q0zWzZUl/Jxtcfw1JfTlp6Bn16F1J06Uz69evHwIED5fa3OGGZmZlkZmZ2dTWEOCGlpaUcPnyY48eP8/bb75CYW8Qlcxah1ZuwWdsauEIbH3zDWgam8QXe/T9bzt/g2PvbN4GC2+0Z79QzRJqns5pOp/FODOD2DusW3tQfOnOeT+Bt/44CY4jeOuUTrYNYR3ytxpE6i4XWFcCtiX4ce0sD1SX7aW2owRiXSExiGqrbSUt9NQ6rhfjUTBLSctHo9aguF3ZrC62NNdiaG9CbYvw5kz7HvlnHsW/W4XTY6Dm8LXCuLtlPdcn+oDru/2o1h7asxWW3EJ/Zk7iULCz1ldSV7KeuzDNEkSkuibG3PEl2vxHYWy1YLa0YYxI9z60vOHN6vtzo9KDxzVjmnYBB0WiYePt8PvnTT3j//Q/Iy+tBQkIC3/nOd8jLy/PXxeFw8MYbb1BcvJ/WGiuoKvlpqaQWDiA7ewrZ2dlkZGSQnJxMc3Mze/bsweFwEBMTQ8+ePf2zLv3gBz/A5XLhdDoxGo3U1taycuVKqqursdvtWCwWqqv3cvDbz7C0NAMwfMQIbnn8Yb755hvKysqIj49Hp9Nx8OBBtv3zZb7+8E9t3/zwDHM1eNAgZlzyfTIzM0lLSyMpKYmEhISTGtPVarWybds2XnrpzxhjkzAn5uG0OT3/My41aAgp33+JolVw2KOkFAVcZ9qA/6XQ/LLQ5YEidezrqBXZ/8bawf+QRhM8FXVo7ntg3rQnPz74GIFv3AD7172Npb4cgEcfeZhBgwa1e3whhDgfqKrKrl27OHToELt27WbdurWAZ0r03IHjGXXjY+gMns+k0IDYN0GTT+B7amDZs5nocN6mVby3Zic3Th7MlfPeQ2+MDQpifS2LgTmF/rGPvY8jBcX+FuYoKRKBLWuBt6Hb65jV3nSM0aZohM7fyvYt9wz55cbtctDa2Ii9tYH6sqPUlR6k5tg+ao7uobmuKmo9zyStVkth7z7kZGdhMpk4evQYFZWVpKenk9cjl5EjR5Kfn89LL/2ZHd/uICm7L40VB3G7XaTkDiCtcDgxSWmY41NJye9HTGJ625cFlwtrUyX2ljoUrQ6d3kBD2QGObFuDw9pCY+Ux7C2NzJgxnTlz5pyW3vInSlVVampqsNvt7Y5FWltby6ZNmzAajSQkJNCjRw8yMzNPaEKBSFpbW/nzn//MF19spLXVM3ZtSu4Q+l02F1OiJ29a8bYC+35HPI+A4NmzTeRxniG8VTlweXt/RwqqIy0Lmo2unXz8wO06mvwmMIXC7W4bDcY3JFvN/kOUfPsPSnd9DMDChQsZOXJku/sUQohzXUNDA3/+8/+xbt1atDo9Cek96DX2GvKHTkFnig/7jIrWaBHY8TnS3XCHrYV/PztTxjk+Fe+t3smNUwZzxSN/R28M7q3tm6HJ1yKk0bS1GvsC5sDhxHyBsi+oDpxEpD2+Y0SaUKKzwW2k7ZyBM1XpNRzfu42DX62isaoUjVZL3/FX47A00lRdRs2xfVQd+hZLY13EfScmJtOzVwF9+/ShsLCQnj17kpqaSlNTEzU1NRgMBpKTkzGZTFRXV1NRUYHL5UlTMZlMpKSkkJCQgMVioampiebmZpqbm2ltbUWv1/tvI/s6Fbndbv/2er2euLg4Bg4c2Kn8OYfDwb///W927NjBwIEDMZvNbNy0iT179tLc1OjPS4pJTEOj0eFy2LBZGqPepjfHJ2OISaKhwtMh78UXX6Rnz54d1uN80NLSwq5du/j222/5ZNWnWCxWcgddgykuE3N8JvEZRSiqiqLTotFrQKsJCow7O4tiaLAcKLTTZbSgO5rQuzah/ydt+w/P/Y8UtLd3Tv5OId7WYpfLTWNVKTWHd1Fz6FuaK/bSWHMYg9HI2DGjueyyyxgzZswJnY8QQpxL6uvree+991ixciVuVWHEDf9N7uDLgkY58ok0qVI0gY0bbpeK0+lpkLBZmvj3b2+U4PhUvLd6FzdOGcQVj/wdQ0xc0DrfYPkQ/CEZOK1paOuyVtfWChb4ARtp1jb/ugijL/jG3g0tG7jfUIF5xb7ZYtqG9armg4WzcTkdjBw1ivLyCkpLjgGe8U0L8vMZMKA/GRkZaLVatFotcXFxJCQkkJmZSWJiYmeezm7P7XZTV1fHnj17KC4uRlEUTCYTCQkJZGdnk5ycjNvtxuFw0NTURG1trT/Yr62tY/r0aYwfHz5t8rnM5XJRVlZGSUkJR48e5ciRIxw7VkpVdRXNTZ7JIEyxySRlDSNv8A2Yva3EUQVc675WZLTB41OHBrhKJwLfoI6rneyoGm1/Gm+nW8/fmqgpGKFfgtWQW3jWVgdut4rd0oq1uQ5rYzXNVcdorjxCS+1hmqsPYrd6UmGysnMYOKA/Q4cOZdy4cZjN5qjnKoQQ54OGhgYefWwetXWN5I+6iv6X3oQx1hNPaAIaIH1Cg+NoqXWeZcEjDPkeW5oa+Nei70qHvFPha8VXtEpYC5Vbo6K41aAZqnyJ4KpGwe0PklU0bk8A7HS40ek1bUM4BXw4+3IVAy8Gl9NNQPpt+DBSAYFGpHxjH5f3NkNgazGApa6cyoPbaKmrwOV0UFjYm4ULFuByuaisrCQlJQWj0XgSz9y5SaPRkJqayoQJE5gwYUJXV+e0q6+vp7nZE4ypqorNZqO1tRWbzYZOp0On06HVanE6nTgcDvbu3csHHyyjtdUzTqreEENsUh6m+HxSC0eSG5tGfFpfTHFtaRmhM7aFj1gSYdgdX6c2nbYtYAZ/0OwLmAP3FRpEB6YWRcs5DuuM4WxbF1QOz4gRvo59vjtCobPtqS4HFQd30lJ7nKbqcprrKrE1VWNtrMJuacTtcuB2OXE57W31VhTS0jLoW9iL3pdeS9++fSkqKpIh1IQQF5w33niDuoZmJtz9IgnpuZ7GBLvTEzu5O38XMPJgAu6gRkq32+0ZCUg65J063zcNk1GPVqcJml0MwGTyDufmHcHB12wPbcngbd9sPB+sTqfnxTIYdf4gGVwdtvy21Ulh7WsLaamrpO+4q8govAiHzYKtpQmbpRFLYy225gYyeg8nq+/wgDwbC42VR6kvPUDNsd1UH/qG+vIjKIqCOSaW5JRUf8cfrVZLdnb2aXgGRXfhdru57bbbTmgbjVZPVp+p9M4ZQWxyLnpTUljel+pWQSWsk4Mvdzja9MaB27v9nVcDZsfTazxBc8AXQF9qBniDY3dbeoYvUPYNoRipJSEwd97X0hxtLOzAVKbAKaqdtlZqju6kfN/XHNmyAltLI4qikJSUQlp6GnmZKaQOHEpSUpI/JSg2NpakpCRSUlLIyso6qU6OQghxvtm5azfJ+UMxxmVia3W2vQ9HGcTAx39XPmCG3MB1nt/Bk0KBdxhTXfv7Pp3O2+A4NaGt1VSj0WAwtH1YajQaf+K32x4wtJN3xAODd3zcSDOB+R4rIaNCtDeOse+bj9utcmT7OlwOG5UHd4QFJTq9HpPJzDcrlpCYkY/qdmJvbcba4rkFrigKPfLyGTu0P8PvuIXhw4fLWJcXAI1Gw80338zSpUuDluuNcWT2voKMnhNRNCqq24Wi0aFodOgMsej0Abf3vUFwZwLewN8nw+1w+4PmoNEf9J7xgTV6DYpBg93eABonsanZaLR6VLcb1e3G6VDDWpqD6+PZd2AmuUav8YxT7lbR6BSa66poqi7F2lBGQ8UR6o99S0PZftxuF3HxCVw5ZRJXXHEFPXr0kOHVhBDiBPXv149PPvkPR7dcRM9R1wCeO/XWFjsuqwuXxYHqVjFlxGKO1UftGN3RyEOePl/eidhc4bnMZ8p5GxzXNXvGsNXpNRHH9zUYdf6hQ0Jv29q9k0y0ddQLviXrE3o7oLWhkkOb/4PeaMYUl4w5MYXU/P7ojW1BSu+xV1O67d/MunEmeXl5pKWlERMTQ3x8vD/Q3bhxI1u3biUmJobY2FhSU1Pp0aMHPXr0kGD4AuMbHicuLo4rrriCqqoq/7Scra3NlOz6AI1GQ/5FM9vfT5Sg+FSC4BPVUL6XykOraa7dT2tzlT9lQVE0xCSmk5A9mIw+E0nOGoii0XqCbJsTRe/pFKgxhr9d+VqdHa0W9m9+j/pj22muPobDZvGXSU3L4KKB/Rl07RQGDx5MXl7eKY/wIYQQF7If/egBdDot//7P/9FccwRzQhp6cwLx6fmk5F8EyW1zEkS7y9eZEYUCc5PP5vv2eRsc+55mp8ONom1LBG9rxfWs1+m0QakVnjJKyO+2Vme3d6IH1a36Z3oDTxDzwS9mAZ4Pe1X13c41kN1/DEWXzCSjz3D6XXYTTdXHWLJkCYqioWevXlw+ZTIzZszwv/Djxo1j3LhxZ/YJEueEXbt28fjjjwMQn9ITrT4Wt9OFRp9IUkIBcSl9yeozJWibjnOHzxBXeE6y6gKn28qRb96i4tBnxMbGMGXKFDIyMsjJycFkMvk7DX6xYSPbP1pFau4I+k98pC0X2ubEbQOaA/J/fekY3v/NI9/8ndJvlnHJJRPIv3wseXl55ObmkpmZicFgOCunL4QQFwqNRsPdd99NU1MT69f/M2jd8Bt+Qo8hU6J2tvPFXG6X6vm7E59RZ3MCEDiPg2MCnkdfKoUvuA1u8XXhtqtB31yCppgNGQbM18EHwGFvW6doFDL7jqCieAv5BQVMGD+OHj16UFxczPKPPuLYjs+5aNochs6Yw+R7f0tLbRmVB7dRtucrXnnlVf7yl7+g1xswmc2YY2JIiI8nMSGe+Ph4jEYjer0enU5HYmIivXv3pk+fPsTHx5+JZ050I+XlngklsvvOID1/AvGpbdMOtzcCxNlsEfbTht8ea2k4wv4vF+Oy13PffT9k+vTpaEOG+RkyZAgAd955J59//jnPPvss1Uc3kl7g+YIYmAMdds5uN2g0tNQeJTMz0z+usNVq5dtvv+WLL76gqamJpqYmnE4nLpcLl8vFNddc4z+uEEKI9pWVlfGnl17i6NFj/vfSVosFV8DIAwZTDHarBY3iwGTWB03sASHjz0cY0aIjZzNAPm+Hcvtg3R6+e+kArp2/HK3B3G5eiy9gDp1oIJrAADn0tkDtsd18++/XqD60HZfTEbRdal5/rvjxS0Dw1Mv1ZYeoPbYHl92C096Kw9qCzdKIrbnB03PeacPldOB22LE01eKwem4ZZ2Rm0b9fEVdeeSVDhw49wWdInAuqqqpYsmQJmzdvwWJpoefQ20jtMQpTfFrE8qpL7XxgHNjSGxDYqqqbqiOfc/TbpRjNyWQUXk5m4WQUpf3cMJfTiq2lGltrFdaWKurLt1N3fDv5+T15/PF59OjRo8MqNTU1ceutt5JReBlF4+8NagWPFCAr3v/FmpLN7Fr1PG5X2/+comgwxMRjMCegM8aiaPQoipaao9soKCjgF7/4BampqR3WSQghLmQHDx7ksXnzMMalUDBiKhqtHkWjRWeMITYlh/i0XMyJ6SgafVAKa+hQbJHGOY4Wm0Uq67C28I9nrpdxjk/Fe5/t4sbLBvlnyAsUbfKA0KmXIw8xErxNKP94xnYrtcd24bJb0cckYDDHE5eShc5gDhv/OLDTUmhHpFAKKg1VJdQe28vx3Rs4unUVAC+//DJZWVlRng1xrnM4HCxa9Bu++uorVNWN0ZyA3hiPVh+H1hiPKTYdc1w25vgcYpPy0eqi56bXV+xg17rfAGAwxaPR6NDqjGh0JjRaA057Cy0NpUyYMIH169cDMHzab4hNyYu6z9rj29j12bP+x1qdjsLC3lx5xVQuv/zyoE5vLpeL6upqKisrKS8vp7S0lGPHjnHo0BGqqioAyB0wg96jb/dsoNGA2902XJzeExCHTkqi4kT13ulRXaDR6VFdbuyWZuyWBhytjTgsDZTvXU31ka8AeOKJJxg7duwJvRZCCHGhaGho4Je/fIbjVfVc98Qb6E0xYYGrL3byzePQNrpQ+GAGofGWf5Qw7zjIwf262gZFcLtVHLZmViw6OzPknbdpFZFCV18rlMul+nu6a/WeYd7ahn5qS62I9iIGlvMJLaszmEjrNTysDr58Z6etlW0f/R6HtZnUvEEY45JQNFqcNgsOaxP21macdgsJ6Xmk9RxMTFIGjZVHaao6Ql1pMbVHdtJYfRyAESNGEBcXF3Yscf7Q6/XMn/8Ezc3NbN26ldLSUpqammhsbKShoYHjx7dxcN9Kf657bGIOMUm9iUnMIzYhD3NCDnpjPBqtAberLXfXbm2KekyLxUJCQiLa2ALMCblA+Bc2X2tuY9Uu/7K8vDxycnIwGAzs2rWL3bt309DYSG1NHXX1dTQ21Ae8uSrEJKRjSsglJmM0Awb3Jj6tNzHJ2Z6Wau/MfG6XEzRuUN24VQeoKg5LE9bmGs/4xM3VWBursTVVYW+pxmlrxmFrCeqY56PT6UlPz0CjUcjI6GDiEyGEuEBVVVVx5513AjD8mh+i6PRhY8Z7AlrvjMMhM5D6UidCW479gx/YXGgDRiPSaLVBLc9tgbJ39KOIkd2Zcd61HC9evJjFixfTZLFRcuSgP63C6Wx7UULHcVW04a3IoX+HPo42WUFnNVWX8Mnv50Rcl5CYREJCAiaTiZKSY1hbW/3rDEYjeXn5DBzQnwEDBjBo0CBSUlI6fVxx/nI4HJSVlbF//3727NnD3n3FlBw7ht1u85fRaPXoDTHo9DGg8QyfhqJFpzejM8RjMCWhKFpcbitOewuKotBr6PcxxqaFTAUdMPOdRoPb7aSpqpj6sp001x5GdTtQ3U7cDhsulxUUHVq9Ea3ec2y9OQ6DOQljXDrGmES0hhj0xliMsalo9Z5ezjWl26jct47m6v0015YS1JEghMkcQ1paOhkZaaSnpZGQkEBcXJz/JzExkYSEBP/y0LxnIYQQwRoaGli4cCH79+8HIKf/KC6++TH/pB+d4Qt2Q++Yh6ZTuN3usMnOAv9W3Sq2liaW/eJaioqK0Gq1zJ07l7lz5578CbbjvAuOff65cR9Xj+vH955biUbXFhy7vLNnOR1uNFoFu80ZNJRbpI557QW9kW4BdKYsgOp20VRdQl3pXo5t+w8V+7cAoNcbGDR4MPOf+DlarZYjR45QV1dHjx49SE9P73BcQCF83G43lZWVlJSU0NTUREtLCy0tLTQ3N1NbW0tVVTVVVdXU1dX6O1boDWZiEvIwxfcgNiGPuORexKb29o8eEdh6rGgV3C47loZSLE3HaG0qx9pchcNSja25itaW2rA6KYpnSJ5o/y8GUxx6UyIt9aXkF/Rk8KCBFBYWYjQaPTNLerePi4sjNTWV1NRUGeJQCCHOEJfLxdatW/ndC7+nsbGBzN4jyLtoHLkDRpOU3cs/MROEp04ECh1SN1CkSdRCRwyzNDWydN50yTk+Ff/cWMzV44qYvehfGExtOceRpmp2Od1tQ4pEefFCX7jQcr7bB6HHCSzb0Qx6tqYaGioOs2/d3ynbu4knn3yS0aNHt7uNEKeD2+2mqqqKI0eO+H8OHDzM8dIS3G4XCal9yRswk/jUIppq9tFUu5+WhmNYm49haSz3p3OkpKSRmZlBZmYGGRkZ5Obm+n9iYmL8gS143nCtVistLS20trbS2NhITU0NVVVVVFVVMWDAAC677DIZk1gIIboBm83GmjVr+Pzz9ezcuROHw47BFENK/kBS8gaQ3XcQKXn9iEvOCHvfjpSnDO0HzNAWN7ldKg5bC2//ZIbkHJ+KwJg/0gx3gTQaBa3OO3tXaE5l2OxcbdtE4mtV00eZ5jBS/rKPOTENh81C9eFvGDZsuIxAIc4ajUZDZmYmmZmZjBkzxr/c4XCwbds2/rb0bXZ+vgid3oTTYcVsjqFnz54UDhtOQUEBvXr1oqCgALPZ3M5Rgmm1WmJjY4mNje24sBBCiC5lNBqZNm0a06ZNw2azsXfvXvbs2cPu3bvZ++Vydq16AwCd3khscjbxGbmkFQxgyLRb0emDx5v3Tx+tiX7HPTSAPpttuedtcJwS73khHJZ6DCYTGq0Ot0tFVVUaKw9jNMcSm5zpefI1Cpb6KmpL9mJvbcbR2ozN0ozD2oLb7SQlrz8ZvYcRm3TynXfCJxYJv+3gBg5v+TcOWyslpaXMf/JJEhMSSEpKYtSoUYwaNUpSKsRZpdfrGT16NCNHjmTlypU0NjYycuRICgsL5VoUQogLlNFoZMiQIf7x4lVVpaqqioMHD1JeXk5ZWRllZWV8s2IJh7d+RlJOH4wxCcQlJ2OMTSQhPZesouFoNG39P9wuJ9bmegzmeHQGY1gA7Xaevc+c8zatYvkXu7luwkAANFodyVn5xKb1oObILlrqqwFIziogs2gEtcf2UXlop39bvd5ATGwsMbGxoKqUHS8FID4lk7Reg0nNH0BsSiam+BTM8cmY4lPQm2L8txEipVd0NNi175tRY+Uxjmxbja2lAWtzPbaWelrrK6kvP0JySiopKSnEx8eREB/v71zky8X05WP6/tbpdEyYMEFGshBCCCHEWbdt2zY+/PBD6hsaaWr0TMhksTQDYIpNJSYlF0drPXZLPTZLo3+72KQ04tNyiU/vQUJ6D+IzPL+X/3qOpFWcitZGK+CZdctgMHDkyBFKSkoZPmkCo0aNoqWlhc2bN7P9mw30LCjg1hseZsiQISQkJASNyQqeHps7d+5k9+7d7N6zl23/WIvTETzBh85gJCE1m7j0HsSn5xKflosxNgGDOQ6DOR5DTBxGcxx6cxyKRoO1sRZLQw2tjTVYGmpwWFvQGc3oDCZScnqiM5oxmGPQGc3ojTE01RynfO8WrC0N1Lc0UFHeiP1gKbaWRhy2Vmzeiy2UwWBg8uTJZ+ZJFkIIIYSIYtiwYQwbNixomcvl4sCBA3zyySc0NTWRlJRPcnIySUlJJCYm0tTURFlZmWcc/OP72PPNWlpamug7bMpZq/d5Gxw7Wjw976dNmxa1J/uECRM6ta/ExETGjx/P+PHjAU9ieXNzM/X19f6f2tpaKioqOH78OKU717K7qhK3y9XBnj20Wi0msxmbzRYWdAeKSUghPjWb2LQcDLFJuBx2FKUZh7U1qJyiaEhITKRXr14ywYEQQgghug2tVktRURFFRUWd3qa5uZn1Wyop3vbpGaxZm/M2ONZ2MNXtqdBoNP4xU/Pz8yOWUVUVq9WKxWKhubkZi8XiH0bL5XKRlJREcnKyN00i3p+/6XQ6sVqtQT8Wi4Xq6moqKiooLy+nvPw4Go2G3r0ySE0dQHp6OqmpnpSLlJQUEhMTZRxXIYQQQpwXPGPW2zsueJqct8ExnRyg+kxRFAWz2YzZbCY1NbXT2+l0Ov/EBUIIIYQQAjiLXeTO2+7m52k/QyGEEEKIC08HYyKfTudxcNzVNRBCCCGEEKfFWcwIOKnguKmpiYceesg/6P/48eP56quv/OtVVeXJJ58kOzsbs9nM1KlTKS4uDtpHSUkJ48ePJy0tjT/84Q9B67Zv3861115LRkYGJpOJnj17ctNNN1FZWdnpOkrLsRBCCCHE+eFshnUnFRzffffd/Oc//+GNN95gx44dXHnllUydOpXSUs94wM8++yz/+7//y0svvcSmTZuIjY1l2rRpWK1W/z4ef/xxvvOd7/DJJ5/w+9//nv379wNQVVXF5ZdfTkpKCitXrmT37t289tpr5OTk0NLS0vlKSnAshBBCCHF+OItx3QlPAtLa2kp8fDwffvghV111lX/5yJEjmTFjBk8//TQ5OTk88sgjPProo4BnnODMzEyWLFnCzTffDMDAgQP5+uuviYmJYd68eYwePZpZs2axbNkyZs2aRWtrKzrdyfcXXPnJDqZfMYS333476lBuQgghhBCi+1u/oZJFv7qre04C4nQ6cblcmEymoOVms5nPP/+cQ4cOUV5eztSpU/3rEhMTGTt2LBs2bPAHx7169WL58uVcffXVrF69mltuuQWArKwsnE4nH3zwATfeeKN/1rmO2Gw2bDab/3FdbT0A3+6qxWRqjbKVEEIIIYTo7o4c88yg19jYGLTcaDRiNBpP78HUkzBu3Dh10qRJamlpqep0OtU33nhD1Wg0alFRkbp+/XoVUI8fPx60zaxZs9TZs2f7H2/evFlNT09XtVqt+sADDwSV/dnPfqbqdDo1JSVFnT59uvrss8+q5eXl7dZpwYIFKhD0YzAYwpbJj/zIj/zIj/zIj/zIz7n3EymuW7BgwcmEsu064bQKgAMHDnDnnXeydu1atFotI0aMoKioiM2bN/PKK68wYcIEjh8/TnZ2tn+b2bNnoygKb7/9tn+Zw+GgqamJlJSUsGPU1NTw6aefsmnTJj744ANqa2tZu3YtF110UcQ6hbYcNzY2kpeXx7Fjx85483sko0ePDuqkeCEd/0I9966+5kBe9wvx3Lv6upPX/cI7966+5kBe9wvx3KNdd2ei5fikknp79+7NZ599RktLC42NjWRnZ3PTTTdRWFhIVlYWABUVFUHBcUVFRdj82nq9PmJgDJCamsqsWbOYNWsWv/rVrxg+fDjPPfccr7/+esTy0Z4c30x2Z5tWq+2yN42uPv6FfO7QddccyOt+oZ47yHvdhXbs7nB8ea/rGhfyucPZue5OaZzj2NhYsrOzqaurY+XKlVx33XX06tWLrKwsVq1a5S/X2NjIpk2bGDdu3Ekdx2Aw0Lt37xMbraKLzZ0794I9/oV87l1NXvcL79hdravP/UJ+3bv6+F1JXvcL79hn00mlVaxcuRJVVenXrx/79+/nsccew2QysW7dOvR6Pb/5zW9YtGgRr7/+Or169WL+/Pl888037Nq1K6wjX6h//OMfLF26lJtvvpmioiJUVeWjjz7i8ccf57XXXuO2227rVB0bGxtJTEw8K70ahQC55kTXkOtOnG1yzYmucDavu5NKq2hoaOCnP/0pJSUlpKSkMHPmTJ555hn0ej0A8+bNo6WlhXvvvZf6+nouueQSVqxY0WFgDJ4h3mJiYnjkkUc4duwYRqORvn378pe//KXTgTF40iwWLFhw+nswChGFXHOiK8h1J842ueZEVzib191JtRwLIYQQQghxPjqlnGMhhBBCCCHOJxIcCyGEEEII4SXBsRBCCCGEEF4SHAshhBBCCOHV7YPjtWvXcs0115CTk4OiKCxbtixofXNzMw888AA9evTAbDYzcOBAXnrppaAyVquVuXPnkpqaSlxcHDNnzqSioiKozI4dOxg8eDDZ2dm8//77Z/q0RDfU1NTEQw89REFBAWazmfHjxwfNBKSqKk8++STZ2dmYzWamTp1KcXFx0D5KSkoYP348aWlp/OEPf/Avv/nmm5k+fXpQ2RUrVqAoCgsXLgxavnDhQvLz80//CYrzwuLFi+nZsycmk4mxY8fy5Zdf+tc1NDTwne98h+TkZH760592YS3FueBMvucB9OzZE0VRwn4WLVp0Vs5PdE/nRFx32iekPs0+/vhj9ec//7n6/vvvq4D6wQcfBK2/55571N69e6urV69WDx06pP75z39WtVqt+uGHH/rL3HfffWpeXp66atUq9euvv1Yvvvhidfz48UH7mTBhgvrqq6+qn3/+uZqXl6c2NDScjdMT3cjs2bPVgQMHqp999plaXFysLliwQE1ISFBLSkpUVVXVRYsWqYmJieqyZcvU7du3q9dee63aq1cvtbW11b+P73//++rTTz+tbt26Ve3Tp49aXFysqqqqvvTSS2pcXJzqcDj8ZefNm6fm5eWpkyZNCqrHpZdeqv7gBz848ycszjlLly5VDQaD+uqrr6o7d+5U77nnHjUpKUmtqKhQVVVVf/7zn6v33XefumvXLnXUqFHqp59+2sU1Ft3ZmXzPU1VVLSgoUJ966im1rKws6Ke5ufmsn6voPs6FuK7bB8eBIj2JgwYNUp966qmgZSNGjFB//vOfq6qqqvX19aper1ffffdd//rdu3ergLphwwb/soKCAv/fs2bNUr/88svTfwKi27JYLKpWq1X/8Y9/BC33XUtut1vNyspSf/vb3/rX1dfXq0ajUf3b3/7mXzZgwAC1paVFVVVVfeyxx9R33nlHVVVV3bt3b9g1N2bMGHXx4sWqyWTyf9i0traqRqNRfe21187UqYpz2JgxY9S5c+f6H7tcLjUnJ0f99a9/raqqqs6YMUPduXOnqqqqunjxYvXZZ5/tknqK7u9Mv+epqudz9Xe/+92ZPRFxTuuucV23T6voyPjx41m+fDmlpaWoqsrq1avZt28fV155JQCbN2/G4XAwdepU/zb9+/cnPz+fDRs2+JfFx8ezfv16Kisr2bx5MwUFBWf9XETXcTqduFyusIlqzGYzn3/+OYcOHaK8vDzoOkpMTGTs2LFB11GvXr1Yvnw5zc3NrF69mj59+gBQVFRETk4Oq1evBjy3M7ds2cKsWbPo2bOnfx9ffPEFNpuNyZMnn+lTFucYu93O5s2bg65BjUbD1KlT/deP7/qz2+2sWLHCf/0JEepMv+cJcbK6Q1x3zgfHL774IgMHDqRHjx4YDAamT5/O4sWLufTSSwEoLy/HYDCQlJQUtF1mZibl5eX+x88++yzTpk0jNzeX++67j4yMjLN5GqKLxcfHM27cOJ5++mmOHz+Oy+XizTffZMOGDZSVlfmvlczMzKDtQq+jp59+mgcffJCkpCQuvvhihg8f7l83efJk1qxZA8C6desoKioiPT2dSy+91L98zZo19OrVS76ciTDV1dW4XK52r8F58+bx8ssvYzab0Wg0XHfddV1RVXEOOBvveQA/+clPiIuLC/pZt27dmT9Bcc7qDnHdSU0f3Z28+OKLbNy4keXLl1NQUMDatWuZO3cuOTk5Qd8qOjJjxgyqq6ux2WwkJiaewRqL7uqNN97gzjvvJDc3F61Wy4gRI7jlllvYvHlzp/cxYsQISktLaWpqIiUlJWjdZZddxkMPPYTD4WDNmjVcdtllAEyaNIk///nPgCc4llZjcbIKCgooLi6mtraWtLS0rq6O6ObO9HsewGOPPcYdd9wRtCw3N/dUqy7OY90hrjung+PW1lZ+9rOf8cEHH3DVVVcBMGTIELZt28Zzzz3H1KlTycrKwm63U19fH/Qto6KigqysrKD9mUymsFtM4sLRu3dvPvvsM1paWmhsbCQ7O5ubbrqJwsJC/7VSUVFBdna2f5uKigqGDRsWtB+9Xh/xQ2Ly5Mm0tLTw1VdfsXr1ah577DHAExzfeeed1NbWsmnTJn74wx+euZMU56y0tDS0Wm1Yj+zQ9zKNRiOBseiUM/2eB57rVlItRGd1l7junE6rcDgcOBwONJrg09BqtbjdbgBGjhyJXq9n1apV/vV79+7l6NGjjBs37qzWV5wbYmNjyc7Opq6ujpUrV3LdddfRq1cvsrKygq6jxsZGNm3a1OnrqHfv3uTl5bF8+XK2bdvGpEmTAE8rSm5uLs8//zx2u11ajkVEBoOBkSNHBl2DbrebVatWyXuZOCVn6j1PiBPVXeK6bt9y3NzczP79+/2PDx06xLZt20hJSSE/P59Jkybx2GOPYTabKSgo4LPPPuOvf/0r//M//wN4OhDcddddPPzww6SkpJCQkMCPfvQjxo0bx8UXX9xVpyW6oZUrV6KqKv369WP//v089thj9O/fnzlz5qAoCg899BC//OUv6du3L7169WL+/Pnk5ORw/fXXd/oYkydP5o9//CN9+vQJyuWbNGkSL774or/jnhCRPPzww9x+++2MGjWKMWPG8MILL9DS0sKcOXO6umriHHQ23vOampqC8kABYmJiSEhIOM1nI84V50Rc1+lxLbrI6tWrVSDs5/bbb1dVVVXLysrUO+64Q83JyVFNJpPar18/9fnnn1fdbrd/H62trep//dd/qcnJyWpMTIx6ww03qGVlZV10RqK7evvtt9XCwkLVYDCoWVlZ6ty5c9X6+nr/erfbrc6fP1/NzMxUjUajevnll6t79+49oWO89tprKqDed999QcuXLFmiAuoPf/jD03Iu4vz14osvqvn5+arBYFDHjBmjbty4saurJM5RZ/o9r6CgIOLnt7zPXdjOhbhOUVVVPT1hthBCCCGEEOe2czrnWAghhBBCiNNJgmMhhBBCCCG8JDgWQgghhBDCS4JjIYQQQgghvCQ4FkIIIYQQwkuCYyGEEEIIIbwkOBZCCCGEEMJLgmMhhBBCCCG8JDgWQgghhBDCS4JjIYQQQgghvCQ4FkIIIYQQwkuCYyGEEEIIIbwkOBZCCCGEEMJLgmMhhBBCCCG8JDgWQgghhBDCS4JjIYQQQgghvCQ4FkIIIYQQwkuCYyGEEEIIIbwkOBZCCCGEEMJLgmMhhBBCCCG8JDgWQgghhBDCS4JjIYQQQgghvCQ4FkIIIYQQwkuCYyGEEEIIIbx0XV2B84Gqqnz55ZesWLECu93e1dURQgghhDijcnJymDlzJllZWV1dldNOUVVV7epKnItUVWX79u1M+8486mu24HJZiE8ciFZrRlG8hXx/eH/7lwOcQBlFE7gQCH0coUzYNkHH8j0MfhyxHr4/Au4xKEQuE1h3JcoxlICD+eroXxKyTfD+Itc96Pny1UsTfFpKQKFodY5YhtAygUeKvH3oubRXJtIxlZAFmgjHbPd5IvK60G0jHTOsLJHKtnfMkMeEFwqrH2FFIpQN31/UdZHqhe8tTg15DChqyGbtlYm2n7ay/mVRtol4LKWd/bRzLP8x/IsilAl9e1dDygStV4N+RS7jW9TefjpzrJMo015ZNVrZdvYTuk2kfXdqPyeyvwhlQs/LHX5I3GrQ5r7HBB0q8jHUiMeMtk2k/XVmP5G3Cd4uyjFpu56ilVUjHDO0PmqEukc9duA+w17O6M9T2P4ANeR18z2OWDbKZXCql8zJlIlYthNlAosex04xrfTHzI//7/d897vfJTU1lfOBBMcnaM+ePUy+/BHqajfjsNeRmDSE5NSRxCcOQKPRAwFBn9YXWGqCH59kGX/Aq9V0WCZwP/59n6b9Kd79abzrNBHKhK5r+912LI02ZPso20Qqqwk5p6B1YWXDy/jPQRu9jP+3El5G20GZ4P15t2mvTJR12k6UaW8/2nbOwVcvTUggrlEi7M93qUQI2tvKtLef9su2t722E/Vqb3+K91NLUTy/Nbj8ZXzLTqaM4i2jUdqimWhllIAyGtrfX+A+2yvjX6b6PpVDfkPAJ5p3mbu9sqFl1Khl1Gj7i7RdaNlI63zbRNxfyPahv6EtWGy3TOg6NWqZsPM7kWOd5DH9x3apwWUBvMtU3zJfGVfbfsLW+QJqV4RjRt1fQPDoDj5GWNmIx+i4XmroNgHL3KFlQp+TCNv7tw14vkLXRT6m+8T3E/q8RVjn9u+HoN9By6JcQpGWdaaML4w7kf2car18anGwiWY20MRhrFxELA//9U9cd911JCQkcK6StIpOOHz4MBMmPkRd7WasreUkJA0iO/cqEpIGo9Uau7p6QgghhBBnXQp6ZpDMDJKpxMFGmnjiB/dwF7czjFgee+dVrrrqKmJiYrq6qidEguMojh8/zphxP6auZjOtlqPEJ/QjPWsKSUlD0OrMXV09IYQQQohuIwM915LCtaRQio2NNPHj2d/nNpyMJI7Hl/8/rrzySozG7t+oKMFxgOrqat577z0efex5mpsPEBfXh9T0i0lKvg+dPq6rqyeEEEII0e3lYmQmRr5LKke8gfKd134XC25GE8fP//13Jk+ejE7XPcPQ7lmrs6ihoYFly5bxox8/S3PjXmJiC0hOGUnP3nPQGxK7unpCCCGEEOckBYWemOiJiZtIYz9WNtLE7Ctn4AbGEMeTaz9iwoQJQX2SutoFGRy3tLTwj3/8gx/e/2sa63diMmeTnDqS/J43YzCeHz0thRBCCCG6CwWFvpjpi5nvk84eWtlAE9+59DL0aLiYeH7x5UpGjRoVNJpTV7hggmObzcaKFSu4486naazfgcGQQnLqKHJ6XIfJnNnV1RNCCCGEuCBoUBhIDAOJ4XYy2ImFDTQxaczFxKNlHPE8/c0aBg8e3CWB8nkdHDscDj799FO+d+tCGuq2o9XFkpwykqKBj2Iy53T5NxMhhBBCiAuZDoWhxDKUWOy4+cYbKI8eMpQ09Iwjnmf2fkFRUdFZrNN5qmjgwxwqfhlVdZGSPo7e/X5ETGy+BMRCCCGEEN2QAQ2jiGMUcVhxs5UW1tNIv3796IuJfWrrWalH98l+Ps2Wv38vsXG9cLsdNDfup7mpGIe9rqurJYQQQggh2uFG5RBWdmNhP1aS0FLE2RtG97xtOe7fvz/1dd9QV1fHsmXL+PFDv+V4yYfExvYkOXUkScnDZTQKIYQQQohuQEXlIFY20MRGmnDiGc3iozWruOSSS9BqtWetLudtcOyTnJzMnDlzmDNnDpWVlbz33nvM+8n/UHL0PeLi+5KcMpKklGHodLFdXVUhhBBCiAuGisox7GygiQ000YyL0cTxtxX/ZMqUKej1+i6p13kfHAfKyMjg/vvv5/7776ekpISLJzxETdV6So68TXzCAJJTR5KYPASt1tTVVRVCCCGEOC8dx85Gb0Bcg4MRxPHysneZPn16t5hB74IKjgP16NGDkiN/B+DgwYNcMum/qSj7hKOH3iIxaRBJqSNJTBqMRmPo4poKIYQQQpzbqnCw0ZsyUYKdYcTwP0v/ytVXX01sbPe6e3/BBseBCgsLOX7sQwB27drF5Vc8SlnJRxw9+CaJyUNIThlJfOIANBp5uoQQQgghOqMOJ5u8AfFBrAwihoVL/sz1119PYmL37fcl0V6IgQMHUlb6Maqqsm3bNqZf9ROOHXkbt8tKUspwklJGEp/QF0U5e4nhQgghhBDngiZcfOVNmdhLK0WYeeSlF5g5cyZpaWldXb1OkeA4CkVRGD58OBXH/42qqmzcuJHrbvgZRw6+DqpKUspwklNHEhtXiKKctyPiCSGEEEK0y4KLzbSwgUa+xUIhJua+sIhZs2aRk5PT1dU7YRIcd4KiKIwbN47K8tW4XC7Wrl3LrJvmc3Df/6HR6klOGUlyykjMMsmIEEIIIS4ANu8kHRtoYjst5GLg/t88xezZs+nZs2dXV++UKKqqql1diXOVw+Hgk08+4dbbfkF93Xa0WhNanQkICZAjxcuK71fnywZs1GGRoELR4vUIZaPG9kr4g3a/B3Ti/KLVOdIphBeNfvCwVUHHjFyfiHtTlHbWRdsu+nPT/nmdSL2ibBNYJNrTHqFiYUtO5NKJcNBOXBYneayOr9P2v5qq4WWU4HVti9t7WwwtG32d/3HE84xSNuK+O1+fiOvCirSzTac+EqLtt519d2a/7ZbpxDHbO89ou27vmCezrt1t2qlMp9ZFLxv1sJH22/GlF7AuZGWksu3UPaxenTjP8CIdPyeRzz/6NRNe/kTKRl95UpdM1Aen4ZI5mf1FWBCp6tU4SPVN77xnPf369YtQ6twkwfFpYrVa2bBhA3a7vaurIroBh8PB0qVLufnmm7tsnEbRvcg1IULJNSFCnUvXRE5ODoMHDz4v75hLcCzEGdDY2EhiYiINDQ0kJCR0dXVENyDXhAgl14QIJddE9yA9yYQQQgghhPCS4FgIIYQQQggvCY6FEEIIIYTwkuBYiDPAaDSyYMGCbjFHvOge5JoQoeSaEKHkmugepEOeEEIIIYQQXtJyLIQQQgghhJcEx0IIIYQQQnhJcCyEEEIIIYSXBMdCCCGEEEJ4SXAsRASLFy+mZ8+emEwmxo4dy5dfftlu+TVr1jBixAiMRiN9+vRhyZIlYWXeffdd+vfvj8lk4qKLLuLjjz8OWr9w4UIURQn66d+//+k8LXEKTvc1sXPnTmbOnEnPnj1RFIUXXnjhtBxXnD1dcU3I+0T3drqviZdffpmJEyeSnJxMcnIyU6dOjbhPeZ84vSQ4FiLE22+/zcMPP8yCBQvYsmULQ4cOZdq0aVRWVkYsf+jQIa666iomT57Mtm3beOihh7j77rtZuXKlv8wXX3zBLbfcwl133cXWrVu5/vrruf766/n222+D9jVo0CDKysr8P59//vkZPVfROWfimrBYLBQWFrJo0SKysrJOy3HF2dNV1wTI+0R3dSauiTVr1nDLLbewevVqNmzYQF5eHldeeSWlpaUnfVzRCaoQIsiYMWPUuXPn+h+7XC41JydH/fWvfx2x/Lx589RBgwYFLbvpppvUadOm+R/Pnj1bveqqq4LKjB07Vv3hD3/of7xgwQJ16NChp+EMxOl2Jq6JQAUFBervfve7Uz6uOHu66pqQ94nu60xfE6qqqk6nU42Pj1dff/31kz6u6Ji0HAsRwG63s3nzZqZOnepfptFomDp1Khs2bIi4zYYNG4LKA0ybNi2ofGfKABQXF5OTk0NhYSHf//73OXr06KmekjhFZ+qaOBPHFWdHV10TPvI+0f2crWvCYrHgcDhISUk56eOKjklwLESA6upqXC4XmZmZQcszMzMpLy+PuE15eXnE8o2NjbS2trZbJnCfY8eOZcmSJaxYsYI//elPHDp0iIkTJ9LU1HQ6Tk2cpDN1TZyJ44qzo6uuCZD3ie7qbF0TP/nJT8jJyfEHw/I+cWbouroCQgiPGTNm+P8eMmQIY8eOpaCggHfeeYe77rqrC2smhOgu5H3iwrVo0SKWLl3KmjVrMJlMXV2d85oEx0IESEtLQ6vVUlFREbS8oqIiageZrKysiOUTEhIwm83tlmmv001SUhJFRUXs37//ZE5FnCZn6po4E8cVZ0dXXRORyPtE93Cmr4nnnnuORYsW8cknnzBkyJBTOq7omKRVCBHAYDAwcuRIVq1a5V/mdrtZtWoV48aNi7jNuHHjgsoD/Oc//wkq35kyoZqbmzlw4ADZ2dkncyriNDlT18SZOK44O7rqmohE3ie6hzN5TTz77LM8/fTTrFixglGjRp3ycUUndHWPQCG6m6VLl6pGo1FdsmSJumvXLvXee+9Vk5KS1PLyclVVVfXxxx9Xb7vtNn/5gwcPqjExMepjjz2m7t69W128eLGq1WrVFStW+MusX79e1el06nPPPafu3r1bXbBggarX69UdO3b4yzzyyCPqmjVr1EOHDqnr169Xp06dqqalpamVlZVn7+RFRGfimrDZbOrWrVvVrVu3qtnZ2eqjjz6qbt26VS0uLu70cUXX6aprQt4nuq8zcU0sWrRINRgM6t///ne1rKzM/9PU1NTp44oTJ8GxEBG8+OKLan5+vmowGNQxY8aoGzdu9K+7/fbb1UmTJgWVX716tTps2DDVYDCohYWF6muvvRa2z3feeUctKipSDQaDOmjQIPWf//xn0PqbbrpJzc7OVg0Gg5qbm6vedNNN6v79+8/E6YmTcLqviUOHDqlA2E/ofto7ruhaXXFNyPtE93a6r4mCgoKI18SCBQs6fVxx4hRVVdUuaLAWQgghhBCi25GcYyGEEEIIIbwkOBZCCCGEEMJLgmMhhBBCCCG8JDgWQgghhBDCS4JjIYQQQgghvCQ4FkIIIYQQwkuCYyGEEEIIIbwkOBZCCCGEEMJLgmMhhIhAURSWLVvmf7xnzx4uvvhiTCYTw4YN4/DhwyiKwrZt205q/2vWrEFRFOrr609LfU/3sZcsWUJSUpL/8cKFCxk2bNhpr8vChQtRFAVFUXjhhRdO+/59fMcIPCchhIhEgmMhRLdSVVXF/fffT35+PkajkaysLKZNm8b69evPaj3KysqYMWOG//GCBQuIjY1l7969rFq1iry8PMrKyhg8ePBZrdfpMH78eMrKykhMTOz0No8++iirVq3yP77jjju4/vrrT0t9Bg0aRFlZGffee69/Wc+ePVEUhY0bNwaVfeihh7jsssvC9lFSUoLBYIj6epSVlZ3R4FsIcf6Q4FgI0a3MnDmTrVu38vrrr7Nv3z6WL1/OZZddRk1NzVmtR1ZWFkaj0f/4wIEDXHLJJRQUFJCamopWqyUrKwudTndW63U6GAwGsrKyUBSl09vExcWRmpp6Ruqj0+nIysoiJiYmaLnJZOInP/lJp/axZMkSZs+eTWNjI5s2bQpbn5WVdUJfBoQQFy4JjoUQ3UZ9fT3r1q3jN7/5DZMnT6agoIAxY8bw05/+lGuvvdZfTlEU/vSnPzFjxgzMZjOFhYX8/e9/D9rXsWPHmD17NklJSaSkpHDddddx+PDhoDKvvvoqgwYNwmg0kp2dzQMPPBB0DF9ahaIobN68maeeegpFUVi4cGHEtIqdO3dy9dVXk5CQQHx8PBMnTuTAgQPtnvPmzZsZNWoUMTExjB8/nr179/rXRWqdDW05veyyy/jRj37EQw89RHJyMpmZmbz88su0tLQwZ84c4uPj6dOnD//617/820RKq1iyZAn5+fnExMRwww03hH0ZCUyrWLhwIa+//joffvihP11hzZo1TJkyJeg5BM+dAIPBENTq3Fn33nsvGzdu5OOPP263nKqqvPbaa9x2221873vf45VXXjnhYwkhhI8Ex0KIbiMuLo64uDiWLVuGzWZrt+z8+fOZOXMm27dv5/vf/z4333wzu3fvBsDhcDBt2jTi4+NZt24d69evJy4ujunTp2O32wH405/+xNy5c7n33nvZsWMHy5cvp0+fPhGPVVZWxqBBg3jkkUcoKyvj0UcfDStTWlrKpZdeitFo5NNPP2Xz5s3ceeedOJ3Ods/j5z//Oc8//zxff/01Op2OO++8szNPVZDXX3+dtLQ0vvzyS370ox9x//33M2vWLMaPH8+WLVu48sorue2227BYLBG337RpE3fddRcPPPAA27ZtY/Lkyfzyl7+MerxHH32U2bNnM336dMrKyigrK2P8+PHcfffdvPXWW0Gv3Ztvvklubi5Tpkw54fPq1asX9913Hz/96U9xu91Ry61evRqLxcLUqVO59dZbWbp0KS0tLSd8PCGEAAmOhRDdiE6nY8mSJbz++uskJSUxYcIEfvazn/HNN9+ElZ01axZ33303RUVFPP3004waNYoXX3wRgLfffhu3281f/vIXLrroIgYMGMBrr73G0aNHWbNmDQC//OUveeSRR/jxj39MUVERo0eP5qGHHopYL1/6RFxcHFlZWcTFxYWVWbx4MYmJiSxdupRRo0ZRVFTEnDlz6NevX7vn/MwzzzBp0iQGDhzI448/zhdffIHVaj2h523o0KE88cQT9O3bl5/+9KeYTCbS0tK455576Nu3L08++SQ1NTURn0eA3//+90yfPp158+ZRVFTEgw8+yLRp06IeLy4uDrPZ7M8Jz8rKwmAw8N3vfheADz/80F92yZIl3HHHHSeUwhHoiSee4NChQ/y///f/opZ55ZVXuPnmm9FqtQwePJjCwkLefffdkzqeEEJIcCyE6FZmzpzJ8ePHWb58OdOnT2fNmjWMGDGCJUuWBJUbN25c2GNfy/H27dvZv38/8fHx/tbolJQUrFYrBw4coLKykuPHj3P55Zeftnpv27aNiRMnotfrT2i7IUOG+P/Ozs4GoLKy8qT3odVqSU1N5aKLLvIvy8zMbHe/u3fvZuzYsUHLQp/fzjCZTNx22228+uqrAGzZsoVvv/2WO+6444T35ZOens6jjz7Kk08+6W/1D1RfX8/777/Prbfe6l926623SmqFEOKknXs9SYQQ5z2TycQVV1zBFVdcwfz587n77rtZsGBBp4Os5uZmRo4cGbG1MT09HY3m9LcLmM3mk9ouMJj2ta76Ugg0Gg2qqgaVdzgc7e7Dt5/29nsm3X333QwbNoySkhJee+01pkyZQkFBwSnt8+GHH+aPf/wjf/zjH8PWvfXWW1it1qDgXlVV3G43+/bto6io6JSOLYS48EjLsRCi2xs4cGBYDmnoEF8bN25kwIABAIwYMYLi4mIyMjLo06dP0E9iYiLx8fH07NnzpDqJRTNkyBDWrVsXMXg9Wenp6ZSVlQUtO9lxldszYMCAsBEeQp/fUAaDAZfLFbb8oosuYtSoUbz88su89dZbJ5VDHSouLo758+fzzDPP0NTUFLTulVde4ZFHHmHbtm3+n+3btzNx4kR/C7YQQpwICY6FEN1GTU0NU6ZM4c033+Sbb77h0KFDvPvuuzz77LNcd911QWXfffddXn31Vfbt28eCBQv48ssv/SMlfP/73yctLY3rrruOdevWcejQIdasWcODDz5ISUkJ4Blx4fnnn+d///d/KS4uZsuWLf6c5ZPxwAMP0NjYyM0338zXX39NcXExb7zxRtDoEydqypQpfP311/z1r3+luLiYBQsW8O233570/qJ58MEHWbFiBc899xzFxcX84Q9/YMWKFe1u07NnT7755hv27t1LdXV10JeCu+++m0WLFqGqKjfccMNpqeO9995LYmIib731ln/Ztm3b2LJlC3fffTeDBw8O+rnlllt4/fXXO+wQKYQQoSQ4FkJ0G3FxcYwdO5bf/e53XHrppQwePJj58+dzzz338Ic//CGo7C9+8QuWLl3KkCFD+Otf/8rf/vY3Bg4cCEBMTAxr164lPz+f7373uwwYMIC77roLq9VKQkICALfffjsvvPACf/zjHxk0aBBXX301xcXFJ1331NRUPv30U5qbm5k0aRIjR47k5ZdfPuEc5EDTpk1j/vz5zJs3j9GjR9PU1MQPfvCDk95fNBdffDEvv/wyv//97xk6dCj//ve/eeKJJ9rd5p577qFfv36MGjWK9PT0oElabrnlFnQ6Hbfccgsmk+m01FGv1/P0008HdVZ85ZVXGDhwIP379w8rf8MNN1BZWdnhMHBCCBFKUUMT2oQQoptTFIUPPvjgtM3QJk6vw4cP07t3b7766itGjBjRbtmFCxeybNmyM5IuEmrJkiU89NBDXTJltxDi3CEtx0IIIU4Lh8NBeXk5TzzxBBdffHGHgbHPjh07iIuLi9jh7nSJi4vjvvvuO2P7F0KcP2S0CiGEEKfF+vXrmTx5MkVFRWEzFkbz4IMP+odhS09PP2N187VMa7XaM3YMIcT5QdIqhBBCCCGE8JK0CiGEEEIIIbwkOBZCCCGEEMJLgmMhhBBCCCG8JDgWQgghhBDCS4JjIYQQQgghvCQ4FkIIIYQQwkuCYyGEEEIIIbwkOBZCCCGEEMLr/wOBCllCnbxnNgAAAABJRU5ErkJggg=="
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 6
+    },
+    {
+      "id": "7106bc6e-e25e-412c-8aff-5692134155c6",
+      "cell_type": "markdown",
+      "source": "Zonal and Meridional wind can be visualized in the same way.\n\n\nThe precipitation flux on **2020-03-31 at 00:00:00 UTC** is plotted as demonstrated below.\n",
+      "metadata": {}
     },
     {
-     "data": {
-      "text/plain": [
-       "(<GeoAxesSubplot:title={'right':'2020-03-31T00:00:00'}>,\n",
-       " [<cartopy.mpl.geocollection.GeoQuadMesh at 0x14f8a5240>,\n",
-       "  <matplotlib.colorbar.Colorbar at 0x149351540>])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+      "id": "b0371f7d-f7bf-4dd7-be17-830de8e24db9",
+      "cell_type": "code",
+      "source": "da_pr = ds.pr.isel(time=89)\nda_pr.pyic.plot()",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "execution_count": 7,
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": "(<GeoAxesSubplot:title={'right':'2020-03-31T00:00:00'}>,\n [<cartopy.mpl.geocollection.GeoQuadMesh at 0xed69490>,\n  <matplotlib.colorbar.Colorbar at 0x103cf850>])"
+          },
+          "metadata": {}
+        },
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 534.6MiB (from 445.5MiB) for this notebook\n",
+          "output_type": "stream"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": "<Figure size 751.967x480.314 with 2 Axes>",
+            "image/png": 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"
+          },
+          "metadata": {}
+        }
+      ],
+      "execution_count": 7
     },
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 751.967x480.314 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+      "id": "9f820694-e442-487d-981a-4fbd238a806e",
+      "cell_type": "code",
+      "source": "",
+      "metadata": {
+        "trusted": true
+      },
+      "outputs": [],
+      "execution_count": null
     }
-   ],
-   "source": [
-    "da_pr = ds.pr.isel(time=89)\n",
-    "da_pr.pyic.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f820694-e442-487d-981a-4fbd238a806e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+  ]
+}
\ No newline at end of file
diff --git a/04-example-datasets/04-ICONXPP.ipynb b/04-example-datasets/04-ICONXPP.ipynb
index ae3ae9f..4d5d2d4 100644
--- a/04-example-datasets/04-ICONXPP.ipynb
+++ b/04-example-datasets/04-ICONXPP.ipynb
@@ -71,7 +71,7 @@
       "outputs": [
         {
           "name": "stdout",
-          "text": "[pyodide]: Loading Jinja2, MarkupSafe, aiohttp, aiosignal, asciitree, async-timeout, attrs, cftime, click, cloudpickle, dask, frozenlist, fsspec, importlib_metadata, locket, msgpack, multidict, numcodecs, numpy, partd, propcache, pyyaml, toolz, yarl, zarr, zipp\n[pyodide]: Loaded Jinja2, MarkupSafe, aiohttp, aiosignal, asciitree, async-timeout, attrs, cftime, click, cloudpickle, dask, frozenlist, fsspec, importlib_metadata, locket, msgpack, multidict, numcodecs, numpy, partd, propcache, pyyaml, toolz, yarl, zarr, zipp\n[pyodide]: Memory usage has grown to 71.9MiB (from 49.9MiB) for this notebook\n",
+          "text": "[pyodide]: Memory usage has grown to 71.9MiB (from 49.9MiB) for this notebook\n",
           "output_type": "stream"
         }
       ],
@@ -86,14 +86,14 @@
     {
       "id": "0b393415-87ca-4dc7-8c72-4cdd101d6621",
       "cell_type": "code",
-      "source": "import json\n\nimport numpy as np\nimport requests\nimport xarray as xr\n\n\ndef load_ICONXPP_data(year=None, month=None):\n    \n    if not isinstance(year, int) or year < 1300 or year > 2300:\n        raise ValueError(\"Please specify a valid year as an integer between 1300 and 2300.\")\n\n    if year == 2300:\n        if not month:\n            print(f\"Warning: Only data for January 1st is available for 2300.\")\n        if month and month != 1:\n            raise ValueError(f\"Only data for January 1st is available for 2300.\")\n            \n    if month:\n        if not isinstance(month, int) or month < 1 or month > 12:\n            raise ValueError(f\"Please specify a valid month as an integer between 1 and 12.\")\n\n    file_year = (year // 10) * 10\n    if not year % 10 <= 4: \n        file_year += 5\n\n\n    if not month and year % 5 == 0:\n        if year == 1300:\n            print(f\"Warning: Data for January 1st of 1300 is not available as is is the simulation starting time.\")\n        else:\n            print(f\"Warning: Data for January 1st of '{year}' is stored in the previous file. Please specify the month to load the correct dataset.\")\n    elif month == 1 and year % 5 == 0:\n        if year == 1300:\n            raise ValueError(f\"Data for January 1st of 1300 is not available as this is the simulation starting time.\")\n        file_year -= 5\n        \n    if year == 2300:\n        file_year = 2295\n\n    url = f\"{BASE_URL}/ICON-XPP/slo1802_atm_2d_ml_{file_year}0101T000000Z.nc\"\n        \n    ref = requests.get(f\"{url}.ref\").json()\n\n    print(f\"Loading dataset {url}\")\n\n    ds = xr.open_dataset(\n        \"reference://\", \n        engine=\"zarr\", \n        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n        consolidated=False, \n        chunks=dict(),\n    )\n    ds.clon.values=np.degrees(ds.clon.values)\n    ds.clat.values=np.degrees(ds.clat.values)\n    \n    return ds\n    ",
+      "source": "import json\n\nimport numpy as np\nimport requests\nimport xarray as xr\n\n\ndef load_ICONXPP_data(year=None, month=None):\n    \n    if not isinstance(year, int) or year < 1300 or year > 2300:\n        raise ValueError(\"Please specify a valid year as an integer between 1300 and 2300.\")\n\n    if year == 2300:\n        if not month:\n            print(f\"Warning: Only data for January 1st are available for 2300.\")\n        if month and month != 1:\n            raise ValueError(f\"Only data for January 1st are available for 2300.\")\n            \n    if month:\n        if not isinstance(month, int) or month < 1 or month > 12:\n            raise ValueError(f\"Please specify a valid month as an integer between 1 and 12.\")\n\n    file_year = (year // 10) * 10\n    if not year % 10 <= 4: \n        file_year += 5\n\n\n    if not month and year % 5 == 0:\n        if year == 1300:\n            print(f\"Warning: Data for January 1st of 1300 are not available as is is the simulation starting time.\")\n        else:\n            print(f\"Warning: Data for January 1st of '{year}' are stored in the previous file. Please specify the month to load the correct dataset.\")\n    elif month == 1 and year % 5 == 0:\n        if year == 1300:\n            raise ValueError(f\"Data for January 1st of 1300 are not available as this is the simulation starting time.\")\n        file_year -= 5\n        \n    if year == 2300:\n        file_year = 2295\n\n    url = f\"{BASE_URL}/ICON-XPP/slo1802_atm_2d_ml_{file_year}0101T000000Z.nc\"\n        \n    ref = requests.get(f\"{url}.ref\").json()\n\n    print(f\"Loading dataset {url}\")\n\n    ds = xr.open_dataset(\n        \"reference://\", \n        engine=\"zarr\", \n        backend_kwargs={\"storage_options\": {\"fo\": ref}},\n        consolidated=False, \n        chunks=dict(),\n    )\n    ds.clon.values=np.degrees(ds.clon.values)\n    ds.clat.values=np.degrees(ds.clat.values)\n    \n    return ds\n    ",
       "metadata": {
         "trusted": true
       },
       "outputs": [
         {
           "name": "stdout",
-          "text": "[pyodide]: Loading pandas, python-dateutil, pytz, tzdata, xarray\n[pyodide]: Loaded pandas, python-dateutil, pytz, tzdata, xarray\n[pyodide]: Memory usage has grown to 124.2MiB (from 71.9MiB) for this notebook\n",
+          "text": "[pyodide]: Memory usage has grown to 149.1MiB (from 71.9MiB) for this notebook\n",
           "output_type": "stream"
         }
       ],
@@ -123,7 +123,7 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": "<xarray.Dataset> Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB 89.46 88.64 89.06 ... -27.32 -27.0\n    clon           (ncells) float64 655kB 72.0 50.25 72.0 ... 1.296 0.6204 0.357\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2020-02-01 ... 2025-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albnirdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    clat_bnds      (ncells, vertices) float64 2MB dask.array<chunksize=(81920, 3), meta=np.ndarray>\n    clct           (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqi_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv            (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    umfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    vmfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB 89.46 88.64 89.06 ... -27.32 -27.0\n    clon           (ncells) float64 655kB 72.0 50.25 72.0 ... 1.296 0.6204 0.357\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2020-02-01 ... 2025-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albnirdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    clat_bnds      (ncells, vertices) float64 2MB dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n    clct           (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqi_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv            (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    umfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    vmfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5b05dfb3-1690-4527-8e33-67d094ca8eb2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5b05dfb3-1690-4527-8e33-67d094ca8eb2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1d3be0d0-2c83-4ff1-a4c2-2ac282409846' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d3be0d0-2c83-4ff1-a4c2-2ac282409846' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.46 88.64 89.06 ... -27.32 -27.0</div><input id='attrs-dfd16620-2964-476a-9fcb-60d5fe07c4ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfd16620-2964-476a-9fcb-60d5fe07c4ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-895575a9-3e03-464c-8aa7-eac91e0c5e9a' class='xr-var-data-in' type='checkbox'><label for='data-895575a9-3e03-464c-8aa7-eac91e0c5e9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><pre>array([ 89.45889176,  88.63772156,  89.06223506, ..., -27.28616813,\n       -27.32237258, -27.0023716 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>72.0 50.25 72.0 ... 0.6204 0.357</div><input id='attrs-0548608a-cbe2-4add-9a9f-e1bb60db2337' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0548608a-cbe2-4add-9a9f-e1bb60db2337' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d2ce33f-e389-44e6-a162-af4bdc2fd892' class='xr-var-data-in' type='checkbox'><label for='data-9d2ce33f-e389-44e6-a162-af4bdc2fd892' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><pre>array([72.        , 50.25313351, 72.        , ...,  1.29630984,\n        0.62040403,  0.3569665 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-aea92e3e-ef9d-4176-82ba-5d68ebe16439' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aea92e3e-ef9d-4176-82ba-5d68ebe16439' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-372aed22-0d68-4a9a-a89c-778b27d421a3' class='xr-var-data-in' type='checkbox'><label for='data-372aed22-0d68-4a9a-a89c-778b27d421a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-b441b54b-7292-4409-be88-4f5af6c4f61a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b441b54b-7292-4409-be88-4f5af6c4f61a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24f2c299-1d8e-42ab-b39a-f2b21b0920d3' class='xr-var-data-in' type='checkbox'><label for='data-24f2c299-1d8e-42ab-b39a-f2b21b0920d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-01 ... 2025-01-01</div><input id='attrs-32bf4cbf-3a6f-414f-ab82-8628d0389e9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32bf4cbf-3a6f-414f-ab82-8628d0389e9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10f8de4b-89bc-4e31-801a-16eaa5b32002' class='xr-var-data-in' type='checkbox'><label for='data-10f8de4b-89bc-4e31-801a-16eaa5b32002' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;, &#x27;2020-03-01T00:00:00.000000000&#x27;,\n       &#x27;2020-04-01T00:00:00.000000000&#x27;, &#x27;2020-05-01T00:00:00.000000000&#x27;,\n       &#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-07-01T00:00:00.000000000&#x27;,\n       &#x27;2020-08-01T00:00:00.000000000&#x27;, &#x27;2020-09-01T00:00:00.000000000&#x27;,\n       &#x27;2020-10-01T00:00:00.000000000&#x27;, &#x27;2020-11-01T00:00:00.000000000&#x27;,\n       &#x27;2020-12-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n       &#x27;2021-02-01T00:00:00.000000000&#x27;, &#x27;2021-03-01T00:00:00.000000000&#x27;,\n       &#x27;2021-04-01T00:00:00.000000000&#x27;, &#x27;2021-05-01T00:00:00.000000000&#x27;,\n       &#x27;2021-06-01T00:00:00.000000000&#x27;, &#x27;2021-07-01T00:00:00.000000000&#x27;,\n       &#x27;2021-08-01T00:00:00.000000000&#x27;, &#x27;2021-09-01T00:00:00.000000000&#x27;,\n       &#x27;2021-10-01T00:00:00.000000000&#x27;, &#x27;2021-11-01T00:00:00.000000000&#x27;,\n       &#x27;2021-12-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:00:00.000000000&#x27;,\n       &#x27;2022-02-01T00:00:00.000000000&#x27;, &#x27;2022-03-01T00:00:00.000000000&#x27;,\n       &#x27;2022-04-01T00:00:00.000000000&#x27;, &#x27;2022-05-01T00:00:00.000000000&#x27;,\n       &#x27;2022-06-01T00:00:00.000000000&#x27;, &#x27;2022-07-01T00:00:00.000000000&#x27;,\n       &#x27;2022-08-01T00:00:00.000000000&#x27;, &#x27;2022-09-01T00:00:00.000000000&#x27;,\n       &#x27;2022-10-01T00:00:00.000000000&#x27;, &#x27;2022-11-01T00:00:00.000000000&#x27;,\n       &#x27;2022-12-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n       &#x27;2023-02-01T00:00:00.000000000&#x27;, &#x27;2023-03-01T00:00:00.000000000&#x27;,\n       &#x27;2023-04-01T00:00:00.000000000&#x27;, &#x27;2023-05-01T00:00:00.000000000&#x27;,\n       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;,\n       &#x27;2023-08-01T00:00:00.000000000&#x27;, &#x27;2023-09-01T00:00:00.000000000&#x27;,\n       &#x27;2023-10-01T00:00:00.000000000&#x27;, &#x27;2023-11-01T00:00:00.000000000&#x27;,\n       &#x27;2023-12-01T00:00:00.000000000&#x27;, &#x27;2024-01-01T00:00:00.000000000&#x27;,\n       &#x27;2024-02-01T00:00:00.000000000&#x27;, &#x27;2024-03-01T00:00:00.000000000&#x27;,\n       &#x27;2024-04-01T00:00:00.000000000&#x27;, &#x27;2024-05-01T00:00:00.000000000&#x27;,\n       &#x27;2024-06-01T00:00:00.000000000&#x27;, &#x27;2024-07-01T00:00:00.000000000&#x27;,\n       &#x27;2024-08-01T00:00:00.000000000&#x27;, &#x27;2024-09-01T00:00:00.000000000&#x27;,\n       &#x27;2024-10-01T00:00:00.000000000&#x27;, &#x27;2024-11-01T00:00:00.000000000&#x27;,\n       &#x27;2024-12-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e60a0b25-0c93-49b9-a4ac-08956a71eafb' class='xr-section-summary-in' type='checkbox'  ><label for='section-e60a0b25-0c93-49b9-a4ac-08956a71eafb' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-49e012d5-64b3-4b17-80aa-be5960b10426' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49e012d5-64b3-4b17-80aa-be5960b10426' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70b5393d-4b85-4e6e-b267-c4571df61697' class='xr-var-data-in' type='checkbox'><label for='data-70b5393d-4b85-4e6e-b267-c4571df61697' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1ea2d5ea-2eb1-4fbd-831c-310af230bb71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ea2d5ea-2eb1-4fbd-831c-310af230bb71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83d39e33-2c24-4a79-b4e3-1567ae916e5c' class='xr-var-data-in' type='checkbox'><label for='data-83d39e33-2c24-4a79-b4e3-1567ae916e5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-abc69030-7b3f-4f80-ade0-1a3bc9e2c20f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abc69030-7b3f-4f80-ade0-1a3bc9e2c20f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c800255-ca04-4796-8494-8185a597aa4a' class='xr-var-data-in' type='checkbox'><label for='data-7c800255-ca04-4796-8494-8185a597aa4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-cdabf300-cb48-4fe4-a0ad-028fe4e6f25f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdabf300-cb48-4fe4-a0ad-028fe4e6f25f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71ead9d8-155d-478e-932f-cb45f80bacc9' class='xr-var-data-in' type='checkbox'><label for='data-71ead9d8-155d-478e-932f-cb45f80bacc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  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class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-ad02fa19-59fd-48ee-b3a5-233979f365dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad02fa19-59fd-48ee-b3a5-233979f365dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-645f9b51-e9b9-453d-80a3-3f81d88835c9' class='xr-var-data-in' type='checkbox'><label for='data-645f9b51-e9b9-453d-80a3-3f81d88835c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a5f80d0f-6b14-457b-b1db-45f829324638' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5f80d0f-6b14-457b-b1db-45f829324638' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ad02202-feab-4d9d-9175-bdefaedc0c14' class='xr-var-data-in' type='checkbox'><label for='data-5ad02202-feab-4d9d-9175-bdefaedc0c14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-26d47575-2bec-4d2d-89e4-d196934dc254' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26d47575-2bec-4d2d-89e4-d196934dc254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb29a67f-cba9-4526-bafa-77d6d0aa974c' class='xr-var-data-in' type='checkbox'><label for='data-eb29a67f-cba9-4526-bafa-77d6d0aa974c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ebc7faf2-73b9-43cb-9c21-d99f5ed15a65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebc7faf2-73b9-43cb-9c21-d99f5ed15a65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-702b06f3-986a-464e-9bcf-c4bb605163d6' class='xr-var-data-in' type='checkbox'><label for='data-702b06f3-986a-464e-9bcf-c4bb605163d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-aad67f80-9688-41fe-989d-161a8d3761e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aad67f80-9688-41fe-989d-161a8d3761e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0ff0673-2305-48e5-b705-bde16903a30d' class='xr-var-data-in' type='checkbox'><label for='data-f0ff0673-2305-48e5-b705-bde16903a30d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320.00 kiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-545117ba-7d5c-46b6-b223-ac559185139b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-545117ba-7d5c-46b6-b223-ac559185139b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6efbad31-38f3-4650-ae84-faa98f332067' class='xr-var-data-in' type='checkbox'><label for='data-6efbad31-38f3-4650-ae84-faa98f332067' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-999035e0-ee7c-4b38-83a3-1cc0d47b9c3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-999035e0-ee7c-4b38-83a3-1cc0d47b9c3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93b1f0d0-31c5-474f-9f23-bac672a61171' class='xr-var-data-in' type='checkbox'><label for='data-93b1f0d0-31c5-474f-9f23-bac672a61171' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-7a111dcf-0b8c-46d4-bedd-077085722474' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a111dcf-0b8c-46d4-bedd-077085722474' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57ffdbfd-19b3-4200-a247-1cb80b4fd3f5' class='xr-var-data-in' type='checkbox'><label for='data-57ffdbfd-19b3-4200-a247-1cb80b4fd3f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-db220078-c98b-4f87-912c-cef180abf63d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db220078-c98b-4f87-912c-cef180abf63d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-324857a5-8264-4bc5-b9b6-34a43a121240' class='xr-var-data-in' type='checkbox'><label for='data-324857a5-8264-4bc5-b9b6-34a43a121240' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1952e0ac-e226-45f5-9399-c1431cc278a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1952e0ac-e226-45f5-9399-c1431cc278a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d7747d9-24a6-435b-9838-7e0da3a3ceb1' class='xr-var-data-in' type='checkbox'><label for='data-9d7747d9-24a6-435b-9838-7e0da3a3ceb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8f8ac19d-b82f-4cb8-9558-bfd9594d9ceb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f8ac19d-b82f-4cb8-9558-bfd9594d9ceb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6225e564-a3e3-4037-bfa8-e96aca0d5374' class='xr-var-data-in' type='checkbox'><label for='data-6225e564-a3e3-4037-bfa8-e96aca0d5374' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ec311409-a637-4206-b9b7-0acf6f336f86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec311409-a637-4206-b9b7-0acf6f336f86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d794d907-9af8-4728-b543-eed59f2d0fd2' class='xr-var-data-in' type='checkbox'><label for='data-d794d907-9af8-4728-b543-eed59f2d0fd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-16f8868f-ff91-4bbb-80cd-b7c1b9268bad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16f8868f-ff91-4bbb-80cd-b7c1b9268bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70e924fb-c3ad-4903-b0b3-ffa48658ccb8' class='xr-var-data-in' type='checkbox'><label for='data-70e924fb-c3ad-4903-b0b3-ffa48658ccb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f62ecdb7-da56-4d20-ae20-fdc257f8cf43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f62ecdb7-da56-4d20-ae20-fdc257f8cf43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cebdbbc8-7084-4bad-a1e8-6d7d05d70bcc' class='xr-var-data-in' type='checkbox'><label for='data-cebdbbc8-7084-4bad-a1e8-6d7d05d70bcc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-35b3e04c-d2ac-473c-8ee9-685e1d768a76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35b3e04c-d2ac-473c-8ee9-685e1d768a76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd26d3ad-0ea8-4cb7-b177-3c57ddf9bc93' class='xr-var-data-in' type='checkbox'><label for='data-bd26d3ad-0ea8-4cb7-b177-3c57ddf9bc93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-20912c7d-b1bf-46ac-8069-0cd8b758a6c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20912c7d-b1bf-46ac-8069-0cd8b758a6c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e74b62b-efcf-4217-bfe2-282c9f3702f1' class='xr-var-data-in' type='checkbox'><label for='data-0e74b62b-efcf-4217-bfe2-282c9f3702f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a7fa2688-48c6-467f-b48a-fb13c635a7e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7fa2688-48c6-467f-b48a-fb13c635a7e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a9eae5a-fe5c-49e2-a138-2d136b5ee258' class='xr-var-data-in' type='checkbox'><label for='data-1a9eae5a-fe5c-49e2-a138-2d136b5ee258' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-467af929-a783-41d3-a30b-c7f8ec1fe6f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-467af929-a783-41d3-a30b-c7f8ec1fe6f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa15a1c9-ea01-4bed-9d63-986106ba544e' class='xr-var-data-in' type='checkbox'><label for='data-aa15a1c9-ea01-4bed-9d63-986106ba544e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ebd8578f-350b-42d6-b4b2-dcd32f1f36ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebd8578f-350b-42d6-b4b2-dcd32f1f36ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35e70693-4462-4ab6-a015-98e05b2adde5' class='xr-var-data-in' type='checkbox'><label for='data-35e70693-4462-4ab6-a015-98e05b2adde5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-7bd05a47-c5fd-4ea1-ad50-dc7f9c9a2546' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bd05a47-c5fd-4ea1-ad50-dc7f9c9a2546' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be2516b2-7f41-48c4-9c96-83b6c1c289e6' class='xr-var-data-in' type='checkbox'><label for='data-be2516b2-7f41-48c4-9c96-83b6c1c289e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-db5eeb27-3252-407a-83dd-b67b55ed2050' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db5eeb27-3252-407a-83dd-b67b55ed2050' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-823df9ee-abde-433f-8c18-019275228dee' class='xr-var-data-in' type='checkbox'><label for='data-823df9ee-abde-433f-8c18-019275228dee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-605ef7cf-191f-4a88-961a-9a5a3150dd50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-605ef7cf-191f-4a88-961a-9a5a3150dd50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-822d5616-57e9-4e3d-a968-c4481aec52d3' class='xr-var-data-in' type='checkbox'><label for='data-822d5616-57e9-4e3d-a968-c4481aec52d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp_10m</span></div><div class='xr-var-dims'>(time, height, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-6595553f-5cde-42b4-b655-66d9d5b99154' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6595553f-5cde-42b4-b655-66d9d5b99154' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d73fffd-280a-466d-beea-adfb24f1f7d0' class='xr-var-data-in' type='checkbox'><label for='data-5d73fffd-280a-466d-beea-adfb24f1f7d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>wind speed in 10m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.0</dd><dt><span>standard_name :</span></dt><dd>sp_10m</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"26\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"26\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"27\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"28\" />\n  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"29\" />\n  <line x1=\"15\" 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Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-30dcf2ad-44c9-4666-98a8-a6bf2309f997' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30dcf2ad-44c9-4666-98a8-a6bf2309f997' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87f68f5c-c917-4f62-b24c-98ab009228f0' class='xr-var-data-in' type='checkbox'><label for='data-87f68f5c-c917-4f62-b24c-98ab009228f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- 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transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3261370a-7345-4ac1-aa51-9cdb54d5987b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3261370a-7345-4ac1-aa51-9cdb54d5987b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1750de10-2ddf-406b-b94c-ac0e785dd61e' class='xr-var-data-in' type='checkbox'><label for='data-1750de10-2ddf-406b-b94c-ac0e785dd61e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-9b84e28f-450b-407a-ae52-65a2a9689e78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b84e28f-450b-407a-ae52-65a2a9689e78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-829acbd2-0244-4319-b7c7-6b2d0ec454a9' class='xr-var-data-in' type='checkbox'><label for='data-829acbd2-0244-4319-b7c7-6b2d0ec454a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-768a206f-780e-4f49-8ac8-d73e6a348c49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-768a206f-780e-4f49-8ac8-d73e6a348c49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-512b6e89-0efe-461c-912e-34da60682bda' class='xr-var-data-in' type='checkbox'><label for='data-512b6e89-0efe-461c-912e-34da60682bda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c57de098-e0d9-4f24-918f-f29930cfcd10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c57de098-e0d9-4f24-918f-f29930cfcd10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e4903b1-5e77-422d-ac35-2f9a4ee1d29a' class='xr-var-data-in' type='checkbox'><label for='data-8e4903b1-5e77-422d-ac35-2f9a4ee1d29a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-9bed908b-c91a-4784-86d8-5d99efc524ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bed908b-c91a-4784-86d8-5d99efc524ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1d0fb81-af4f-4313-b0e6-d85dd6c34e21' class='xr-var-data-in' type='checkbox'><label for='data-c1d0fb81-af4f-4313-b0e6-d85dd6c34e21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-45b59d1e-dce1-4d2a-9ab1-fac4575c90c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45b59d1e-dce1-4d2a-9ab1-fac4575c90c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e80afda-bcf2-4c64-b4ce-248302697a7a' class='xr-var-data-in' type='checkbox'><label for='data-5e80afda-bcf2-4c64-b4ce-248302697a7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1d3af677-2a59-4971-b8b5-e8103723d1dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d3af677-2a59-4971-b8b5-e8103723d1dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7553f34-2869-4713-9640-b84e266065c5' class='xr-var-data-in' type='checkbox'><label for='data-b7553f34-2869-4713-9640-b84e266065c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d0f9bd13-d254-4f7c-9a69-91fe6e0b4b4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0f9bd13-d254-4f7c-9a69-91fe6e0b4b4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebfae586-7720-45ad-893e-a74af422df1f' class='xr-var-data-in' type='checkbox'><label for='data-ebfae586-7720-45ad-893e-a74af422df1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-75650f34-c2c0-4552-b1a8-759bea7f72ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75650f34-c2c0-4552-b1a8-759bea7f72ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9276272-3fb8-42d4-9464-8af212fa7a12' class='xr-var-data-in' type='checkbox'><label for='data-a9276272-3fb8-42d4-9464-8af212fa7a12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-711b3661-5e8b-4c61-b2dd-23235b790008' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-711b3661-5e8b-4c61-b2dd-23235b790008' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25f361f1-d996-4916-8792-173f61592e09' class='xr-var-data-in' type='checkbox'><label for='data-25f361f1-d996-4916-8792-173f61592e09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  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120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1b62f034-c43e-4ff8-8089-9c7abf27935b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b62f034-c43e-4ff8-8089-9c7abf27935b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67129103-b98f-4f1c-a4f1-151014dd327b' class='xr-var-data-in' type='checkbox'><label for='data-67129103-b98f-4f1c-a4f1-151014dd327b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f8c3cb0d-b6d0-4942-abce-c35de869c094' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8c3cb0d-b6d0-4942-abce-c35de869c094' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5602a71-b17f-4dff-bae1-9da7cb989ad6' class='xr-var-data-in' type='checkbox'><label for='data-e5602a71-b17f-4dff-bae1-9da7cb989ad6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-749041ed-3f8a-4056-8875-1a5ee45132a0' class='xr-section-summary-in' type='checkbox'  ><label for='section-749041ed-3f8a-4056-8875-1a5ee45132a0' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3db74a5e-d7db-46ab-b82b-d96de1a35d10' class='xr-index-data-in' type='checkbox'/><label for='index-3db74a5e-d7db-46ab-b82b-d96de1a35d10' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c754965d-9969-4e04-8036-002e3b15d833' class='xr-index-data-in' type='checkbox'/><label for='index-c754965d-9969-4e04-8036-002e3b15d833' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d6cc486d-92c4-493b-b048-aeeebef74fce' class='xr-index-data-in' type='checkbox'/><label for='index-d6cc486d-92c4-493b-b048-aeeebef74fce' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-02-01&#x27;, &#x27;2020-03-01&#x27;, &#x27;2020-04-01&#x27;, &#x27;2020-05-01&#x27;,\n               &#x27;2020-06-01&#x27;, &#x27;2020-07-01&#x27;, &#x27;2020-08-01&#x27;, &#x27;2020-09-01&#x27;,\n               &#x27;2020-10-01&#x27;, &#x27;2020-11-01&#x27;, &#x27;2020-12-01&#x27;, &#x27;2021-01-01&#x27;,\n               &#x27;2021-02-01&#x27;, &#x27;2021-03-01&#x27;, &#x27;2021-04-01&#x27;, &#x27;2021-05-01&#x27;,\n               &#x27;2021-06-01&#x27;, &#x27;2021-07-01&#x27;, &#x27;2021-08-01&#x27;, &#x27;2021-09-01&#x27;,\n               &#x27;2021-10-01&#x27;, &#x27;2021-11-01&#x27;, &#x27;2021-12-01&#x27;, &#x27;2022-01-01&#x27;,\n               &#x27;2022-02-01&#x27;, &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;,\n               &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;,\n               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;, &#x27;2023-05-01&#x27;,\n               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;, &#x27;2023-08-01&#x27;, &#x27;2023-09-01&#x27;,\n               &#x27;2023-10-01&#x27;, &#x27;2023-11-01&#x27;, &#x27;2023-12-01&#x27;, &#x27;2024-01-01&#x27;,\n               &#x27;2024-02-01&#x27;, &#x27;2024-03-01&#x27;, &#x27;2024-04-01&#x27;, &#x27;2024-05-01&#x27;,\n               &#x27;2024-06-01&#x27;, &#x27;2024-07-01&#x27;, &#x27;2024-08-01&#x27;, &#x27;2024-09-01&#x27;,\n               &#x27;2024-10-01&#x27;, &#x27;2024-11-01&#x27;, &#x27;2024-12-01&#x27;, &#x27;2025-01-01&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8a6db70e-a7fb-4c2d-b4bb-db9f8fc9f1bb' class='xr-section-summary-in' type='checkbox'  ><label for='section-8a6db70e-a7fb-4c2d-b4bb-db9f8fc9f1bb' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 044638</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB 89.46 88.64 89.06 ... -27.32 -27.0\n    clon           (ncells) float64 655kB 72.0 50.25 72.0 ... 1.296 0.6204 0.357\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2020-02-01 ... 2025-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albnirdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    clat_bnds      (ncells, vertices) float64 2MB dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n    clct           (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqi_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv            (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    umfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    vmfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-acfbb5a0-533b-4030-afad-897b5d0a1416' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-acfbb5a0-533b-4030-afad-897b5d0a1416' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-267fb2d5-0201-4044-8f44-14c2632bad76' class='xr-section-summary-in' type='checkbox'  checked><label for='section-267fb2d5-0201-4044-8f44-14c2632bad76' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.46 88.64 89.06 ... -27.32 -27.0</div><input id='attrs-b424372a-18cd-4660-81c4-9083fc3a211e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b424372a-18cd-4660-81c4-9083fc3a211e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53e32306-32a4-44fd-b280-27b3a37f2e9d' class='xr-var-data-in' type='checkbox'><label for='data-53e32306-32a4-44fd-b280-27b3a37f2e9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><pre>array([ 89.45889176,  88.63772156,  89.06223506, ..., -27.28616813,\n       -27.32237258, -27.0023716 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>72.0 50.25 72.0 ... 0.6204 0.357</div><input id='attrs-5dc16e36-2d44-486f-af6f-840192df6d01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dc16e36-2d44-486f-af6f-840192df6d01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea9129cf-5df2-4c16-bfdf-237948fa9a14' class='xr-var-data-in' type='checkbox'><label for='data-ea9129cf-5df2-4c16-bfdf-237948fa9a14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><pre>array([72.        , 50.25313351, 72.        , ...,  1.29630984,\n        0.62040403,  0.3569665 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-2a3d5931-6fdb-4958-8f08-b02c88efccad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a3d5931-6fdb-4958-8f08-b02c88efccad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-268dd1e9-95f4-4492-b7e3-8ab20c0aacee' class='xr-var-data-in' type='checkbox'><label for='data-268dd1e9-95f4-4492-b7e3-8ab20c0aacee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-97aeb499-8ee9-4fb9-8fff-5c144b87fce0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97aeb499-8ee9-4fb9-8fff-5c144b87fce0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a534856-a1e8-4380-ac19-2de33c419e4b' class='xr-var-data-in' type='checkbox'><label for='data-2a534856-a1e8-4380-ac19-2de33c419e4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-02-01 ... 2025-01-01</div><input id='attrs-e4331358-2568-4c5e-882b-32b5057a1d25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4331358-2568-4c5e-882b-32b5057a1d25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8c57dad-9282-4df0-ab13-e0c86198e740' class='xr-var-data-in' type='checkbox'><label for='data-f8c57dad-9282-4df0-ab13-e0c86198e740' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-02-01T00:00:00.000000000&#x27;, &#x27;2020-03-01T00:00:00.000000000&#x27;,\n       &#x27;2020-04-01T00:00:00.000000000&#x27;, &#x27;2020-05-01T00:00:00.000000000&#x27;,\n       &#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-07-01T00:00:00.000000000&#x27;,\n       &#x27;2020-08-01T00:00:00.000000000&#x27;, &#x27;2020-09-01T00:00:00.000000000&#x27;,\n       &#x27;2020-10-01T00:00:00.000000000&#x27;, &#x27;2020-11-01T00:00:00.000000000&#x27;,\n       &#x27;2020-12-01T00:00:00.000000000&#x27;, &#x27;2021-01-01T00:00:00.000000000&#x27;,\n       &#x27;2021-02-01T00:00:00.000000000&#x27;, &#x27;2021-03-01T00:00:00.000000000&#x27;,\n       &#x27;2021-04-01T00:00:00.000000000&#x27;, &#x27;2021-05-01T00:00:00.000000000&#x27;,\n       &#x27;2021-06-01T00:00:00.000000000&#x27;, &#x27;2021-07-01T00:00:00.000000000&#x27;,\n       &#x27;2021-08-01T00:00:00.000000000&#x27;, &#x27;2021-09-01T00:00:00.000000000&#x27;,\n       &#x27;2021-10-01T00:00:00.000000000&#x27;, &#x27;2021-11-01T00:00:00.000000000&#x27;,\n       &#x27;2021-12-01T00:00:00.000000000&#x27;, &#x27;2022-01-01T00:00:00.000000000&#x27;,\n       &#x27;2022-02-01T00:00:00.000000000&#x27;, &#x27;2022-03-01T00:00:00.000000000&#x27;,\n       &#x27;2022-04-01T00:00:00.000000000&#x27;, &#x27;2022-05-01T00:00:00.000000000&#x27;,\n       &#x27;2022-06-01T00:00:00.000000000&#x27;, &#x27;2022-07-01T00:00:00.000000000&#x27;,\n       &#x27;2022-08-01T00:00:00.000000000&#x27;, &#x27;2022-09-01T00:00:00.000000000&#x27;,\n       &#x27;2022-10-01T00:00:00.000000000&#x27;, &#x27;2022-11-01T00:00:00.000000000&#x27;,\n       &#x27;2022-12-01T00:00:00.000000000&#x27;, &#x27;2023-01-01T00:00:00.000000000&#x27;,\n       &#x27;2023-02-01T00:00:00.000000000&#x27;, &#x27;2023-03-01T00:00:00.000000000&#x27;,\n       &#x27;2023-04-01T00:00:00.000000000&#x27;, &#x27;2023-05-01T00:00:00.000000000&#x27;,\n       &#x27;2023-06-01T00:00:00.000000000&#x27;, &#x27;2023-07-01T00:00:00.000000000&#x27;,\n       &#x27;2023-08-01T00:00:00.000000000&#x27;, &#x27;2023-09-01T00:00:00.000000000&#x27;,\n       &#x27;2023-10-01T00:00:00.000000000&#x27;, &#x27;2023-11-01T00:00:00.000000000&#x27;,\n       &#x27;2023-12-01T00:00:00.000000000&#x27;, &#x27;2024-01-01T00:00:00.000000000&#x27;,\n       &#x27;2024-02-01T00:00:00.000000000&#x27;, &#x27;2024-03-01T00:00:00.000000000&#x27;,\n       &#x27;2024-04-01T00:00:00.000000000&#x27;, &#x27;2024-05-01T00:00:00.000000000&#x27;,\n       &#x27;2024-06-01T00:00:00.000000000&#x27;, &#x27;2024-07-01T00:00:00.000000000&#x27;,\n       &#x27;2024-08-01T00:00:00.000000000&#x27;, &#x27;2024-09-01T00:00:00.000000000&#x27;,\n       &#x27;2024-10-01T00:00:00.000000000&#x27;, &#x27;2024-11-01T00:00:00.000000000&#x27;,\n       &#x27;2024-12-01T00:00:00.000000000&#x27;, &#x27;2025-01-01T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-43a971ff-ecdf-40e9-8fdb-2973e6e38126' class='xr-section-summary-in' type='checkbox'  ><label for='section-43a971ff-ecdf-40e9-8fdb-2973e6e38126' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-01cf3795-1289-4152-b176-c8dfdbf8781f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01cf3795-1289-4152-b176-c8dfdbf8781f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-561d5474-39c8-42a7-9221-d895ec4ebe3d' class='xr-var-data-in' type='checkbox'><label for='data-561d5474-39c8-42a7-9221-d895ec4ebe3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-55b0533d-e121-4464-a4ba-908b18a22e48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55b0533d-e121-4464-a4ba-908b18a22e48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40ad930f-a78f-4352-930e-5a32119b8d2c' class='xr-var-data-in' type='checkbox'><label for='data-40ad930f-a78f-4352-930e-5a32119b8d2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-05e98bbc-e7e1-401f-98ae-43f7e9c1aa97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05e98bbc-e7e1-401f-98ae-43f7e9c1aa97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be00ed4d-1248-40cf-9c9d-d26289a4144f' class='xr-var-data-in' type='checkbox'><label for='data-be00ed4d-1248-40cf-9c9d-d26289a4144f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-76bf84d2-ff18-4c4f-8d75-820844443017' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76bf84d2-ff18-4c4f-8d75-820844443017' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a396c4e-4526-464c-982f-3ee252b50e2c' class='xr-var-data-in' type='checkbox'><label for='data-4a396c4e-4526-464c-982f-3ee252b50e2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-194d99c3-5925-49de-a99e-387ab8f6320e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-194d99c3-5925-49de-a99e-387ab8f6320e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfc34eea-2154-43b2-a89f-537986294fb7' class='xr-var-data-in' type='checkbox'><label for='data-bfc34eea-2154-43b2-a89f-537986294fb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c1d7a160-ffe1-4ea7-afef-a9f6e128516c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1d7a160-ffe1-4ea7-afef-a9f6e128516c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5f42036-1011-477e-abab-d5377edf3259' class='xr-var-data-in' type='checkbox'><label for='data-a5f42036-1011-477e-abab-d5377edf3259' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-0de27416-09c5-4c23-9ecb-7bbe2ce59bea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0de27416-09c5-4c23-9ecb-7bbe2ce59bea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4a7391c-8e96-479f-94c6-46a9d8ab603a' class='xr-var-data-in' type='checkbox'><label for='data-b4a7391c-8e96-479f-94c6-46a9d8ab603a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-bc67ed5a-bb1e-4426-b410-f39f6b94b098' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc67ed5a-bb1e-4426-b410-f39f6b94b098' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58a90f70-5dce-4045-b7e4-96ae26e109ed' class='xr-var-data-in' type='checkbox'><label for='data-58a90f70-5dce-4045-b7e4-96ae26e109ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-91e14a58-364a-4c41-b28d-d231ea1c62aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91e14a58-364a-4c41-b28d-d231ea1c62aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69dbfeab-bb70-4082-9ef1-95ee052c774b' class='xr-var-data-in' type='checkbox'><label for='data-69dbfeab-bb70-4082-9ef1-95ee052c774b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320.00 kiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-094fa632-e75b-40a9-a272-9c8e52729747' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-094fa632-e75b-40a9-a272-9c8e52729747' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65b20119-ab74-4833-b9f6-5eca02919e1a' class='xr-var-data-in' type='checkbox'><label for='data-65b20119-ab74-4833-b9f6-5eca02919e1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-0a9826a4-3313-4c29-ac93-c90b9aa1893e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a9826a4-3313-4c29-ac93-c90b9aa1893e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dfa7267-5cb1-4254-8fe4-76410b1d1383' class='xr-var-data-in' type='checkbox'><label for='data-3dfa7267-5cb1-4254-8fe4-76410b1d1383' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-12b1e949-044f-4236-af93-4a6c919bd8c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12b1e949-044f-4236-af93-4a6c919bd8c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2444e7df-b577-47cd-b119-479feac352af' class='xr-var-data-in' type='checkbox'><label for='data-2444e7df-b577-47cd-b119-479feac352af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4c055aa2-b72c-4289-8484-8c0f972111c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c055aa2-b72c-4289-8484-8c0f972111c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db015534-9136-4e89-a770-d3a47a27df73' class='xr-var-data-in' type='checkbox'><label for='data-db015534-9136-4e89-a770-d3a47a27df73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b5f7816c-6bce-4969-8445-749f918b41cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5f7816c-6bce-4969-8445-749f918b41cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc425e81-d9f9-4e97-9f6b-93cc692c81c2' class='xr-var-data-in' type='checkbox'><label for='data-dc425e81-d9f9-4e97-9f6b-93cc692c81c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-50ff70d5-493e-4549-9bf5-8310b477edb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50ff70d5-493e-4549-9bf5-8310b477edb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5949c694-3e0e-4734-825d-813ff17398a2' class='xr-var-data-in' type='checkbox'><label for='data-5949c694-3e0e-4734-825d-813ff17398a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-49217a87-a434-49a4-8a2e-b0e33f8b8677' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49217a87-a434-49a4-8a2e-b0e33f8b8677' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef1d9163-0bd8-4ca3-9fcd-ef904e26a24a' class='xr-var-data-in' type='checkbox'><label for='data-ef1d9163-0bd8-4ca3-9fcd-ef904e26a24a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d04db676-bcde-4d3a-af61-c65dbf429316' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d04db676-bcde-4d3a-af61-c65dbf429316' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2adc4a9-f749-49a5-8d25-1695afcccd77' class='xr-var-data-in' type='checkbox'><label for='data-b2adc4a9-f749-49a5-8d25-1695afcccd77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-577f3687-adf0-484f-b9a6-c8db8d926b04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-577f3687-adf0-484f-b9a6-c8db8d926b04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11790482-d92a-4774-a207-60cdc7475d68' class='xr-var-data-in' type='checkbox'><label for='data-11790482-d92a-4774-a207-60cdc7475d68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-775a6936-58c9-444e-9e6e-f5655473ff71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-775a6936-58c9-444e-9e6e-f5655473ff71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-504892ec-1bc9-448a-adb7-993540fb6080' class='xr-var-data-in' type='checkbox'><label for='data-504892ec-1bc9-448a-adb7-993540fb6080' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-9501b227-28af-4b11-a20a-8aff992c40c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9501b227-28af-4b11-a20a-8aff992c40c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-449eaf16-06f2-4044-a314-698b02eb5703' class='xr-var-data-in' type='checkbox'><label for='data-449eaf16-06f2-4044-a314-698b02eb5703' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-66ddda77-f356-4222-ad19-e35116cf02de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66ddda77-f356-4222-ad19-e35116cf02de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff22abdc-4bc8-49a0-908e-3d6c596faf50' class='xr-var-data-in' type='checkbox'><label for='data-ff22abdc-4bc8-49a0-908e-3d6c596faf50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-704b2f05-684c-47cc-9c5d-bab60f0bee17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-704b2f05-684c-47cc-9c5d-bab60f0bee17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab1bb92a-541d-4ea4-8ce8-57547b3ba9ad' class='xr-var-data-in' type='checkbox'><label for='data-ab1bb92a-541d-4ea4-8ce8-57547b3ba9ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c5a7f897-b493-49a1-aed9-c333c57ffc32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5a7f897-b493-49a1-aed9-c333c57ffc32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bfd6948-9282-4dca-a24f-bb37fb44f734' class='xr-var-data-in' type='checkbox'><label for='data-5bfd6948-9282-4dca-a24f-bb37fb44f734' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-9ca15fee-bae0-4fbb-9b10-36decbf26dce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ca15fee-bae0-4fbb-9b10-36decbf26dce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb7a007e-e316-470e-8058-2d8f055cc02b' class='xr-var-data-in' type='checkbox'><label for='data-cb7a007e-e316-470e-8058-2d8f055cc02b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-bbc8f190-02dc-4d5a-84da-11094d2a623d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbc8f190-02dc-4d5a-84da-11094d2a623d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25699fcc-26be-4f1e-a3c8-657cb125154a' class='xr-var-data-in' type='checkbox'><label for='data-25699fcc-26be-4f1e-a3c8-657cb125154a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-cb6095d4-8adf-4269-95b0-829ad162b066' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb6095d4-8adf-4269-95b0-829ad162b066' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afa16e10-7f9c-472e-a9e2-42f7104c9a61' class='xr-var-data-in' type='checkbox'><label for='data-afa16e10-7f9c-472e-a9e2-42f7104c9a61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp_10m</span></div><div class='xr-var-dims'>(time, height, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-6af3bd68-3806-4342-a636-9e7107adfd6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6af3bd68-3806-4342-a636-9e7107adfd6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a666873-334c-4bdd-8ebb-7f789d407bff' class='xr-var-data-in' type='checkbox'><label for='data-2a666873-334c-4bdd-8ebb-7f789d407bff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>wind speed in 10m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.0</dd><dt><span>standard_name :</span></dt><dd>sp_10m</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"26\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"26\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"27\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"28\" />\n  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"29\" />\n  <line x1=\"15\" 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Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-fed37125-0658-48fb-9e4e-aca527f9c281' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fed37125-0658-48fb-9e4e-aca527f9c281' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40fa2047-4ddb-4bcf-8f6a-0a2a532c7c38' class='xr-var-data-in' type='checkbox'><label for='data-40fa2047-4ddb-4bcf-8f6a-0a2a532c7c38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- 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-->\n  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"24\" y1=\"40\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,40.36121446433689 24.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f265f180-9e95-411d-82bd-7606ca05feae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f265f180-9e95-411d-82bd-7606ca05feae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e06b1a5e-be2b-45bd-9ca2-06755d8cebc9' class='xr-var-data-in' type='checkbox'><label for='data-e06b1a5e-be2b-45bd-9ca2-06755d8cebc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" 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id='data-f9c15c0c-966c-4d48-8cee-0ef7f31a40ee' class='xr-var-data-in' type='checkbox'><label for='data-f9c15c0c-966c-4d48-8cee-0ef7f31a40ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d802b88c-eab7-4166-add2-1f076733969b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d802b88c-eab7-4166-add2-1f076733969b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faef3223-e764-4000-b69a-fb0a29d5a345' class='xr-var-data-in' type='checkbox'><label for='data-faef3223-e764-4000-b69a-fb0a29d5a345' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4a81303e-f56f-4fbc-840a-40ab6f9d6062' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a81303e-f56f-4fbc-840a-40ab6f9d6062' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2709f30c-f4be-4370-bd99-a2e4fed22caa' class='xr-var-data-in' type='checkbox'><label for='data-2709f30c-f4be-4370-bd99-a2e4fed22caa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-32450473-4d09-47e6-a750-14ef87147766' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32450473-4d09-47e6-a750-14ef87147766' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce627922-7be7-4a9b-b4ee-224426e264f2' class='xr-var-data-in' type='checkbox'><label for='data-ce627922-7be7-4a9b-b4ee-224426e264f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-39aa40b3-f6b1-4414-adfe-3da332aa78dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39aa40b3-f6b1-4414-adfe-3da332aa78dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79e86756-32d1-41ae-9d2b-e2b528cd0b43' class='xr-var-data-in' type='checkbox'><label for='data-79e86756-32d1-41ae-9d2b-e2b528cd0b43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5b98c485-a84c-47a7-9200-77ca5390437c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b98c485-a84c-47a7-9200-77ca5390437c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea1aa440-1d1e-4ca8-a4d0-286f1983a1ff' class='xr-var-data-in' type='checkbox'><label for='data-ea1aa440-1d1e-4ca8-a4d0-286f1983a1ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-dd5f0782-87d6-4c62-81b6-e0d542477c42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd5f0782-87d6-4c62-81b6-e0d542477c42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6b71e71-af13-421f-9acb-7e002e29ffe1' class='xr-var-data-in' type='checkbox'><label for='data-e6b71e71-af13-421f-9acb-7e002e29ffe1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-460c40f8-65db-4df8-89f9-4d5cc014217a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-460c40f8-65db-4df8-89f9-4d5cc014217a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cebb3a93-97bc-4ebe-97df-8facbaa18458' class='xr-var-data-in' type='checkbox'><label for='data-cebb3a93-97bc-4ebe-97df-8facbaa18458' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-1d37b69d-b2d0-4b21-a0a9-f562e75692c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d37b69d-b2d0-4b21-a0a9-f562e75692c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ef51b53-4c0a-414b-b0bf-13eab0b53c21' class='xr-var-data-in' type='checkbox'><label for='data-6ef51b53-4c0a-414b-b0bf-13eab0b53c21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ceb01531-dd3b-42e0-b6bf-bf090ee98822' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ceb01531-dd3b-42e0-b6bf-bf090ee98822' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44301d0b-310f-4f9b-bb4e-68ca4012e8c2' class='xr-var-data-in' type='checkbox'><label for='data-44301d0b-310f-4f9b-bb4e-68ca4012e8c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" 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x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a9377bba-97bf-4fdc-bf6a-97ea2bbef397' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9377bba-97bf-4fdc-bf6a-97ea2bbef397' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d276a924-859e-4393-b22d-2dcb9ae01873' class='xr-var-data-in' type='checkbox'><label for='data-d276a924-859e-4393-b22d-2dcb9ae01873' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-751f49b6-0890-4816-887d-2dc0a992f9c1' class='xr-section-summary-in' type='checkbox'  ><label for='section-751f49b6-0890-4816-887d-2dc0a992f9c1' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-5dade1d9-1590-4073-9629-155cc246fc83' class='xr-index-data-in' type='checkbox'/><label for='index-5dade1d9-1590-4073-9629-155cc246fc83' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d7e42943-2a2a-42aa-a342-97c5ac411f84' class='xr-index-data-in' type='checkbox'/><label for='index-d7e42943-2a2a-42aa-a342-97c5ac411f84' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-97109f70-1db9-4052-9bee-f73a546c5b69' class='xr-index-data-in' type='checkbox'/><label for='index-97109f70-1db9-4052-9bee-f73a546c5b69' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-02-01&#x27;, &#x27;2020-03-01&#x27;, &#x27;2020-04-01&#x27;, &#x27;2020-05-01&#x27;,\n               &#x27;2020-06-01&#x27;, &#x27;2020-07-01&#x27;, &#x27;2020-08-01&#x27;, &#x27;2020-09-01&#x27;,\n               &#x27;2020-10-01&#x27;, &#x27;2020-11-01&#x27;, &#x27;2020-12-01&#x27;, &#x27;2021-01-01&#x27;,\n               &#x27;2021-02-01&#x27;, &#x27;2021-03-01&#x27;, &#x27;2021-04-01&#x27;, &#x27;2021-05-01&#x27;,\n               &#x27;2021-06-01&#x27;, &#x27;2021-07-01&#x27;, &#x27;2021-08-01&#x27;, &#x27;2021-09-01&#x27;,\n               &#x27;2021-10-01&#x27;, &#x27;2021-11-01&#x27;, &#x27;2021-12-01&#x27;, &#x27;2022-01-01&#x27;,\n               &#x27;2022-02-01&#x27;, &#x27;2022-03-01&#x27;, &#x27;2022-04-01&#x27;, &#x27;2022-05-01&#x27;,\n               &#x27;2022-06-01&#x27;, &#x27;2022-07-01&#x27;, &#x27;2022-08-01&#x27;, &#x27;2022-09-01&#x27;,\n               &#x27;2022-10-01&#x27;, &#x27;2022-11-01&#x27;, &#x27;2022-12-01&#x27;, &#x27;2023-01-01&#x27;,\n               &#x27;2023-02-01&#x27;, &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;, &#x27;2023-05-01&#x27;,\n               &#x27;2023-06-01&#x27;, &#x27;2023-07-01&#x27;, &#x27;2023-08-01&#x27;, &#x27;2023-09-01&#x27;,\n               &#x27;2023-10-01&#x27;, &#x27;2023-11-01&#x27;, &#x27;2023-12-01&#x27;, &#x27;2024-01-01&#x27;,\n               &#x27;2024-02-01&#x27;, &#x27;2024-03-01&#x27;, &#x27;2024-04-01&#x27;, &#x27;2024-05-01&#x27;,\n               &#x27;2024-06-01&#x27;, &#x27;2024-07-01&#x27;, &#x27;2024-08-01&#x27;, &#x27;2024-09-01&#x27;,\n               &#x27;2024-10-01&#x27;, &#x27;2024-11-01&#x27;, &#x27;2024-12-01&#x27;, &#x27;2025-01-01&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-865e0e02-3129-4e04-9f02-bbff0727bf6d' class='xr-section-summary-in' type='checkbox'  ><label for='section-865e0e02-3129-4e04-9f02-bbff0727bf6d' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 044638</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
           },
           "metadata": {}
         }
@@ -144,11 +144,6 @@
         "trusted": true
       },
       "outputs": [
-        {
-          "name": "stdout",
-          "text": "[pyodide]: Loading Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n[pyodide]: Loaded Cartopy, Pillow, adjustText, affine, appdirs, cads-api-client, cdsapi, cffi, cfgrib, cligj, cycler, earthkit, earthkit-data, earthkit-geo, earthkit-meteo, earthkit-plots, earthkit-plots-default-styles, earthkit-regrid, earthkit-transforms, eccodes, ecmwf-api-client, ecmwf-opendata, entrypoints, filelock, findlibs, fiona, flexcache, flexparser, fonttools, gdal, geopandas, geos, h5py, jsonschema, jsonschema_specifications, kiwisolver, lru-dict, markdown, matplotlib, matplotlib-pyodide, multiurl, netcdf4, openblas, pdbufr, pint, pkgconfig, pycparser, pyparsing, pyproj, pyrsistent, pyshp, rasterio, referencing, rpds-py, scipy, setuptools, shapely, tqdm, typing-extensions\n",
-          "output_type": "stream"
-        },
         {
           "name": "stderr",
           "text": "/lib/python3.12/site-packages/earthkit/regrid/utils/caching.py:27: UserWarning: only soft file lock is available\n  from filelock import FileLock\n",
@@ -156,7 +151,7 @@
         },
         {
           "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 309.4MiB (from 124.2MiB) for this notebook\n",
+          "text": "[pyodide]: Memory usage has grown to 309.4MiB (from 149.1MiB) for this notebook\n",
           "output_type": "stream"
         }
       ],
@@ -185,13 +180,13 @@
           "execution_count": 6,
           "output_type": "execute_result",
           "data": {
-            "text/plain": "<earthkit.plots.components.maps.Map at 0x10848dd8>"
+            "text/plain": "<earthkit.plots.components.maps.Map at 0x113e6318>"
           },
           "metadata": {}
         },
         {
           "name": "stdout",
-          "text": "[pyodide]: Memory usage has grown to 637.2MiB (from 309.4MiB) for this notebook\n",
+          "text": "[pyodide]: Memory usage has grown to 637.3MiB (from 309.4MiB) for this notebook\n",
           "output_type": "stream"
         },
         {
@@ -229,7 +224,7 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": "<xarray.Dataset> Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB 89.46 88.64 89.06 ... -27.32 -27.0\n    clon           (ncells) float64 655kB 72.0 50.25 72.0 ... 1.296 0.6204 0.357\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2015-02-01 ... 2020-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albnirdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdif      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    albvisdir      (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    clat_bnds      (ncells, vertices) float64 2MB dask.array<chunksize=(81920, 3), meta=np.ndarray>\n    clct           (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqi_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv            (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    tqv_dia        (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    umfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\n    vmfl_s         (time, ncells) float32 20MB dask.array<chunksize=(1, 81920), meta=np.ndarray>\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7",
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB 89.46 88.64 89.06 ... -27.32 -27.0\n    clon           (ncells) float64 655kB 72.0 50.25 72.0 ... 1.296 0.6204 0.357\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2015-02-01 ... 2020-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albnirdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    clat_bnds      (ncells, vertices) float64 2MB dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n    clct           (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqi_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv            (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    umfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    vmfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-476f6d22-190e-4fc2-9f75-ceba4096c720' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-476f6d22-190e-4fc2-9f75-ceba4096c720' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-97dbb2c6-2800-4879-a309-cc055ab70525' class='xr-section-summary-in' type='checkbox'  checked><label for='section-97dbb2c6-2800-4879-a309-cc055ab70525' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.46 88.64 89.06 ... -27.32 -27.0</div><input id='attrs-dd5923da-5376-406d-869b-a6f4eadc379e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd5923da-5376-406d-869b-a6f4eadc379e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-508b0108-b4bc-4dae-9bd0-2e649d041491' class='xr-var-data-in' type='checkbox'><label for='data-508b0108-b4bc-4dae-9bd0-2e649d041491' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><pre>array([ 89.45889176,  88.63772156,  89.06223506, ..., -27.28616813,\n       -27.32237258, -27.0023716 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>72.0 50.25 72.0 ... 0.6204 0.357</div><input id='attrs-c36467d6-7eeb-415c-8d7d-8b4245500d6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c36467d6-7eeb-415c-8d7d-8b4245500d6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d5cd5a7-a1a6-4f9b-bc4c-385f1ddf3b35' class='xr-var-data-in' type='checkbox'><label for='data-5d5cd5a7-a1a6-4f9b-bc4c-385f1ddf3b35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><pre>array([72.        , 50.25313351, 72.        , ...,  1.29630984,\n        0.62040403,  0.3569665 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-55a77492-b035-49a8-aa6b-bcf763e0a7ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55a77492-b035-49a8-aa6b-bcf763e0a7ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f12469b6-8b5b-4978-822d-aa99ad34edad' class='xr-var-data-in' type='checkbox'><label for='data-f12469b6-8b5b-4978-822d-aa99ad34edad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-2cb517dc-102a-4ba7-8791-578e78ff3dff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cb517dc-102a-4ba7-8791-578e78ff3dff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-891e8949-c6ce-48ed-b54a-eef0c6d6918d' class='xr-var-data-in' type='checkbox'><label for='data-891e8949-c6ce-48ed-b54a-eef0c6d6918d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-02-01 ... 2020-01-01</div><input id='attrs-9f11dafa-8b4b-4627-9e38-e114157f6fb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f11dafa-8b4b-4627-9e38-e114157f6fb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8d6f664-f355-4304-871f-6c9d50d56021' class='xr-var-data-in' type='checkbox'><label for='data-a8d6f664-f355-4304-871f-6c9d50d56021' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n       &#x27;2016-12-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n       &#x27;2017-02-01T00:00:00.000000000&#x27;, &#x27;2017-03-01T00:00:00.000000000&#x27;,\n       &#x27;2017-04-01T00:00:00.000000000&#x27;, &#x27;2017-05-01T00:00:00.000000000&#x27;,\n       &#x27;2017-06-01T00:00:00.000000000&#x27;, &#x27;2017-07-01T00:00:00.000000000&#x27;,\n       &#x27;2017-08-01T00:00:00.000000000&#x27;, &#x27;2017-09-01T00:00:00.000000000&#x27;,\n       &#x27;2017-10-01T00:00:00.000000000&#x27;, &#x27;2017-11-01T00:00:00.000000000&#x27;,\n       &#x27;2017-12-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n       &#x27;2018-02-01T00:00:00.000000000&#x27;, &#x27;2018-03-01T00:00:00.000000000&#x27;,\n       &#x27;2018-04-01T00:00:00.000000000&#x27;, &#x27;2018-05-01T00:00:00.000000000&#x27;,\n       &#x27;2018-06-01T00:00:00.000000000&#x27;, &#x27;2018-07-01T00:00:00.000000000&#x27;,\n       &#x27;2018-08-01T00:00:00.000000000&#x27;, &#x27;2018-09-01T00:00:00.000000000&#x27;,\n       &#x27;2018-10-01T00:00:00.000000000&#x27;, &#x27;2018-11-01T00:00:00.000000000&#x27;,\n       &#x27;2018-12-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;,\n       &#x27;2019-04-01T00:00:00.000000000&#x27;, &#x27;2019-05-01T00:00:00.000000000&#x27;,\n       &#x27;2019-06-01T00:00:00.000000000&#x27;, &#x27;2019-07-01T00:00:00.000000000&#x27;,\n       &#x27;2019-08-01T00:00:00.000000000&#x27;, &#x27;2019-09-01T00:00:00.000000000&#x27;,\n       &#x27;2019-10-01T00:00:00.000000000&#x27;, &#x27;2019-11-01T00:00:00.000000000&#x27;,\n       &#x27;2019-12-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da81ea68-f16f-4eb2-9d78-d2386e0c2a34' class='xr-section-summary-in' type='checkbox'  ><label for='section-da81ea68-f16f-4eb2-9d78-d2386e0c2a34' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-75125f1d-2600-4caf-bed5-5d2cd12e63e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75125f1d-2600-4caf-bed5-5d2cd12e63e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d0fc992-c79d-493b-85d5-e4be2a32d6c3' class='xr-var-data-in' type='checkbox'><label for='data-9d0fc992-c79d-493b-85d5-e4be2a32d6c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-17b5b174-30b9-4888-89a9-e42a1ee6e974' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17b5b174-30b9-4888-89a9-e42a1ee6e974' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4139139d-9f8a-4f17-8c45-a11a9926d972' class='xr-var-data-in' type='checkbox'><label for='data-4139139d-9f8a-4f17-8c45-a11a9926d972' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f20282e6-6d95-4e7a-b817-154e32077b86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f20282e6-6d95-4e7a-b817-154e32077b86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51258272-b0f4-4199-aad1-5df8c78e27ac' class='xr-var-data-in' type='checkbox'><label for='data-51258272-b0f4-4199-aad1-5df8c78e27ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-50f570cd-c028-4a24-93cf-af45aeb34643' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50f570cd-c028-4a24-93cf-af45aeb34643' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7e5fab8-5daa-45b7-aae0-4a2e53780ebf' class='xr-var-data-in' type='checkbox'><label for='data-f7e5fab8-5daa-45b7-aae0-4a2e53780ebf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  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class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-b2f3f785-3fe8-42db-9113-773ddca8f00e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2f3f785-3fe8-42db-9113-773ddca8f00e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8be29722-3a70-4818-a176-df2da7b29a2e' class='xr-var-data-in' type='checkbox'><label for='data-8be29722-3a70-4818-a176-df2da7b29a2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-69f1e0ff-73d5-45e1-917f-c42d0247f3a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69f1e0ff-73d5-45e1-917f-c42d0247f3a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b231c91c-4157-4c82-95b7-242464f5fc94' class='xr-var-data-in' type='checkbox'><label for='data-b231c91c-4157-4c82-95b7-242464f5fc94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-57ce5fdb-22ef-4a50-aa91-6c3fec4af5ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57ce5fdb-22ef-4a50-aa91-6c3fec4af5ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-923a9870-00ee-4cd6-b5e3-e1ba380befd6' class='xr-var-data-in' type='checkbox'><label for='data-923a9870-00ee-4cd6-b5e3-e1ba380befd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-215bc499-4976-4446-a002-74f06564e3b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-215bc499-4976-4446-a002-74f06564e3b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4daea53a-f789-440c-b691-f1d5808c4bfb' class='xr-var-data-in' type='checkbox'><label for='data-4daea53a-f789-440c-b691-f1d5808c4bfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-913d51b7-5159-43cd-b515-4b152db503cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-913d51b7-5159-43cd-b515-4b152db503cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79f1af18-8170-4a17-8539-2e672e1da3d3' class='xr-var-data-in' type='checkbox'><label for='data-79f1af18-8170-4a17-8539-2e672e1da3d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320.00 kiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-94ce0900-063e-4552-aa2c-715c4ef00fcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94ce0900-063e-4552-aa2c-715c4ef00fcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-966c4203-3992-4143-a197-cfe431892b14' class='xr-var-data-in' type='checkbox'><label for='data-966c4203-3992-4143-a197-cfe431892b14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c4b9badb-5998-4d74-bfdf-bd921e4e04a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4b9badb-5998-4d74-bfdf-bd921e4e04a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ddc770a-c2f9-41a7-84f0-dcae3afce69d' class='xr-var-data-in' type='checkbox'><label for='data-2ddc770a-c2f9-41a7-84f0-dcae3afce69d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-0af72539-fd50-47ce-b405-5cfa49775d20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0af72539-fd50-47ce-b405-5cfa49775d20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87427130-444a-4210-9e3a-0ac275557788' class='xr-var-data-in' type='checkbox'><label for='data-87427130-444a-4210-9e3a-0ac275557788' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-99067a5d-7bf0-4aee-8ec2-3b68f57b85f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99067a5d-7bf0-4aee-8ec2-3b68f57b85f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25cbc13f-3901-418e-a7b7-a2d39fb43946' class='xr-var-data-in' type='checkbox'><label for='data-25cbc13f-3901-418e-a7b7-a2d39fb43946' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2e4f1507-dda4-41dc-a07b-d4faf6558cbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e4f1507-dda4-41dc-a07b-d4faf6558cbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bdb4829-12af-472c-b74c-d34a93377e18' class='xr-var-data-in' type='checkbox'><label for='data-8bdb4829-12af-472c-b74c-d34a93377e18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c315f50b-9ece-461d-8c3d-610fd9af342f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c315f50b-9ece-461d-8c3d-610fd9af342f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b33dead-506f-4a67-8297-8e45085546c1' class='xr-var-data-in' type='checkbox'><label for='data-2b33dead-506f-4a67-8297-8e45085546c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-7c1fbce8-ffcd-48f2-ab7f-154359db29a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c1fbce8-ffcd-48f2-ab7f-154359db29a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50db397b-bcef-4732-886f-8397f2cbcf9d' class='xr-var-data-in' type='checkbox'><label for='data-50db397b-bcef-4732-886f-8397f2cbcf9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-6c66d9fb-9e41-4be5-b19c-9d01cd6f8f2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c66d9fb-9e41-4be5-b19c-9d01cd6f8f2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8671a2b0-5d12-4d5d-9b67-f746709a2184' class='xr-var-data-in' type='checkbox'><label for='data-8671a2b0-5d12-4d5d-9b67-f746709a2184' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-cbc3ebd6-182d-4288-87cb-3d55806c0254' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbc3ebd6-182d-4288-87cb-3d55806c0254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d4e0d92-d208-45f1-a9d5-aefd69b57d88' class='xr-var-data-in' type='checkbox'><label for='data-8d4e0d92-d208-45f1-a9d5-aefd69b57d88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-dc9665ec-dc56-4eb0-9cc0-b70a4ebe7819' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc9665ec-dc56-4eb0-9cc0-b70a4ebe7819' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d4590ed-9f0a-4bc0-889b-f032141412a2' class='xr-var-data-in' type='checkbox'><label for='data-3d4590ed-9f0a-4bc0-889b-f032141412a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-064700e0-cfd3-4650-9800-0dde8be333a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-064700e0-cfd3-4650-9800-0dde8be333a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5deb341-2e78-4deb-ab81-e54b57499743' class='xr-var-data-in' type='checkbox'><label for='data-e5deb341-2e78-4deb-ab81-e54b57499743' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ce03ac63-ef24-49ab-b978-7908d28e1e35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce03ac63-ef24-49ab-b978-7908d28e1e35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34b28deb-d7ba-433d-a360-2af4851581aa' class='xr-var-data-in' type='checkbox'><label for='data-34b28deb-d7ba-433d-a360-2af4851581aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8960b254-132e-4b24-9c8c-404bbc02f44c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8960b254-132e-4b24-9c8c-404bbc02f44c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47367123-3e25-4fe9-ba28-e85e0efb623b' class='xr-var-data-in' type='checkbox'><label for='data-47367123-3e25-4fe9-ba28-e85e0efb623b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b2427e54-3820-47af-afdc-8ae908ed1446' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2427e54-3820-47af-afdc-8ae908ed1446' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce266f07-56c2-4999-986f-f161f4912287' class='xr-var-data-in' type='checkbox'><label for='data-ce266f07-56c2-4999-986f-f161f4912287' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c0829ea2-33ac-42c1-b28b-6b3f47a46a62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0829ea2-33ac-42c1-b28b-6b3f47a46a62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1f827eb-c339-4fd8-9364-6a6cc413c71e' class='xr-var-data-in' type='checkbox'><label for='data-d1f827eb-c339-4fd8-9364-6a6cc413c71e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  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y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5d1e6660-f364-4a0e-b3dd-51812ed2731a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d1e6660-f364-4a0e-b3dd-51812ed2731a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79ed8637-09f1-448f-82c4-630026364b7c' class='xr-var-data-in' type='checkbox'><label for='data-79ed8637-09f1-448f-82c4-630026364b7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-e96758f6-d1ca-4f29-89ae-702b0f4d91c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e96758f6-d1ca-4f29-89ae-702b0f4d91c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-779004f0-8172-44a7-8dbb-2b50af520212' class='xr-var-data-in' type='checkbox'><label for='data-779004f0-8172-44a7-8dbb-2b50af520212' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp_10m</span></div><div class='xr-var-dims'>(time, height, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3a426bc4-c855-46e2-9049-1e71f96b1d73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a426bc4-c855-46e2-9049-1e71f96b1d73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9292982e-8180-4130-b46f-f3392027086f' class='xr-var-data-in' type='checkbox'><label for='data-9292982e-8180-4130-b46f-f3392027086f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>wind speed in 10m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.0</dd><dt><span>standard_name :</span></dt><dd>sp_10m</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"26\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"26\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"27\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"28\" />\n  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"29\" />\n  <line x1=\"15\" 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Text -->\n  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">1</text>\n  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_2m</span></div><div class='xr-var-dims'>(time, height_2, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-4b7ff6b5-26c2-4d45-bddd-0233076d240a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b7ff6b5-26c2-4d45-bddd-0233076d240a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cf96390-4e98-44cb-a3f1-fb8eb2f44878' class='xr-var-data-in' type='checkbox'><label for='data-4cf96390-4e98-44cb-a3f1-fb8eb2f44878' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- 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transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2f7d92cd-7bad-4384-9387-8ecfb82d13ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f7d92cd-7bad-4384-9387-8ecfb82d13ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4305817-4412-4080-b2da-769a7c5ba663' class='xr-var-data-in' type='checkbox'><label for='data-f4305817-4412-4080-b2da-769a7c5ba663' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" 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0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-32405702-c6cb-4986-a334-1f609479fa92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32405702-c6cb-4986-a334-1f609479fa92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-058d07d6-c898-4a76-ac8c-0546fb94304c' class='xr-var-data-in' type='checkbox'><label for='data-058d07d6-c898-4a76-ac8c-0546fb94304c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-07305c90-5e9f-43f2-b6e0-74cd1d6ecb4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07305c90-5e9f-43f2-b6e0-74cd1d6ecb4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd87a60a-0049-46d4-92cf-487295b74472' class='xr-var-data-in' type='checkbox'><label for='data-bd87a60a-0049-46d4-92cf-487295b74472' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ddab5de8-a100-4cae-8c54-8ef12263a56c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddab5de8-a100-4cae-8c54-8ef12263a56c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6711a7b7-f640-4cae-b26f-b876f15573ed' class='xr-var-data-in' type='checkbox'><label for='data-6711a7b7-f640-4cae-b26f-b876f15573ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-901339db-bbf3-4ff8-bdad-1811bb252773' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-901339db-bbf3-4ff8-bdad-1811bb252773' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6b1baba-b9dd-47eb-ba48-dd9e78efe2fa' class='xr-var-data-in' type='checkbox'><label for='data-c6b1baba-b9dd-47eb-ba48-dd9e78efe2fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8e8f2e51-0d91-46fc-86d2-b3e275ff0184' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e8f2e51-0d91-46fc-86d2-b3e275ff0184' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36ffad12-e338-417d-9974-3b15bb874d67' class='xr-var-data-in' type='checkbox'><label for='data-36ffad12-e338-417d-9974-3b15bb874d67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b0ba6877-dab3-4346-9e05-909b76c89f50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0ba6877-dab3-4346-9e05-909b76c89f50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff2bf06b-93a7-4638-a44c-238c3930c60a' class='xr-var-data-in' type='checkbox'><label for='data-ff2bf06b-93a7-4638-a44c-238c3930c60a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-cbd5bfe0-fe82-4599-a43b-e838ed59c2b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbd5bfe0-fe82-4599-a43b-e838ed59c2b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f900b986-f173-4439-b131-7853a137fe7e' class='xr-var-data-in' type='checkbox'><label for='data-f900b986-f173-4439-b131-7853a137fe7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c0cb0400-9727-453a-b701-330b83f7ce15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0cb0400-9727-453a-b701-330b83f7ce15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30225307-50ee-4034-a9dd-aad654ec200d' class='xr-var-data-in' type='checkbox'><label for='data-30225307-50ee-4034-a9dd-aad654ec200d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2d6c7f5b-a597-49df-b22d-7ccce40fbd5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d6c7f5b-a597-49df-b22d-7ccce40fbd5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e485daf7-8f19-4bea-ba4b-ade010ee6110' class='xr-var-data-in' type='checkbox'><label for='data-e485daf7-8f19-4bea-ba4b-ade010ee6110' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  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id='data-43c4f804-90a7-4ecd-bb1c-3f800927ce32' class='xr-var-data-in' type='checkbox'><label for='data-43c4f804-90a7-4ecd-bb1c-3f800927ce32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c55dfb5d-728f-46d8-9f24-55a3d57f1776' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c55dfb5d-728f-46d8-9f24-55a3d57f1776' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfa3c8d5-cf78-42fd-99c3-78cb2f31e6cf' class='xr-var-data-in' type='checkbox'><label for='data-bfa3c8d5-cf78-42fd-99c3-78cb2f31e6cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-970bd62b-a15d-474f-b884-66c6fd0c5372' class='xr-section-summary-in' type='checkbox'  ><label for='section-970bd62b-a15d-474f-b884-66c6fd0c5372' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6d6fc225-810e-4943-b93f-ab8ec5692611' class='xr-index-data-in' type='checkbox'/><label for='index-6d6fc225-810e-4943-b93f-ab8ec5692611' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8d042a6-65ed-4f17-85f7-21c05f52edd3' class='xr-index-data-in' type='checkbox'/><label for='index-b8d042a6-65ed-4f17-85f7-21c05f52edd3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d287326d-de80-4abb-bc92-06c13c94187b' class='xr-index-data-in' type='checkbox'/><label for='index-d287326d-de80-4abb-bc92-06c13c94187b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2015-02-01&#x27;, &#x27;2015-03-01&#x27;, &#x27;2015-04-01&#x27;, &#x27;2015-05-01&#x27;,\n               &#x27;2015-06-01&#x27;, &#x27;2015-07-01&#x27;, &#x27;2015-08-01&#x27;, &#x27;2015-09-01&#x27;,\n               &#x27;2015-10-01&#x27;, &#x27;2015-11-01&#x27;, &#x27;2015-12-01&#x27;, &#x27;2016-01-01&#x27;,\n               &#x27;2016-02-01&#x27;, &#x27;2016-03-01&#x27;, &#x27;2016-04-01&#x27;, &#x27;2016-05-01&#x27;,\n               &#x27;2016-06-01&#x27;, &#x27;2016-07-01&#x27;, &#x27;2016-08-01&#x27;, &#x27;2016-09-01&#x27;,\n               &#x27;2016-10-01&#x27;, &#x27;2016-11-01&#x27;, &#x27;2016-12-01&#x27;, &#x27;2017-01-01&#x27;,\n               &#x27;2017-02-01&#x27;, &#x27;2017-03-01&#x27;, &#x27;2017-04-01&#x27;, &#x27;2017-05-01&#x27;,\n               &#x27;2017-06-01&#x27;, &#x27;2017-07-01&#x27;, &#x27;2017-08-01&#x27;, &#x27;2017-09-01&#x27;,\n               &#x27;2017-10-01&#x27;, &#x27;2017-11-01&#x27;, &#x27;2017-12-01&#x27;, &#x27;2018-01-01&#x27;,\n               &#x27;2018-02-01&#x27;, &#x27;2018-03-01&#x27;, &#x27;2018-04-01&#x27;, &#x27;2018-05-01&#x27;,\n               &#x27;2018-06-01&#x27;, &#x27;2018-07-01&#x27;, &#x27;2018-08-01&#x27;, &#x27;2018-09-01&#x27;,\n               &#x27;2018-10-01&#x27;, &#x27;2018-11-01&#x27;, &#x27;2018-12-01&#x27;, &#x27;2019-01-01&#x27;,\n               &#x27;2019-02-01&#x27;, &#x27;2019-03-01&#x27;, &#x27;2019-04-01&#x27;, &#x27;2019-05-01&#x27;,\n               &#x27;2019-06-01&#x27;, &#x27;2019-07-01&#x27;, &#x27;2019-08-01&#x27;, &#x27;2019-09-01&#x27;,\n               &#x27;2019-10-01&#x27;, &#x27;2019-11-01&#x27;, &#x27;2019-12-01&#x27;, &#x27;2020-01-01&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-18ddb750-c1e0-4636-a618-7de2d8972256' class='xr-section-summary-in' type='checkbox'  ><label for='section-18ddb750-c1e0-4636-a618-7de2d8972256' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 020510</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
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!important;\n  stroke-width: 0;\n  stroke: currentColor;\n  fill: currentColor;\n}\n</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 733MB\nDimensions:        (time: 60, ncells: 81920, vertices: 3, height: 1, height_2: 1)\nCoordinates:\n    clat           (ncells) float64 655kB 89.46 88.64 89.06 ... -27.32 -27.0\n    clon           (ncells) float64 655kB 72.0 50.25 72.0 ... 1.296 0.6204 0.357\n  * height         (height) float64 8B 10.0\n  * height_2       (height_2) float64 8B 2.0\n  * time           (time) datetime64[ns] 480B 2015-02-01 ... 2020-01-01\nDimensions without coordinates: ncells, vertices\nData variables: (12/40)\n    albnirdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albnirdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdif      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    albvisdir      (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    clat_bnds      (ncells, vertices) float64 2MB dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;\n    clct           (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    ...             ...\n    tqc_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqi_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv            (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    tqv_dia        (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    umfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\n    vmfl_s         (time, ncells) float32 20MB dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;\nAttributes:\n    CDI:                  Climate Data Interface version 2.4.0 (https://mpime...\n    Conventions:          CF-1.6\n    comment:              Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-47...\n    grid_file_uri:        http://icon-downloads.mpimet.mpg.de/grids/public/ic...\n    history:              /work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717...\n    institution:          Max Planck Institute for Meteorology/Deutscher Wett...\n    number_of_grid_used:  30\n    references:           see MPIM/DWD publications\n    source:               git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c...\n    title:                ICON simulation\n    uuidOfHGrid:          a668eac0-fe2e-11e4-9a47-e3e5371a87a7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-148ac13a-e405-44ad-9fb0-e7faa4e8aec2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-148ac13a-e405-44ad-9fb0-e7faa4e8aec2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span>ncells</span>: 81920</li><li><span>vertices</span>: 3</li><li><span class='xr-has-index'>height</span>: 1</li><li><span class='xr-has-index'>height_2</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-44ac1afa-44ab-4f32-a803-c3a7be209327' class='xr-section-summary-in' type='checkbox'  checked><label for='section-44ac1afa-44ab-4f32-a803-c3a7be209327' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>clat</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.46 88.64 89.06 ... -27.32 -27.0</div><input id='attrs-3898ee4e-4d4f-40d7-a6f0-5f96abc6a383' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3898ee4e-4d4f-40d7-a6f0-5f96abc6a383' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa09c3bf-d4ca-4da3-8b5e-990d6be316f7' class='xr-var-data-in' type='checkbox'><label for='data-aa09c3bf-d4ca-4da3-8b5e-990d6be316f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clat_bnds</dd><dt><span>long_name :</span></dt><dd>center latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><pre>array([ 89.45889176,  88.63772156,  89.06223506, ..., -27.28616813,\n       -27.32237258, -27.0023716 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>72.0 50.25 72.0 ... 0.6204 0.357</div><input id='attrs-a02cff1f-c731-4f12-8228-ffdbb9f2152b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a02cff1f-c731-4f12-8228-ffdbb9f2152b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca8a682e-bfb4-43e7-a1ff-67aaa7862d45' class='xr-var-data-in' type='checkbox'><label for='data-ca8a682e-bfb4-43e7-a1ff-67aaa7862d45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>clon_bnds</dd><dt><span>long_name :</span></dt><dd>center longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>radian</dd></dl></div><div class='xr-var-data'><pre>array([72.        , 50.25313351, 72.        , ...,  1.29630984,\n        0.62040403,  0.3569665 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height</span></div><div class='xr-var-dims'>(height)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>10.0</div><input id='attrs-e19f747f-bae6-4d43-80a4-91916fd92738' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e19f747f-bae6-4d43-80a4-91916fd92738' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8abbfed6-ed9e-47c6-8968-e4df9013d43d' class='xr-var-data-in' type='checkbox'><label for='data-8abbfed6-ed9e-47c6-8968-e4df9013d43d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>height_2</span></div><div class='xr-var-dims'>(height_2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-f6bbb8f2-c632-42cc-af68-cfa5226a1359' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6bbb8f2-c632-42cc-af68-cfa5226a1359' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77fb139d-7a8a-46d6-9da4-0bcc15c3e285' class='xr-var-data-in' type='checkbox'><label for='data-77fb139d-7a8a-46d6-9da4-0bcc15c3e285' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-02-01 ... 2020-01-01</div><input id='attrs-884dce00-ebbf-4cc9-b5bc-9ddbc1d33502' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-884dce00-ebbf-4cc9-b5bc-9ddbc1d33502' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4d4463f-b93a-487a-b1b0-5cc5f10e515b' class='xr-var-data-in' type='checkbox'><label for='data-f4d4463f-b93a-487a-b1b0-5cc5f10e515b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-02-01T00:00:00.000000000&#x27;, &#x27;2015-03-01T00:00:00.000000000&#x27;,\n       &#x27;2015-04-01T00:00:00.000000000&#x27;, &#x27;2015-05-01T00:00:00.000000000&#x27;,\n       &#x27;2015-06-01T00:00:00.000000000&#x27;, &#x27;2015-07-01T00:00:00.000000000&#x27;,\n       &#x27;2015-08-01T00:00:00.000000000&#x27;, &#x27;2015-09-01T00:00:00.000000000&#x27;,\n       &#x27;2015-10-01T00:00:00.000000000&#x27;, &#x27;2015-11-01T00:00:00.000000000&#x27;,\n       &#x27;2015-12-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n       &#x27;2016-02-01T00:00:00.000000000&#x27;, &#x27;2016-03-01T00:00:00.000000000&#x27;,\n       &#x27;2016-04-01T00:00:00.000000000&#x27;, &#x27;2016-05-01T00:00:00.000000000&#x27;,\n       &#x27;2016-06-01T00:00:00.000000000&#x27;, &#x27;2016-07-01T00:00:00.000000000&#x27;,\n       &#x27;2016-08-01T00:00:00.000000000&#x27;, &#x27;2016-09-01T00:00:00.000000000&#x27;,\n       &#x27;2016-10-01T00:00:00.000000000&#x27;, &#x27;2016-11-01T00:00:00.000000000&#x27;,\n       &#x27;2016-12-01T00:00:00.000000000&#x27;, &#x27;2017-01-01T00:00:00.000000000&#x27;,\n       &#x27;2017-02-01T00:00:00.000000000&#x27;, &#x27;2017-03-01T00:00:00.000000000&#x27;,\n       &#x27;2017-04-01T00:00:00.000000000&#x27;, &#x27;2017-05-01T00:00:00.000000000&#x27;,\n       &#x27;2017-06-01T00:00:00.000000000&#x27;, &#x27;2017-07-01T00:00:00.000000000&#x27;,\n       &#x27;2017-08-01T00:00:00.000000000&#x27;, &#x27;2017-09-01T00:00:00.000000000&#x27;,\n       &#x27;2017-10-01T00:00:00.000000000&#x27;, &#x27;2017-11-01T00:00:00.000000000&#x27;,\n       &#x27;2017-12-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n       &#x27;2018-02-01T00:00:00.000000000&#x27;, &#x27;2018-03-01T00:00:00.000000000&#x27;,\n       &#x27;2018-04-01T00:00:00.000000000&#x27;, &#x27;2018-05-01T00:00:00.000000000&#x27;,\n       &#x27;2018-06-01T00:00:00.000000000&#x27;, &#x27;2018-07-01T00:00:00.000000000&#x27;,\n       &#x27;2018-08-01T00:00:00.000000000&#x27;, &#x27;2018-09-01T00:00:00.000000000&#x27;,\n       &#x27;2018-10-01T00:00:00.000000000&#x27;, &#x27;2018-11-01T00:00:00.000000000&#x27;,\n       &#x27;2018-12-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:00:00.000000000&#x27;,\n       &#x27;2019-02-01T00:00:00.000000000&#x27;, &#x27;2019-03-01T00:00:00.000000000&#x27;,\n       &#x27;2019-04-01T00:00:00.000000000&#x27;, &#x27;2019-05-01T00:00:00.000000000&#x27;,\n       &#x27;2019-06-01T00:00:00.000000000&#x27;, &#x27;2019-07-01T00:00:00.000000000&#x27;,\n       &#x27;2019-08-01T00:00:00.000000000&#x27;, &#x27;2019-09-01T00:00:00.000000000&#x27;,\n       &#x27;2019-10-01T00:00:00.000000000&#x27;, &#x27;2019-11-01T00:00:00.000000000&#x27;,\n       &#x27;2019-12-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9f465480-5599-436e-afc0-12b75f6f8b37' class='xr-section-summary-in' type='checkbox'  ><label for='section-9f465480-5599-436e-afc0-12b75f6f8b37' class='xr-section-summary' >Data variables: <span>(40)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>albnirdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-dec1b92a-b82b-40ac-9638-4d7ad8270c0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dec1b92a-b82b-40ac-9638-4d7ad8270c0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ca4a3b7-f6e3-442f-b485-3e2ea1dce47c' class='xr-var-data-in' type='checkbox'><label for='data-2ca4a3b7-f6e3-442f-b485-3e2ea1dce47c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>223.19.0</dd><dt><span>standard_name :</span></dt><dd>albnirdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albnirdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-efbfcd81-004b-4e9d-ad15-3a10c0ef5946' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efbfcd81-004b-4e9d-ad15-3a10c0ef5946' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e885caa-9de4-4946-a4e7-5407ef841db8' class='xr-var-data-in' type='checkbox'><label for='data-8e885caa-9de4-4946-a4e7-5407ef841db8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Near IR albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.128.192</dd><dt><span>standard_name :</span></dt><dd>albnirdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdif</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-6ba44c64-aaef-4516-ae9f-a9fadf07930c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ba44c64-aaef-4516-ae9f-a9fadf07930c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-259a27c1-20eb-4140-a3dc-8e60621d6638' class='xr-var-data-in' type='checkbox'><label for='data-259a27c1-20eb-4140-a3dc-8e60621d6638' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for diffuse radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>222.19.0</dd><dt><span>standard_name :</span></dt><dd>albvisdif</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>albvisdir</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c3beb314-ae9d-499e-ac4f-cbb68d1d727a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3beb314-ae9d-499e-ac4f-cbb68d1d727a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41307b9c-1c53-4667-be0d-f4b93e8c9b6c' class='xr-var-data-in' type='checkbox'><label for='data-41307b9c-1c53-4667-be0d-f4b93e8c9b6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>UV visible albedo for direct radiation</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>15.128.192</dd><dt><span>standard_name :</span></dt><dd>albvisdir</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clat_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-2cf996b1-5c50-47c5-810a-b1df560801a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cf996b1-5c50-47c5-810a-b1df560801a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bfa18ec-afd0-4550-9b8d-fffd9912980e' class='xr-var-data-in' type='checkbox'><label for='data-4bfa18ec-afd0-4550-9b8d-fffd9912980e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clct</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d1fd9f13-ce16-4c43-be9f-02fb74ab023d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1fd9f13-ce16-4c43-be9f-02fb74ab023d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbeb4d55-f116-41ef-9488-866b5c0ce06d' class='xr-var-data-in' type='checkbox'><label for='data-dbeb4d55-f116-41ef-9488-866b5c0ce06d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total cloud cover</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.6.0</dd><dt><span>standard_name :</span></dt><dd>clct</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clon_bnds</span></div><div class='xr-var-dims'>(ncells, vertices)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920, 3), meta=np.ndarray&gt;</div><input id='attrs-9ba7e0c4-2faf-4519-bff9-84e1b8e2c218' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ba7e0c4-2faf-4519-bff9-84e1b8e2c218' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f6ff98b-7d27-4fc6-85c0-5ed39aef1bb7' class='xr-var-data-in' type='checkbox'><label for='data-0f6ff98b-7d27-4fc6-85c0-5ed39aef1bb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 1.88 MiB </td>\n                        <td> 1.88 MiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920, 3) </td>\n                        <td> (81920, 3) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">81920</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condhf_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-bce46a6c-7e2a-4bcf-8f93-45e79898d4bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bce46a6c-7e2a-4bcf-8f93-45e79898d4bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7361f012-f271-49a1-81d4-d4142963559b' class='xr-var-data-in' type='checkbox'><label for='data-7361f012-f271-49a1-81d4-d4142963559b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>conductive heat flux at sea-ice bottom</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>condhf_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_land</span></div><div class='xr-var-dims'>(ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(81920,), meta=np.ndarray&gt;</div><input id='attrs-9df81133-02cb-4d35-9fbb-7f4ac884a4c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9df81133-02cb-4d35-9fbb-7f4ac884a4c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c31f9f2c-d591-4192-8c74-2c9e7cb1797f' class='xr-var-data-in' type='checkbox'><label for='data-c31f9f2c-d591-4192-8c74-2c9e7cb1797f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>Fraction land</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.2</dd><dt><span>standard_name :</span></dt><dd>land_area_fraction</dd><dt><span>units :</span></dt><dd>-</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 320.00 kiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (81920,) </td>\n                        <td> (81920,) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fr_seaice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ad4eea7f-83bb-4cce-915e-ecc54fe92fec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad4eea7f-83bb-4cce-915e-ecc54fe92fec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99ab39f8-7fa7-4719-a13f-c23e4c6ac1fa' class='xr-var-data-in' type='checkbox'><label for='data-99ab39f8-7fa7-4719-a13f-c23e4c6ac1fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>fraction of sea ice</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.2.10</dd><dt><span>standard_name :</span></dt><dd>fr_seaice</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-c68487a0-026f-4914-bde8-e6dadf82543d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c68487a0-026f-4914-bde8-e6dadf82543d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edce297c-e1f8-4984-b7e6-d88a26f7fe12' class='xr-var-data-in' type='checkbox'><label for='data-edce297c-e1f8-4984-b7e6-d88a26f7fe12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice depth</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.10</dd><dt><span>standard_name :</span></dt><dd>h_ice</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ice_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2f8b7f36-54e7-454e-9e1a-6756c45933f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f8b7f36-54e7-454e-9e1a-6756c45933f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd31e071-c955-41ce-aa03-76dd29b9010d' class='xr-var-data-in' type='checkbox'><label for='data-cd31e071-c955-41ce-aa03-76dd29b9010d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale ice rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>68.1.0</dd><dt><span>standard_name :</span></dt><dd>ice_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-06cddf39-5ddf-4c15-9b45-39d9784237b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06cddf39-5ddf-4c15-9b45-39d9784237b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9ea0bea-8945-4eab-8c80-d46f5a1c02f8' class='xr-var-data-in' type='checkbox'><label for='data-e9ea0bea-8945-4eab-8c80-d46f5a1c02f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface latent heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>10.0.0</dd><dt><span>standard_name :</span></dt><dd>lhfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meltpot_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-95d45a68-5214-4235-a705-713e28d302b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95d45a68-5214-4235-a705-713e28d302b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d541c65-c474-400e-8be4-967ab0740582' class='xr-var-data-in' type='checkbox'><label for='data-8d541c65-c474-400e-8be4-967ab0740582' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>melt potential at sea-ice top</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>meltpot_ice</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_msl</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3a592238-ee1c-4d28-8842-8617c0d793ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a592238-ee1c-4d28-8842-8617c0d793ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6f213b3-faa9-42c2-ab82-01764fbf9ea1' class='xr-var-data-in' type='checkbox'><label for='data-a6f213b3-faa9-42c2-ab82-01764fbf9ea1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>mean sea level pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.3.0</dd><dt><span>standard_name :</span></dt><dd>mean sea level pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pres_sfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5820e0f8-4887-476b-bb58-e3dedefad07b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5820e0f8-4887-476b-bb58-e3dedefad07b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd6cc554-0e7f-4464-9ccf-93a8572d1755' class='xr-var-data-in' type='checkbox'><label for='data-cd6cc554-0e7f-4464-9ccf-93a8572d1755' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface pressure</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qhfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-cbf899d6-2327-4177-966a-65e22c897e4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbf899d6-2327-4177-966a-65e22c897e4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76be6f2e-51bc-45da-b5ca-2dc06fcee141' class='xr-var-data-in' type='checkbox'><label for='data-76be6f2e-51bc-45da-b5ca-2dc06fcee141' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface moisture flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>6.0.2</dd><dt><span>standard_name :</span></dt><dd>qhfl_s</dd><dt><span>units :</span></dt><dd>Kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qifl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f2bd9750-1b20-4049-9be1-e4b159bbd20c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2bd9750-1b20-4049-9be1-e4b159bbd20c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a49a26a-a748-4013-92f2-83b030ed2cf3' class='xr-var-data-in' type='checkbox'><label for='data-7a49a26a-a748-4013-92f2-83b030ed2cf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>code :</span></dt><dd>255</dd><dt><span>long_name :</span></dt><dd>surface cloud ice deposition flux due to diffusion</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>qifl_s</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5ee3ea5a-b1fe-4539-98f1-1f7ee2396ef6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ee3ea5a-b1fe-4539-98f1-1f7ee2396ef6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9c6eb72-e992-41c8-86f1-f92fe61d13da' class='xr-var-data-in' type='checkbox'><label for='data-b9c6eb72-e992-41c8-86f1-f92fe61d13da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>surface sensible heat flux</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>11.0.0</dd><dt><span>standard_name :</span></dt><dd>shfl_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_con_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-3f4212f5-18e1-4b21-903b-dfcf6981d6a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f4212f5-18e1-4b21-903b-dfcf6981d6a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43942f6d-d504-4822-8dfa-72143eeefa7e' class='xr-var-data-in' type='checkbox'><label for='data-43942f6d-d504-4822-8dfa-72143eeefa7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>convective snow rate (convection scheme output for next time step)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>55.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_con_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_gsp_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-ed2bc22c-6b9b-4033-8cd1-ad8108fa7b40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed2bc22c-6b9b-4033-8cd1-ad8108fa7b40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e471a5c0-dcc7-4fd6-aa51-e7ffe3014e98' class='xr-var-data-in' type='checkbox'><label for='data-e471a5c0-dcc7-4fd6-aa51-e7ffe3014e98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>gridscale snow rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>56.1.0</dd><dt><span>standard_name :</span></dt><dd>snow_gsp_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2197bf61-8289-4aee-81c0-49ced1fe2092' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2197bf61-8289-4aee-81c0-49ced1fe2092' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d9b6782-0ab8-4625-a5db-a8eee333931f' class='xr-var-data-in' type='checkbox'><label for='data-4d9b6782-0ab8-4625-a5db-a8eee333931f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sob_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b1346c6f-fef8-4187-a0d2-a9d2b6bebe73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1346c6f-fef8-4187-a0d2-a9d2b6bebe73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a24159bb-701a-4eb1-9f2a-5f7895a2f081' class='xr-var-data-in' type='checkbox'><label for='data-a24159bb-701a-4eb1-9f2a-5f7895a2f081' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>9.4.0</dd><dt><span>standard_name :</span></dt><dd>sob_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sod_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-b11cafbd-412e-4b84-a838-6338ef26bc19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b11cafbd-412e-4b84-a838-6338ef26bc19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4119dbc7-6c40-4b9c-9040-8b50af4d0564' class='xr-var-data-in' type='checkbox'><label for='data-4119dbc7-6c40-4b9c-9040-8b50af4d0564' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>downward shortwave flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>7.4.0</dd><dt><span>standard_name :</span></dt><dd>sod_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  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y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-60d0c889-eeac-4b3b-a701-66b29522fa45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60d0c889-eeac-4b3b-a701-66b29522fa45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4899a7a6-b6f7-4ed2-b7f0-b1280eef663b' class='xr-var-data-in' type='checkbox'><label for='data-4899a7a6-b6f7-4ed2-b7f0-b1280eef663b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sou_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2d4bd9ef-bc8e-4f92-a0ac-4fdb8847dafe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d4bd9ef-bc8e-4f92-a0ac-4fdb8847dafe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dde412ed-f951-4f62-a2b4-c12abacc8350' class='xr-var-data-in' type='checkbox'><label for='data-dde412ed-f951-4f62-a2b4-c12abacc8350' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>shortwave upward flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.4.0</dd><dt><span>standard_name :</span></dt><dd>sou_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp_10m</span></div><div class='xr-var-dims'>(time, height, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1, 81920), meta=np.ndarray&gt;</div><input id='attrs-8ab48c58-51e1-4309-92a4-b06abb3956cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ab48c58-51e1-4309-92a4-b06abb3956cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d47ce6f-e051-4624-91cc-78e2f625ec0f' class='xr-var-data-in' type='checkbox'><label for='data-7d47ce6f-e051-4624-91cc-78e2f625ec0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>wind speed in 10m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>1.2.0</dd><dt><span>standard_name :</span></dt><dd>sp_10m</dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"26\" />\n  <line x1=\"11\" y1=\"1\" x2=\"11\" y2=\"26\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"27\" />\n  <line x1=\"12\" y1=\"2\" x2=\"12\" y2=\"28\" />\n  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"29\" />\n  <line x1=\"15\" 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-86a92558-3039-4043-8915-909b4243a01a' class='xr-var-data-in' type='checkbox'><label for='data-86a92558-3039-4043-8915-909b4243a01a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>temperature in 2m</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.0.0</dd><dt><span>standard_name :</span></dt><dd>t_2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 1, 81920) </td>\n                        <td> (1, 1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- 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transform=\"rotate(45,7.474299,52.886915)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ice</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5ad7c084-f4e4-43ad-bb77-5d7037a1b34d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ad7c084-f4e4-43ad-bb77-5d7037a1b34d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb6ee24d-b318-49c3-a440-59e8b54614d9' class='xr-var-data-in' type='checkbox'><label for='data-eb6ee24d-b318-49c3-a440-59e8b54614d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea/lake-ice temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>8.2.10</dd><dt><span>standard_name :</span></dt><dd>t_ice</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" 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id='data-6e2a611b-d306-4a47-aa53-c6dd65685769' class='xr-var-data-in' type='checkbox'><label for='data-6e2a611b-d306-4a47-aa53-c6dd65685769' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>weighted temperature of ground surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.3.2</dd><dt><span>standard_name :</span></dt><dd>t_s</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_seasfc</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d0f59c24-0442-4ec0-acac-03879a2e57b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0f59c24-0442-4ec0-acac-03879a2e57b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-befd1a81-fe35-4d9e-94a4-fd6daa54f77d' class='xr-var-data-in' type='checkbox'><label for='data-befd1a81-fe35-4d9e-94a4-fd6daa54f77d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>sea surface temperature</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>0.3.10</dd><dt><span>standard_name :</span></dt><dd>t_seasfc</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-d50e78e7-b514-47b3-ab8e-626114772d4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d50e78e7-b514-47b3-ab8e-626114772d4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c940b69-48ef-4e02-b583-371810349f3f' class='xr-var-data-in' type='checkbox'><label for='data-0c940b69-48ef-4e02-b583-371810349f3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>longwave net flux at surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_s</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thb_t</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-2963fca0-ba6d-48bb-81ea-653e66512f3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2963fca0-ba6d-48bb-81ea-653e66512f3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a10348e-1e90-4591-9508-75b5c5b4fef0' class='xr-var-data-in' type='checkbox'><label for='data-2a10348e-1e90-4591-9508-75b5c5b4fef0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>level_type :</span></dt><dd>toa</dd><dt><span>long_name :</span></dt><dd>thermal net flux at TOA</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>5.5.0</dd><dt><span>standard_name :</span></dt><dd>thb_t</dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tot_prec_rate</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-f8027d90-c70a-465c-ac13-8e089d41171d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8027d90-c70a-465c-ac13-8e089d41171d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad5b21f9-ff8b-4ae3-aa4f-83acdd273abc' class='xr-var-data-in' type='checkbox'><label for='data-ad5b21f9-ff8b-4ae3-aa4f-83acdd273abc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total precipitation rate</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>52.1.0</dd><dt><span>standard_name :</span></dt><dd>tot_prec_rate</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqc_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-e20e34e3-2e1f-40ee-86d7-4d5a209801ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e20e34e3-2e1f-40ee-86d7-4d5a209801ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7811e97-a25a-462e-89da-92c671e00133' class='xr-var-data-in' type='checkbox'><label for='data-d7811e97-a25a-462e-89da-92c671e00133' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud water (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>215.1.0</dd><dt><span>standard_name :</span></dt><dd>tqc_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqi_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-750304db-05dc-4edf-b784-9827282c9fa8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-750304db-05dc-4edf-b784-9827282c9fa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9d665bb-3727-43c6-b75b-51f67348d88e' class='xr-var-data-in' type='checkbox'><label for='data-b9d665bb-3727-43c6-b75b-51f67348d88e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated cloud ice (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>216.1.0</dd><dt><span>standard_name :</span></dt><dd>tqi_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-5f755f3f-020f-4332-abc5-cf4d55fa3c89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f755f3f-020f-4332-abc5-cf4d55fa3c89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b937e91-2c43-4a30-b371-93881f97993d' class='xr-var-data-in' type='checkbox'><label for='data-5b937e91-2c43-4a30-b371-93881f97993d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>64.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv</dd><dt><span>units :</span></dt><dd>kg m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tqv_dia</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-a41a2127-ae61-4427-894a-83bdc8d5e0ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a41a2127-ae61-4427-894a-83bdc8d5e0ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a61967c2-0d88-411a-be01-d365fec7da7f' class='xr-var-data-in' type='checkbox'><label for='data-a61967c2-0d88-411a-be01-d365fec7da7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>total column integrated water vapour (diagnostic)</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>214.1.0</dd><dt><span>standard_name :</span></dt><dd>tqv_dia</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>umfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-00178838-bb91-42df-be34-85ceee3f6c90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00178838-bb91-42df-be34-85ceee3f6c90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93b181e3-7e3b-48a4-b1d6-655af3ea97ae' class='xr-var-data-in' type='checkbox'><label for='data-93b181e3-7e3b-48a4-b1d6-655af3ea97ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>u-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>17.2.0</dd><dt><span>standard_name :</span></dt><dd>umfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vmfl_s</span></div><div class='xr-var-dims'>(time, ncells)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 81920), meta=np.ndarray&gt;</div><input id='attrs-75756691-b9c7-4db8-9e3e-8e5d10f50e81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75756691-b9c7-4db8-9e3e-8e5d10f50e81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dd16e12-8888-4f68-b0f2-f0083453d9b6' class='xr-var-data-in' type='checkbox'><label for='data-1dd16e12-8888-4f68-b0f2-f0083453d9b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>CDI_grid_type :</span></dt><dd>unstructured</dd><dt><span>long_name :</span></dt><dd>v-momentum flux at the surface</dd><dt><span>number_of_grid_in_reference :</span></dt><dd>1</dd><dt><span>param :</span></dt><dd>18.2.0</dd><dt><span>standard_name :</span></dt><dd>vmfl_s</dd><dt><span>units :</span></dt><dd>N m-2</dd></dl></div><div class='xr-var-data'><table>\n    <tr>\n        <td>\n            <table style=\"border-collapse: collapse;\">\n                <thead>\n                    <tr>\n                        <td> </td>\n                        <th> Array </th>\n                        <th> Chunk </th>\n                    </tr>\n                </thead>\n                <tbody>\n                    \n                    <tr>\n                        <th> Bytes </th>\n                        <td> 18.75 MiB </td>\n                        <td> 320.00 kiB </td>\n                    </tr>\n                    \n                    <tr>\n                        <th> Shape </th>\n                        <td> (60, 81920) </td>\n                        <td> (1, 81920) </td>\n                    </tr>\n                    <tr>\n                        <th> Dask graph </th>\n                        <td colspan=\"2\"> 60 chunks in 2 graph layers </td>\n                    </tr>\n                    <tr>\n                        <th> Data type </th>\n                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n                    </tr>\n                </tbody>\n            </table>\n        </td>\n        <td>\n        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n\n  <!-- Horizontal lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n  <line x1=\"0\" y1=\"1\" x2=\"120\" y2=\"1\" />\n  <line x1=\"0\" y1=\"2\" x2=\"120\" y2=\"2\" />\n  <line x1=\"0\" y1=\"3\" x2=\"120\" y2=\"3\" />\n  <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n  <line x1=\"0\" y1=\"6\" x2=\"120\" y2=\"6\" />\n  <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n  <line x1=\"0\" y1=\"9\" x2=\"120\" y2=\"9\" />\n  <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n  <line x1=\"0\" y1=\"11\" x2=\"120\" y2=\"11\" />\n  <line x1=\"0\" y1=\"13\" x2=\"120\" y2=\"13\" />\n  <line x1=\"0\" y1=\"14\" x2=\"120\" y2=\"14\" />\n  <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n  <line x1=\"0\" y1=\"17\" x2=\"120\" y2=\"17\" />\n  <line x1=\"0\" y1=\"18\" x2=\"120\" y2=\"18\" />\n  <line x1=\"0\" y1=\"19\" x2=\"120\" y2=\"19\" />\n  <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n  <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n  <line x1=\"0\" y1=\"23\" x2=\"120\" y2=\"23\" />\n  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Vertical lines -->\n  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n\n  <!-- Colored Rectangle -->\n  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n\n  <!-- Text -->\n  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >81920</text>\n  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">60</text>\n</svg>\n        </td>\n    </tr>\n</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-078ffa3e-904c-4c3b-8aa3-65a7183c061c' class='xr-section-summary-in' type='checkbox'  ><label for='section-078ffa3e-904c-4c3b-8aa3-65a7183c061c' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>height</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9b5db1e3-4447-458b-a02e-1b9b843206f9' class='xr-index-data-in' type='checkbox'/><label for='index-9b5db1e3-4447-458b-a02e-1b9b843206f9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([10.0], dtype=&#x27;float64&#x27;, name=&#x27;height&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>height_2</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d05fa45a-427a-49c3-baaf-961e463ca23d' class='xr-index-data-in' type='checkbox'/><label for='index-d05fa45a-427a-49c3-baaf-961e463ca23d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.0], dtype=&#x27;float64&#x27;, name=&#x27;height_2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f0ded2ac-ed7e-4d5f-b7fd-b424ec211498' class='xr-index-data-in' type='checkbox'/><label for='index-f0ded2ac-ed7e-4d5f-b7fd-b424ec211498' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2015-02-01&#x27;, &#x27;2015-03-01&#x27;, &#x27;2015-04-01&#x27;, &#x27;2015-05-01&#x27;,\n               &#x27;2015-06-01&#x27;, &#x27;2015-07-01&#x27;, &#x27;2015-08-01&#x27;, &#x27;2015-09-01&#x27;,\n               &#x27;2015-10-01&#x27;, &#x27;2015-11-01&#x27;, &#x27;2015-12-01&#x27;, &#x27;2016-01-01&#x27;,\n               &#x27;2016-02-01&#x27;, &#x27;2016-03-01&#x27;, &#x27;2016-04-01&#x27;, &#x27;2016-05-01&#x27;,\n               &#x27;2016-06-01&#x27;, &#x27;2016-07-01&#x27;, &#x27;2016-08-01&#x27;, &#x27;2016-09-01&#x27;,\n               &#x27;2016-10-01&#x27;, &#x27;2016-11-01&#x27;, &#x27;2016-12-01&#x27;, &#x27;2017-01-01&#x27;,\n               &#x27;2017-02-01&#x27;, &#x27;2017-03-01&#x27;, &#x27;2017-04-01&#x27;, &#x27;2017-05-01&#x27;,\n               &#x27;2017-06-01&#x27;, &#x27;2017-07-01&#x27;, &#x27;2017-08-01&#x27;, &#x27;2017-09-01&#x27;,\n               &#x27;2017-10-01&#x27;, &#x27;2017-11-01&#x27;, &#x27;2017-12-01&#x27;, &#x27;2018-01-01&#x27;,\n               &#x27;2018-02-01&#x27;, &#x27;2018-03-01&#x27;, &#x27;2018-04-01&#x27;, &#x27;2018-05-01&#x27;,\n               &#x27;2018-06-01&#x27;, &#x27;2018-07-01&#x27;, &#x27;2018-08-01&#x27;, &#x27;2018-09-01&#x27;,\n               &#x27;2018-10-01&#x27;, &#x27;2018-11-01&#x27;, &#x27;2018-12-01&#x27;, &#x27;2019-01-01&#x27;,\n               &#x27;2019-02-01&#x27;, &#x27;2019-03-01&#x27;, &#x27;2019-04-01&#x27;, &#x27;2019-05-01&#x27;,\n               &#x27;2019-06-01&#x27;, &#x27;2019-07-01&#x27;, &#x27;2019-08-01&#x27;, &#x27;2019-09-01&#x27;,\n               &#x27;2019-10-01&#x27;, &#x27;2019-11-01&#x27;, &#x27;2019-12-01&#x27;, &#x27;2020-01-01&#x27;],\n              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7e5ec635-d679-4169-99d2-1f4df2b29820' class='xr-section-summary-in' type='checkbox'  ><label for='section-7e5ec635-d679-4169-99d2-1f4df2b29820' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CDI :</span></dt><dd>Climate Data Interface version 2.4.0 (https://mpimet.mpg.de/cdi)</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>comment :</span></dt><dd>Stephan Lorenz (m211032) on l20308 (Linux 4.18.0-477.58.1.el8_8.x86_64 x86_64)</dd><dt><span>grid_file_uri :</span></dt><dd>http://icon-downloads.mpimet.mpg.de/grids/public/icon_grid_0030_R02B05_G.nc</dd><dt><span>history :</span></dt><dd>/work/mh0287/m211032/Icon/Git_lev/icon.XPP.20240717/build.intel-hdext/bin/icon.x.intel-hdext.fba959455 at 20240910 020510</dd><dt><span>institution :</span></dt><dd>Max Planck Institute for Meteorology/Deutscher Wetterdienst</dd><dt><span>number_of_grid_used :</span></dt><dd>30</dd><dt><span>references :</span></dt><dd>see MPIM/DWD publications</dd><dt><span>source :</span></dt><dd>git@gitlab.dkrz.de:icon/icon-mpim.git@fba959455f42c89be2cd340d45cbcdb77b8ddc60</dd><dt><span>title :</span></dt><dd>ICON simulation</dd><dt><span>uuidOfHGrid :</span></dt><dd>a668eac0-fe2e-11e4-9a47-e3e5371a87a7</dd></dl></div></li></ul></div></div>"
           },
           "metadata": {}
         }
@@ -254,10 +249,15 @@
           "execution_count": 8,
           "output_type": "execute_result",
           "data": {
-            "text/plain": "<earthkit.plots.components.maps.Map at 0x10e42e48>"
+            "text/plain": "<earthkit.plots.components.maps.Map at 0x18587de8>"
           },
           "metadata": {}
         },
+        {
+          "name": "stdout",
+          "text": "[pyodide]: Memory usage has grown to 734.0MiB (from 637.3MiB) for this notebook\n",
+          "output_type": "stream"
+        },
         {
           "output_type": "display_data",
           "data": {
@@ -272,7 +272,7 @@
     {
       "id": "e3153d77-e5e3-49b0-94ba-9048a71889e8",
       "cell_type": "markdown",
-      "source": "### Please note: due to an earthkit-plots bug related to np.datetime64 it is currently only possible to plot data ranging from 1678 to 2262.",
+      "source": "Please note: The `earthkit-plots` does currently not support plotting data outside of years 1678 to 2262.",
       "metadata": {}
     },
     {

From ec6a6eefac57ca8323c3454403267714fba79470 Mon Sep 17 00:00:00 2001
From: Sara Faghih-Naini <sara.faghihnaini@ecmwf.int>
Date: Fri, 13 Dec 2024 15:36:52 +0100
Subject: [PATCH 8/8] Minor

---
 04-example-datasets/01-hplp.ipynb     | 2 +-
 04-example-datasets/02-OpenIFS.ipynb  | 2 +-
 04-example-datasets/03-NextGEMS.ipynb | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/04-example-datasets/01-hplp.ipynb b/04-example-datasets/01-hplp.ipynb
index d95ffd7..c513038 100644
--- a/04-example-datasets/01-hplp.ipynb
+++ b/04-example-datasets/01-hplp.ipynb
@@ -36,7 +36,7 @@
     {
       "id": "463bf2c0-ac3f-4786-9687-362393802dce",
       "cell_type": "markdown",
-      "source": "This notebook is a comprehensive guide to accessing GRIB files for the [hplp experiment](https://apps.ecmwf.int/ifs-experiments/rd/hplp/). It includes details about the available parameters, levels, and steps, along with instructions for using the `load_hplp_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/). The GRIB files are not downloaded locally; instead, they are accessed remotely using `gribscan`, `fsspec`, and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `gribscan`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point single-time atmospheric and wave model (long window 4Dvar) outputs from the IFS.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco399), corresponding to a horizontal resolution of approximately 28 km, and with 137 vertical model levels. The 10-day weather forecast is provided in 12-hour intervals, beginning from 2020-07-21 at 00:00:00 UTC.\nThe total volume of the available dataset is about 187 GB.\n\nThe GRIB files are organized into different types of data fields based on the levels at which they are stored. This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/).\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in hPa):\n```python\n[1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000]\n```\n\nThe corresponding IDs and more information are given in the table below:",
+      "source": "This notebook is a comprehensive guide to accessing GRIB files for the [hplp experiment](https://apps.ecmwf.int/ifs-experiments/rd/hplp/). It includes details about the available parameters, levels, and steps, along with instructions for using the `load_hplp_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/hplp/). The GRIB files are not downloaded locally; instead, they are accessed remotely using `gribscan`, `fsspec`, and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `gribscan`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point single-time atmospheric and wave model (long window 4Dvar) outputs from the IFS.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco399), corresponding to a horizontal resolution of approximately 28 km, and with 137 vertical model levels. The 10-day weather forecast is provided in 12-hour intervals, beginning from 2020-07-21 at 00:00:00 UTC.\nThe total volume of the available dataset is about 187 GB.\n\nThe GRIB files are organized into different types of data fields based on the levels at which they are stored. These data are published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/).\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in hPa):\n```python\n[1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000]\n```\n\nThe corresponding IDs and more information are given in the table below:",
       "metadata": {}
     },
     {
diff --git a/04-example-datasets/02-OpenIFS.ipynb b/04-example-datasets/02-OpenIFS.ipynb
index 95500ff..21cb6af 100644
--- a/04-example-datasets/02-OpenIFS.ipynb
+++ b/04-example-datasets/02-OpenIFS.ipynb
@@ -36,7 +36,7 @@
     {
       "id": "463bf2c0-ac3f-4786-9687-362393802dce",
       "cell_type": "markdown",
-      "source": "This notebook is a comprehensive guide to accessing NetCDF files for an OpenIFS simulation. It includes details about the available parameters, levels, and steps, along with instructions for using the `load_OpenIFS_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point output from the OpenIFS 43r3 model.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco1279), corresponding to a horizontal resolution of approximately 9 km.\nThe 5-day forecast was run with a time step length of 600 seconds using a HighResMIP based output configuration. \nThe total volume of the available dataset is about 258 GB.\n\n\nThere is a single NetCDF file for the surface level fields and separate files for each pressure level field. This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/).\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in Pa):\n```python\n\n[100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 25000, 20000, 15000, 10000, 7000, 5000, 3000, 2000, 1000, 500, 100]\n\n```\nThe available time steps start at 2009-10-15 18:00:00 UTC, increasing in 6-hour steps to 2009-10-20 12:00:00 UTC.\n\n### Surface Level Parameters\n\nThe following parameters are available as surface level fields:\n```python\n[\"10u\", \"10v\", \"2d\", \"2t\", \"cp\", \"e\", \"es\", \"ewss\", \"msl\", \"nsss\", \"ro\", \"rsn\", \"sd\", \"sf\", \"skt\", \"slhf\", \"smlt\", \"sp\", \"sro\", \"sshf\", \"ssr\", \"ssrc\", \"ssrd\", \"stl1\", \"stl2\", \"stl3\", \"stl4\", \"str\", \"strc\", \"strd\", \"swvl1\", \"swvl2\", \"swvl3\", \"swvl4\", \"tcc\", \"tciw\", \"tclw\", \"tcwv\", \"tisr\", \"tp\", \"tsn\", \"tsr\", \"tsrc\", \"ttr\", \"ttrc\"]\n```\n\nHere, the available time steps start at 2009-10-15 15:00:00 UTC, increasing in 3-hour steps to 2009-10-20 12:00:00 UTC.",
+      "source": "This notebook is a comprehensive guide to accessing NetCDF files for an OpenIFS simulation. It includes details about the available parameters, levels, and steps, along with instructions for using the `load_OpenIFS_data` function to access these files from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/OpenIFS/). The NetCDF files are not downloaded locally; instead, they are accessed remotely using `kerchunk` and `zarr` via their corresponding `.ref` metadata files (also hosted in the S3 bucket), which were produced up-front using `kerchunk`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset contains uncompressed IEEE-754 Standard (1985) 64-bit floating-point output from the OpenIFS 43r3 model.\nThe simulation was conducted on a reduced Gaussian octahedral grid (Tco1279), corresponding to a horizontal resolution of approximately 9 km.\nThe 5-day forecast was run with a time step length of 600 seconds using a HighResMIP based output configuration. \nThe total volume of the available dataset is about 258 GB.\n\n\nThere is a single NetCDF file for the surface level fields and separate files for each pressure level field. These data are published under a Creative Commons Attribution 4.0 International (CC BY 4.0). [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/).\n\n### Pressure Level Parameters\n\nThe following parameters are available as pressure level fields:\n```python\n[\"z\", \"t\", \"q\", \"w\", \"vo\", \"d\", \"u\", \"v\", \"r\"]\n```\n\nThese parameters are stored at the following pressure levels (in Pa):\n```python\n\n[100000, 92500, 85000, 70000, 60000, 50000, 40000, 30000, 25000, 20000, 15000, 10000, 7000, 5000, 3000, 2000, 1000, 500, 100]\n\n```\nThe available time steps start at 2009-10-15 18:00:00 UTC, increasing in 6-hour steps to 2009-10-20 12:00:00 UTC.\n\n### Surface Level Parameters\n\nThe following parameters are available as surface level fields:\n```python\n[\"10u\", \"10v\", \"2d\", \"2t\", \"cp\", \"e\", \"es\", \"ewss\", \"msl\", \"nsss\", \"ro\", \"rsn\", \"sd\", \"sf\", \"skt\", \"slhf\", \"smlt\", \"sp\", \"sro\", \"sshf\", \"ssr\", \"ssrc\", \"ssrd\", \"stl1\", \"stl2\", \"stl3\", \"stl4\", \"str\", \"strc\", \"strd\", \"swvl1\", \"swvl2\", \"swvl3\", \"swvl4\", \"tcc\", \"tciw\", \"tclw\", \"tcwv\", \"tisr\", \"tp\", \"tsn\", \"tsr\", \"tsrc\", \"ttr\", \"ttrc\"]\n```\n\nHere, the available time steps start at 2009-10-15 15:00:00 UTC, increasing in 3-hour steps to 2009-10-20 12:00:00 UTC.",
       "metadata": {}
     },
     {
diff --git a/04-example-datasets/03-NextGEMS.ipynb b/04-example-datasets/03-NextGEMS.ipynb
index 35937c0..73a328a 100644
--- a/04-example-datasets/03-NextGEMS.ipynb
+++ b/04-example-datasets/03-NextGEMS.ipynb
@@ -36,7 +36,7 @@
     {
       "id": "463bf2c0-ac3f-4786-9687-362393802dce",
       "cell_type": "markdown",
-      "source": "This notebook is a comprehensive guide to accessing a Zarr dataset for an ICON simulation within the 4th model cycle phase of the NextGEMS project. It includes details about the available parameters and steps, along with instructions for using the `load_NextGEMS_data` function to access these data from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/EW3_ICON_ngc4008_90d_Compression_test.zarr/). The Zarr data are not downloaded locally; instead, it is accessed remotely using `zarr`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset comprises output data from an ICON run within the 4th model cycle phase of the NextGEMS project (NGC4008). This simulation spans a total of 30 years and uses an ssp370-like scenario run from 2020.\nThe simulation was performed using a horizontal grid spacing of approximately 100 km (R2B8/8). \nA global, coupled simulation covering a time period of 1000 years, starting in 1300, was run. 48-hourly output data were generated throughout the simulation.\nHowever, only a subset of the data is provided: three months of data for four selected variables at three model levels.\nThe data are stored on a HEALPix grid with lossless compression, and the total size of the available dataset is approximately 9 GB.\n\nThe dataset is contained in a Zarr directory. The data is available for reuse under the Creative Commons Attribution 4.0 International license.\n\nThe following 2D parameters are available:\n```python\n[\"hus\", \"pr\" \"ua\", \"va\"]\n```\nThe following model levels are available:\n```python\n[89, 65, 55]\n```\n\n\nThe available time steps begin at 2020-01-02 00:00:00 UTC, increasing in 2-day steps until 2020-03-31 00:00:00 UTC.\n\n",
+      "source": "This notebook is a comprehensive guide to accessing a Zarr dataset for an ICON simulation within the 4th model cycle phase of the NextGEMS project. It includes details about the available parameters and steps, along with instructions for using the `load_NextGEMS_data` function to access these data from the [ECMWF S3 bucket](https://object-store.os-api.cci1.ecmwf.int/esiwacebucket/EW3_ICON_ngc4008_90d_Compression_test.zarr/). The Zarr data are not downloaded locally; instead, it is accessed remotely using `zarr`.\n\n---\n\n## Experiment Details and Available Data Fields\n\nThe dataset comprises output data from an ICON run within the 4th model cycle phase of the NextGEMS project (NGC4008). This simulation spans a total of 30 years and uses an ssp370-like scenario run from 2020.\nThe simulation was performed using a horizontal grid spacing of approximately 100 km (R2B8/8). \nA global, coupled simulation covering a time period of 1000 years, starting in 1300, was run. 48-hourly output data were generated throughout the simulation.\nHowever, only a subset of the data is provided: three months of data for four selected variables at three model levels.\nThe data are stored on a HEALPix grid with lossless compression, and the total size of the available dataset is approximately 9 GB.\n\nThe dataset is contained in a Zarr directory. The data are available for reuse under the Creative Commons Attribution 4.0 International license.\n\nThe following 2D parameters are available:\n```python\n[\"hus\", \"pr\" \"ua\", \"va\"]\n```\nThe following model levels are available:\n```python\n[89, 65, 55]\n```\n\n\nThe available time steps begin at 2020-01-02 00:00:00 UTC, increasing in 2-day steps until 2020-03-31 00:00:00 UTC.\n\n",
       "metadata": {}
     },
     {